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66,168 | https://paperswithcode.com/paper/role-play-based-question-answering-by-real | null | Role play-based question-answering by real users for building chatbots with consistent personalities | Having consistent personalities is important for chatbots if we want them to be believable. Typically, many question-answer pairs are prepared by hand for achieving consistent responses; however, the creation of such pairs is costly. In this study, our goal is to collect a large number of question-answer pairs for a particular character by using role play-based question-answering in which multiple users play the roles of certain characters and respond to questions by online users. Focusing on two famous characters, we conducted a large-scale experiment to collect question-answer pairs by using real users. We evaluated the effectiveness of role play-based question-answering and found that, by using our proposed method, the collected pairs lead to good-quality chatbots that exhibit consistent personalities. | https://aclanthology.org/W18-5031 | https://aclanthology.org/W18-5031.pdf | WS 2018 7 | [
"Ryuichiro Higashinaka",
"Masahiro Mizukami",
"Hidetoshi Kawabata",
"Emi Yamaguchi",
"Noritake Adachi",
"Junji Tomita"
] | [
"Chatbot",
"Question Answering"
] | 1,530,403,200,000 | [] | 188,980 |
137,204 | https://paperswithcode.com/paper/rne-a-scalable-network-embedding-for-billion | 2003.07158 | RNE: A Scalable Network Embedding for Billion-scale Recommendation | Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods. In this paper, given the user-item interaction graph, we propose RNE, a data-efficient Recommendation-based Network Embedding method, to give personalized and diverse items to users. Specifically, we propose a diversity- and dynamics-aware neighbor sampling method for network embedding. On the one hand, the method is able to preserve the local structure between the users and items while modeling the diversity and dynamic property of the user interest to boost the recommendation quality. On the other hand the sampling method can reduce the complexity of the whole method theoretically to make it possible for billion-scale recommendation. We also implement the designed algorithm in a distributed way to further improves its scalability. Experimentally, we deploy RNE on a recommendation scenario of Taobao, the largest E-commerce platform in China, and train it on a billion-scale user-item graph. As is shown on several online metrics on A/B testing, RNE is able to achieve both high-quality and diverse results compared with CF-based methods. We also conduct the offline experiments on Pinterest dataset comparing with several state-of-the-art recommendation methods and network embedding methods. The results demonstrate that our method is able to produce a good result while runs much faster than the baseline methods. | https://arxiv.org/abs/2003.07158v2 | https://arxiv.org/pdf/2003.07158v2.pdf | null | [
"Jianbin Lin",
"Daixin Wang",
"Lu Guan",
"Yin Zhao",
"Binqiang Zhao",
"Jun Zhou",
"Xiaolong Li",
"Yuan Qi"
] | [
"Network Embedding"
] | 1,583,798,400,000 | [] | 131,068 |
269,679 | https://paperswithcode.com/paper/recurrent-feature-propagation-and-edge-skip | 2201.00317 | Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation | Automatic segmentation of abdominal organs in computed tomography (CT) images can support radiation therapy and image-guided surgery workflows. Developing of such automatic solutions remains challenging mainly owing to complex organ interactions and blurry boundaries in CT images. To address these issues, we focus on effective spatial context modeling and explicit edge segmentation priors. Accordingly, we propose a 3D network with four main components trained end-to-end including shared encoder, edge detector, decoder with edge skip-connections (ESCs) and recurrent feature propagation head (RFP-Head). To capture wide-range spatial dependencies, the RFP-Head propagates and harvests local features through directed acyclic graphs (DAGs) formulated with recurrent connections in an efficient slice-wise manner, with regard to spatial arrangement of image units. To leverage edge information, the edge detector learns edge prior knowledge specifically tuned for semantic segmentation by exploiting intermediate features from the encoder with the edge supervision. The ESCs then aggregate the edge knowledge with multi-level decoder features to learn a hierarchy of discriminative features explicitly modeling complementarity between organs' interiors and edges for segmentation. We conduct extensive experiments on two challenging abdominal CT datasets with eight annotated organs. Experimental results show that the proposed network outperforms several state-of-the-art models, especially for the segmentation of small and complicated structures (gallbladder, esophagus, stomach, pancreas and duodenum). The code will be publicly available. | https://arxiv.org/abs/2201.00317v1 | https://arxiv.org/pdf/2201.00317v1.pdf | null | [
"Zefan Yang",
"Di Lin",
"Yi Wang"
] | [
"Computed Tomography (CT)",
"Semantic Segmentation"
] | 1,641,081,600,000 | [] | 140,992 |
55,434 | https://paperswithcode.com/paper/periocular-recognition-using-cnn-features-off | 1809.06157 | Periocular Recognition Using CNN Features Off-the-Shelf | Periocular refers to the region around the eye, including sclera, eyelids,
lashes, brows and skin. With a surprisingly high discrimination ability, it is
the ocular modality requiring the least constrained acquisition. Here, we apply
existing pre-trained architectures, proposed in the context of the ImageNet
Large Scale Visual Recognition Challenge, to the task of periocular
recognition. These have proven to be very successful for many other computer
vision tasks apart from the detection and classification tasks for which they
were designed. Experiments are done with a database of periocular images
captured with a digital camera. We demonstrate that these off-the-shelf CNN
features can effectively recognize individuals based on periocular images,
despite being trained to classify generic objects. Compared against reference
periocular features, they show an EER reduction of up to ~40%, with the fusion
of CNN and traditional features providing additional improvements. | http://arxiv.org/abs/1809.06157v1 | http://arxiv.org/pdf/1809.06157v1.pdf | null | [
"Kevin Hernandez-Diaz",
"Fernando Alonso-Fernandez",
"Josef Bigun"
] | [
"Object Recognition"
] | 1,537,142,400,000 | [] | 126,610 |
205,973 | https://paperswithcode.com/paper/temporal-action-segmentation-from-timestamp | 2103.06669 | Temporal Action Segmentation from Timestamp Supervision | Temporal action segmentation approaches have been very successful recently. However, annotating videos with frame-wise labels to train such models is very expensive and time consuming. While weakly supervised methods trained using only ordered action lists require less annotation effort, the performance is still worse than fully supervised approaches. In this paper, we propose to use timestamp supervision for the temporal action segmentation task. Timestamps require a comparable annotation effort to weakly supervised approaches, and yet provide a more supervisory signal. To demonstrate the effectiveness of timestamp supervision, we propose an approach to train a segmentation model using only timestamps annotations. Our approach uses the model output and the annotated timestamps to generate frame-wise labels by detecting the action changes. We further introduce a confidence loss that forces the predicted probabilities to monotonically decrease as the distance to the timestamps increases. This ensures that all and not only the most distinctive frames of an action are learned during training. The evaluation on four datasets shows that models trained with timestamps annotations achieve comparable performance to the fully supervised approaches. | https://arxiv.org/abs/2103.06669v3 | https://arxiv.org/pdf/2103.06669v3.pdf | CVPR 2021 1 | [
"Zhe Li",
"Yazan Abu Farha",
"Juergen Gall"
] | [
"Action Segmentation",
"Weakly Supervised Action Localization"
] | 1,615,420,800,000 | [] | 2,715 |
30,815 | https://paperswithcode.com/paper/progressive-neural-networks | 1606.04671 | Progressive Neural Networks | Learning to solve complex sequences of tasks--while both leveraging transfer
and avoiding catastrophic forgetting--remains a key obstacle to achieving
human-level intelligence. The progressive networks approach represents a step
forward in this direction: they are immune to forgetting and can leverage prior
knowledge via lateral connections to previously learned features. We evaluate
this architecture extensively on a wide variety of reinforcement learning tasks
(Atari and 3D maze games), and show that it outperforms common baselines based
on pretraining and finetuning. Using a novel sensitivity measure, we
demonstrate that transfer occurs at both low-level sensory and high-level
control layers of the learned policy. | http://arxiv.org/abs/1606.04671v3 | http://arxiv.org/pdf/1606.04671v3.pdf | null | [
"Andrei A. Rusu",
"Neil C. Rabinowitz",
"Guillaume Desjardins",
"Hubert Soyer",
"James Kirkpatrick",
"Koray Kavukcuoglu",
"Razvan Pascanu",
"Raia Hadsell"
] | [
"Continual Learning",
"reinforcement-learning"
] | 1,465,948,800,000 | [] | 43,046 |
215,344 | https://paperswithcode.com/paper/salad-self-adaptive-lightweight-anomaly | 2104.09968 | SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series | Real-world time series data often present recurrent or repetitive patterns and it is often generated in real time, such as transportation passenger volume, network traffic, system resource consumption, energy usage, and human gait. Detecting anomalous events based on machine learning approaches in such time series data has been an active research topic in many different areas. However, most machine learning approaches require labeled datasets, offline training, and may suffer from high computation complexity, consequently hindering their applicability. Providing a lightweight self-adaptive approach that does not need offline training in advance and meanwhile is able to detect anomalies in real time could be highly beneficial. Such an approach could be immediately applied and deployed on any commodity machine to provide timely anomaly alerts. To facilitate such an approach, this paper introduces SALAD, which is a Self-Adaptive Lightweight Anomaly Detection approach based on a special type of recurrent neural networks called Long Short-Term Memory (LSTM). Instead of using offline training, SALAD converts a target time series into a series of average absolute relative error (AARE) values on the fly and predicts an AARE value for every upcoming data point based on short-term historical AARE values. If the difference between a calculated AARE value and its corresponding forecast AARE value is higher than a self-adaptive detection threshold, the corresponding data point is considered anomalous. Otherwise, the data point is considered normal. Experiments based on two real-world open-source time series datasets demonstrate that SALAD outperforms five other state-of-the-art anomaly detection approaches in terms of detection accuracy. In addition, the results also show that SALAD is lightweight and can be deployed on a commodity machine. | https://arxiv.org/abs/2104.09968v3 | https://arxiv.org/pdf/2104.09968v3.pdf | null | [
"Ming-Chang Lee",
"Jia-Chun Lin",
"Ernst Gunnar Gran"
] | [
"Anomaly Detection",
"Time Series"
] | 1,618,790,400,000 | [] | 51,681 |
160,700 | https://paperswithcode.com/paper/optimization-of-graph-neural-networks-with | 2008.09624 | Optimization of Graph Neural Networks with Natural Gradient Descent | In this work, we propose to employ information-geometric tools to optimize a graph neural network architecture such as the graph convolutional networks. More specifically, we develop optimization algorithms for the graph-based semi-supervised learning by employing the natural gradient information in the optimization process. This allows us to efficiently exploit the geometry of the underlying statistical model or parameter space for optimization and inference. To the best of our knowledge, this is the first work that has utilized the natural gradient for the optimization of graph neural networks that can be extended to other semi-supervised problems. Efficient computations algorithms are developed and extensive numerical studies are conducted to demonstrate the superior performance of our algorithms over existing algorithms such as ADAM and SGD. | https://arxiv.org/abs/2008.09624v1 | https://arxiv.org/pdf/2008.09624v1.pdf | null | [
"Mohammad Rasool Izadi",
"Yihao Fang",
"Robert Stevenson",
"Lizhen Lin"
] | [
"Node Classification"
] | 1,597,968,000,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "SGD",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**SGD with Momentum** is a stochastic optimization method that adds a momentum term to regular stochastic gradient descent:\r\n\r\n$$v\\_{t} = \\gamma{v}\\_{t-1} + \\eta\\nabla\\_{\\theta}J\\left(\\theta\\right)$$\r\n$$\\theta\\_{t} = \\theta\\_{t-1} - v\\_{t} $$\r\n\r\nA typical value for $\\gamma$ is $0.9$. The momentum name comes from an analogy to physics, such as ball accelerating down a slope. In the case of weight updates, we can think of the weights as a particle traveling through parameter space which incurs acceleration from the gradient of the loss.\r\n\r\nImage Source: [Juan Du](https://www.researchgate.net/figure/The-compare-of-the-SGD-algorithms-with-and-without-momentum-Take-Task-1-as-example-The_fig1_333469047)",
"full_name": "SGD with Momentum",
"introduced_year": 1999,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "SGD with Momentum",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
}
] | 120,927 |
293,849 | https://paperswithcode.com/paper/multi-scale-distribution-deep-variational | null | Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation | Generating explanations for recommender systems is essential for improving their transparency, as users often wish to understand the reason for receiving a specified recommendation. Previous methods mainly focus on improving the generation quality, but often produce generic explanations that fail to incorporate user and item specific details. To resolve this problem, we present Multi-Scale Distribution Deep Variational Autoencoders (MVAE).These are deep hierarchical VAEs with a prior network that eliminates noise while retaining meaningful signals in the input, coupled with a recognition network serving as the source of information to guide the learning of the prior network. Further, the Multi-scale distribution Learning Framework (MLF) along with a Target Tracking Kullback-Leibler divergence (TKL) mechanism are proposed to employ multi KL divergences at different scales for more effective learning. Extensive empirical experiments demonstrate that our methods can generate explanations with concrete input-specific contents. | https://aclanthology.org/2022.findings-acl.7 | https://aclanthology.org/2022.findings-acl.7.pdf | Findings (ACL) 2022 5 | [
"ZeFeng Cai",
"LinLin Wang",
"Gerard de Melo",
"Fei Sun",
"Liang He"
] | [
"Explanation Generation",
"Recommendation Systems"
] | 1,651,363,200,000 | [] | 54,032 |
55,569 | https://paperswithcode.com/paper/multiobjective-reinforcement-learning-for | 1809.06750 | Multiobjective Reinforcement Learning for Reconfigurable Adaptive Optimal Control of Manufacturing Processes | In industrial applications of adaptive optimal control often multiple
contrary objectives have to be considered. The weights (relative importance) of
the objectives are often not known during the design of the control and can
change with changing production conditions and requirements. In this work a
novel model-free multiobjective reinforcement learning approach for adaptive
optimal control of manufacturing processes is proposed. The approach enables
sample-efficient learning in sequences of control configurations, given by
particular objective weights. | http://arxiv.org/abs/1809.06750v2 | http://arxiv.org/pdf/1809.06750v2.pdf | null | [
"Johannes Dornheim",
"Norbert Link"
] | [
"reinforcement-learning"
] | 1,537,228,800,000 | [] | 40,144 |
56,450 | https://paperswithcode.com/paper/continuous-learning-of-context-dependent | 1810.01256 | Continual Learning of Context-dependent Processing in Neural Networks | Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts. To lift such limits, we developed a novel approach involving a learning algorithm, called orthogonal weights modification (OWM), with the addition of a context-dependent processing (CDP) module. We demonstrated that with OWM to overcome the problem of catastrophic forgetting, and the CDP module to learn how to reuse a feature representation and a classifier for different contexts, a single network can acquire numerous context-dependent mapping rules in an online and continual manner, with as few as $\sim$10 samples to learn each. This should enable highly compact systems to gradually learn myriad regularities of the real world and eventually behave appropriately within it. | https://arxiv.org/abs/1810.01256v3 | https://arxiv.org/pdf/1810.01256v3.pdf | null | [
"Guanxiong Zeng",
"Yang Chen",
"Bo Cui",
"Shan Yu"
] | [
"Continual Learning"
] | 1,538,179,200,000 | [] | 147,160 |
167,173 | https://paperswithcode.com/paper/exploring-vulnerabilities-of-bert-based-apis | null | EXPLORING VULNERABILITIES OF BERT-BASED APIS | Natural language processing (NLP) tasks, ranging from text classification to text generation, have been revolutionised by pretrained BERT models. This allows corporations to easily build powerful APIs by encapsulating fine-tuned BERT models. These BERT-based APIs are often designed to not only provide reliable service but also protect intellectual properties or privacy-sensitive information of the training data. However, a series of privacy and robustness issues may still exist when a fine-tuned BERT model is deployed as a service. In this work, we first present an effective model extraction attack, where the adversary can steal a BERT-based API (the victim model), without knowing the victim model’s architecture, parameters or the training data distribution. We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data. We also show that the extracted model can lead to highly transferable adversarial attacks against the original model. Extensive experiments on multiple benchmark datasets under various settings validate the potential privacy issues and adversarial vulnerabilities of BERT-based APIs. | https://openreview.net/forum?id=7nfCtKep-v | https://openreview.net/pdf?id=7nfCtKep-v | null | [
"Xuanli He",
"Lingjuan Lyu",
"Lichao Sun",
"Xiaojun Chang",
"Jun Zhao"
] | [
"Inference Attack",
"Model extraction",
"Text Classification",
"Text Classification",
"Text Generation"
] | 1,609,459,200,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L584",
"description": "The **Gaussian Error Linear Unit**, or **GELU**, is an activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their percentile, rather than gates inputs by their sign as in [ReLUs](https://paperswithcode.com/method/relu) ($x\\mathbf{1}_{x>0}$). Consequently the GELU can be thought of as a smoother ReLU.\r\n\r\n$$\\text{GELU}\\left(x\\right) = x{P}\\left(X\\leq{x}\\right) = x\\Phi\\left(x\\right) = x \\cdot \\frac{1}{2}\\left[1 + \\text{erf}(x/\\sqrt{2})\\right],$$\r\nif $X\\sim \\mathcal{N}(0,1)$.\r\n\r\nOne can approximate the GELU with\r\n$0.5x\\left(1+\\tanh\\left[\\sqrt{2/\\pi}\\left(x + 0.044715x^{3}\\right)\\right]\\right)$ or $x\\sigma\\left(1.702x\\right),$\r\nbut PyTorch's exact implementation is sufficiently fast such that these approximations may be unnecessary. (See also the [SiLU](https://paperswithcode.com/method/silu) $x\\sigma(x)$ which was also coined in the paper that introduced the GELU.)\r\n\r\nGELUs are used in [GPT-3](https://paperswithcode.com/method/gpt-3), [BERT](https://paperswithcode.com/method/bert), and most other Transformers.",
"full_name": "Gaussian Error Linear Units",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "GELU",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v4"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "**WordPiece** is a subword segmentation algorithm used in natural language processing. The vocabulary is initialized with individual characters in the language, then the most frequent combinations of symbols in the vocabulary are iteratively added to the vocabulary. The process is:\r\n\r\n1. Initialize the word unit inventory with all the characters in the text.\r\n2. Build a language model on the training data using the inventory from 1.\r\n3. Generate a new word unit by combining two units out of the current word inventory to increment the word unit inventory by one. Choose the new word unit out of all the possible ones that increases the likelihood on the training data the most when added to the model.\r\n4. Goto 2 until a predefined limit of word units is reached or the likelihood increase falls below a certain threshold.\r\n\r\nText: [Source](https://stackoverflow.com/questions/55382596/how-is-wordpiece-tokenization-helpful-to-effectively-deal-with-rare-words-proble/55416944#55416944)\r\n\r\nImage: WordPiece as used in [BERT](https://paperswithcode.com/method/bert)",
"full_name": "WordPiece",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "WordPiece",
"source_title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
"source_url": "http://arxiv.org/abs/1609.08144v2"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "",
"description": "**Weight Decay**, or **$L_{2}$ Regularization**, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the $L\\_{2}$ Norm of the weights:\r\n\r\n$$L\\_{new}\\left(w\\right) = L\\_{original}\\left(w\\right) + \\lambda{w^{T}w}$$\r\n\r\nwhere $\\lambda$ is a value determining the strength of the penalty (encouraging smaller weights). \r\n\r\nWeight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function).\r\n\r\nImage Source: Deep Learning, Goodfellow et al",
"full_name": "Weight Decay",
"introduced_year": 1943,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Weight Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Linear Warmup With Linear Decay** is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.",
"full_name": "Linear Warmup With Linear Decay",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Linear Warmup With Linear Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where elements are randomly dropped out of the [softmax](https://paperswithcode.com/method/softmax) in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$",
"full_name": "Attention Dropout",
"introduced_year": 2018,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Attention Dropout",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/5c0264915ab43485adc576f88971fc3d42b10445/transformer/Modules.py#L7",
"description": "**Scaled dot-product attention** is an attention mechanism where the dot products are scaled down by $\\sqrt{d_k}$. Formally we have a query $Q$, a key $K$ and a value $V$ and calculate the attention as:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$\r\n\r\nIf we assume that $q$ and $k$ are $d_k$-dimensional vectors whose components are independent random variables with mean $0$ and variance $1$, then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} u_iv_i$, has mean $0$ and variance $d_k$. Since we would prefer these values to have variance $1$, we divide by $\\sqrt{d_k}$.",
"full_name": "Scaled Dot-Product Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Mechanisms** are a component used in neural networks to model long-range interaction, for example across a text in NLP. The key idea is to build shortcuts between a context vector and the input, to allow a model to attend to different parts. Below you can find a continuously updating list of attention mechanisms.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Scaled Dot-Product Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/google-research/bert",
"description": "**BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a *next sentence prediction* task that jointly pre-trains text-pair representations. \r\n\r\nThere are two steps in BERT: *pre-training* and *fine-tuning*. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they\r\nare initialized with the same pre-trained parameters.",
"full_name": "BERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "BERT",
"source_title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"source_url": "https://arxiv.org/abs/1810.04805v2"
}
] | 179,402 |
253,840 | https://paperswithcode.com/paper/evaluating-the-performance-of-back | null | Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021 | In this paper, we discuss the details of the various Machine Translation (MT) systems that we have submitted for the English-Marathi LoResMT task. As a part of this task, we have submitted three different Neural Machine Translation (NMT) systems; a Baseline English-Marathi system, a Baseline Marathi-English system, and an English-Marathi system that is based on the back-translation technique. We explore the performance of these NMT systems between English and Marathi languages, which forms a low resource language pair due to unavailability of sufficient parallel data. We also explore the performance of the back-translation technique when the back-translated data is obtained from NMT systems that are trained on a very less amount of data. From our experiments, we observe that the back-translation technique can help improve the MT quality over the baseline for the English-Marathi language pair. | https://aclanthology.org/2021.mtsummit-loresmt.17 | https://aclanthology.org/2021.mtsummit-loresmt.17.pdf | MTSummit 2021 8 | [
"Aditya Jain",
"Shivam Mhaskar",
"Pushpak Bhattacharyya"
] | [
"Machine Translation"
] | 1,627,776,000,000 | [] | 12,844 |
139,002 | https://paperswithcode.com/paper/eolo-embedded-object-segmentation-only-look | 2004.00123 | EOLO: Embedded Object Segmentation only Look Once | In this paper, we introduce an anchor-free and single-shot instance segmentation method, which is conceptually simple with 3 independent branches, fully convolutional and can be used by easily embedding it into mobile and embedded devices. Our method, refer as EOLO, reformulates the instance segmentation problem as predicting semantic segmentation and distinguishing overlapping objects problem, through instance center classification and 4D distance regression on each pixel. Moreover, we propose one effective loss function to deal with sampling a high-quality center of gravity examples and optimization for 4D distance regression, which can significantly improve the mAP performance. Without any bells and whistles, EOLO achieves 27.7$\%$ in mask mAP under IoU50 and reaches 30 FPS on 1080Ti GPU, with a single-model and single-scale training/testing on the challenging COCO2017 dataset. For the first time, we show the different comprehension of instance segmentation in recent methods, in terms of both up-bottom, down-up, and direct-predict paradigms. Then we illustrate our model and present related experiments and results. We hope that the proposed EOLO framework can serve as a fundamental baseline for a single-shot instance segmentation task in Real-time Industrial Scenarios. | https://arxiv.org/abs/2004.00123v1 | https://arxiv.org/pdf/2004.00123v1.pdf | null | [
"Longfei Zeng",
"Mohammed Sabah"
] | [
"Instance Segmentation",
"Semantic Segmentation"
] | 1,585,612,800,000 | [] | 91,461 |
179,981 | https://paperswithcode.com/paper/the-limitations-of-model-based-experimental | 1602.05135 | The limitations of model-based experimental design and parameter estimation in sloppy systems | We explore the relationship among model fidelity, experimental design, and
parameter estimation in sloppy models. We show that the approximate nature of
mathematical models poses challenges for experimental design in sloppy models.
In many models of complex biological processes it is unknown what are the
relevant physics that must be included to explain collective behaviors. As a
consequence, models are often overly complex, with many practically
unidentifiable parameters. Furthermore, which details are relevant/irrelevant
vary among potential experiments. By selecting complementary experiments,
experimental design may inadvertently make details that were ommitted from the
model become relevant. When this occurs, the model will fail to give a good fit
to the data. We use a simple hyper-model of model error to quantify a model's
inadequacy and apply it to two models of complex biological processes (EGFR
signaling and DNA repair) with optimally selected experiments. We find that
although parameters may be accurately estimated, the error in the model renders
it less predictive than it was in the sloppy regime where model error is small.
We introduce the concept of a \emph{sloppy system}--a sequence of models of
increasing complexity that become sloppy in the limit of microscopic accuracy.
We explore the limits of accurate parameter estimation in sloppy systems and
argue that system identification better approached by considering a hierarchy
of models of varying detail rather than focusing parameter estimation in a
single model. | http://arxiv.org/abs/1602.05135v3 | http://arxiv.org/pdf/1602.05135v3.pdf | null | [] | [
"Experimental Design"
] | 1,465,862,400,000 | [] | 9,781 |
97,620 | https://paperswithcode.com/paper/embodied-visual-recognition | 1904.04404 | Embodied Visual Recognition | Passive visual systems typically fail to recognize objects in the amodal
setting where they are heavily occluded. In contrast, humans and other embodied
agents have the ability to move in the environment, and actively control the
viewing angle to better understand object shapes and semantics. In this work,
we introduce the task of Embodied Visual Recognition (EVR): An agent is
instantiated in a 3D environment close to an occluded target object, and is
free to move in the environment to perform object classification, amodal object
localization, and amodal object segmentation. To address this, we develop a new
model called Embodied Mask R-CNN, for agents to learn to move strategically to
improve their visual recognition abilities. We conduct experiments using the
House3D environment. Experimental results show that: 1) agents with embodiment
(movement) achieve better visual recognition performance than passive ones; 2)
in order to improve visual recognition abilities, agents can learn strategical
moving paths that are different from shortest paths. | http://arxiv.org/abs/1904.04404v1 | http://arxiv.org/pdf/1904.04404v1.pdf | null | [
"Jianwei Yang",
"Zhile Ren",
"Mingze Xu",
"Xinlei Chen",
"David Crandall",
"Devi Parikh",
"Dhruv Batra"
] | [
"Object Localization",
"Semantic Segmentation"
] | 1,554,768,000,000 | [
{
"code_snippet_url": null,
"description": "A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.\r\n\r\nRPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.",
"full_name": "Region Proposal Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Region Proposal",
"parent": null
},
"name": "RPN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/facebookresearch/detectron2/blob/bb9f5d8e613358519c9865609ab3fe7b6571f2ba/detectron2/layers/roi_align.py#L51",
"description": "**Region of Interest Align**, or **RoIAlign**, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh quantization of [RoI Pool](https://paperswithcode.com/method/roi-pooling), properly *aligning* the extracted features with the input. To avoid any quantization of the RoI boundaries or bins (using $x/16$ instead of $[x/16]$), RoIAlign uses bilinear interpolation to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and the result is then aggregated (using max or average).",
"full_name": "RoIAlign",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**RoI Feature Extractors** are used to extract regions of interest features for tasks such as object detection. Below you can find a continuously updating list of RoI Feature Extractors.",
"name": "RoI Feature Extractors",
"parent": null
},
"name": "RoIAlign",
"source_title": "Mask R-CNN",
"source_url": "http://arxiv.org/abs/1703.06870v3"
},
{
"code_snippet_url": null,
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/facebookresearch/detectron2/blob/601d7666faaf7eb0ba64c9f9ce5811b13861fe12/detectron2/modeling/roi_heads/mask_head.py#L154",
"description": "**Mask R-CNN** extends [Faster R-CNN](http://paperswithcode.com/method/faster-r-cnn) to solve instance segmentation tasks. It achieves this by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. In principle, Mask R-CNN is an intuitive extension of Faster [R-CNN](https://paperswithcode.com/method/r-cnn), but constructing the mask branch properly is critical for good results. \r\n\r\nMost importantly, Faster R-CNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is evident in how [RoIPool](http://paperswithcode.com/method/roi-pooling), the *de facto* core operation for attending to instances, performs coarse spatial quantization for feature extraction. To fix the misalignment, Mask R-CNN utilises a simple, quantization-free layer, called [RoIAlign](http://paperswithcode.com/method/roi-align), that faithfully preserves exact spatial locations. \r\n\r\nSecondly, Mask R-CNN *decouples* mask and class prediction: it predicts a binary mask for each class independently, without competition among classes, and relies on the network's RoI classification branch to predict the category. In contrast, an [FCN](http://paperswithcode.com/method/fcn) usually perform per-pixel multi-class categorization, which couples segmentation and classification.",
"full_name": "Mask R-CNN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Instance Segmentation** models are models that perform the task of [Instance Segmentation](https://paperswithcode.com/task/instance-segmentation).",
"name": "Instance Segmentation Models",
"parent": null
},
"name": "Mask R-CNN",
"source_title": "Mask R-CNN",
"source_url": "http://arxiv.org/abs/1703.06870v3"
}
] | 87,245 |
111,793 | https://paperswithcode.com/paper/session-based-hotel-recommendations | 1908.00071 | Session-Based Hotel Recommendations: Challenges and Future Directions | In the year 2019, the Recommender Systems Challenge deals with a real-world task from the area of e-tourism for the first time, namely the recommendation of hotels in booking sessions. In this context, this article aims at identifying and investigating what we believe are important domain-specific challenges recommendation systems research in hotel search is facing, from both academic and industry perspectives. We focus on three main challenges, namely dealing with (1) multiple stakeholders and value-awareness in recommendations, (2) sparsity of user data and the extensive cold-start problem, and (3) dynamic input data and computational requirements. To this end, we review the state of the art toward solving these challenges and discuss shortcomings. We detail possible future directions and visions we contemplate for the further evolution of the field. This article should, therefore, serve two purposes: giving the interested reader an overview of current challenges in the field and inspiring new approaches for the ACM Recommender Systems Challenge 2019 and beyond. | https://arxiv.org/abs/1908.00071v1 | https://arxiv.org/pdf/1908.00071v1.pdf | null | [
"Jens Adamczak",
"Gerard-Paul Leyson",
"Peter Knees",
"Yashar Deldjoo",
"Farshad Bakhshandegan Moghaddam",
"Julia Neidhardt",
"Wolfgang Wörndl",
"Philipp Monreal"
] | [
"Recommendation Systems"
] | 1,564,531,200,000 | [] | 25,613 |
24,239 | https://paperswithcode.com/paper/f-score-driven-max-margin-neural-network-for | 1611.04234 | F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media | We focus on named entity recognition (NER) for Chinese social media. With
massive unlabeled text and quite limited labelled corpus, we propose a
semi-supervised learning model based on B-LSTM neural network. To take
advantage of traditional methods in NER such as CRF, we combine transition
probability with deep learning in our model. To bridge the gap between label
accuracy and F-score of NER, we construct a model which can be directly trained
on F-score. When considering the instability of F-score driven method and
meaningful information provided by label accuracy, we propose an integrated
method to train on both F-score and label accuracy. Our integrated model yields
7.44\% improvement over previous state-of-the-art result. | http://arxiv.org/abs/1611.04234v2 | http://arxiv.org/pdf/1611.04234v2.pdf | EACL 2017 4 | [
"Hangfeng He",
"Xu sun"
] | [
"Named Entity Recognition",
"Named Entity Recognition",
"Named Entity Recognition"
] | 1,479,081,600,000 | [
{
"code_snippet_url": null,
"description": "**Conditional Random Fields** or **CRFs** are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Graph choice depends on the application, for example linear chain CRFs are popular in natural language processing, whereas in image-based tasks, the graph would connect to neighboring locations in an image to enforce that they have similar predictions.\r\n\r\nImage Credit: [Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields](https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf)",
"full_name": "Conditional Random Field",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Structured Prediction** methods deal with structured outputs with multiple interdependent outputs. Below you can find a continuously updating list of structured prediction methods.",
"name": "Structured Prediction",
"parent": null
},
"name": "CRF",
"source_title": null,
"source_url": null
}
] | 24,510 |
160,213 | https://paperswithcode.com/paper/asya-mindful-verbal-communication-using-deep | 2008.08965 | asya: Mindful verbal communication using deep learning | asya is a mobile application that consists of deep learning models which analyze spectra of a human voice and do noise detection, speaker diarization, gender detection, tempo estimation, and classification of emotions using only voice. All models are language agnostic and capable of running in real-time. Our speaker diarization models have accuracy over 95% on the test data set. These models can be applied for a variety of areas like customer service improvement, sales effective conversations, psychology and couples therapy. | https://arxiv.org/abs/2008.08965v1 | https://arxiv.org/pdf/2008.08965v1.pdf | null | [
"Evalds Urtans",
"Ariel Tabaks"
] | [
"Speaker Diarization",
"Speaker Diarization"
] | 1,597,881,600,000 | [] | 58,606 |
230,352 | https://paperswithcode.com/paper/languagerefer-spatial-language-model-for-3d | 2107.03438 | LanguageRefer: Spatial-Language Model for 3D Visual Grounding | For robots to understand human instructions and perform meaningful tasks in the near future, it is important to develop learned models that comprehend referential language to identify common objects in real-world 3D scenes. In this paper, we introduce a spatial-language model for a 3D visual grounding problem. Specifically, given a reconstructed 3D scene in the form of point clouds with 3D bounding boxes of potential object candidates, and a language utterance referring to a target object in the scene, our model successfully identifies the target object from a set of potential candidates. Specifically, LanguageRefer uses a transformer-based architecture that combines spatial embedding from bounding boxes with fine-tuned language embeddings from DistilBert to predict the target object. We show that it performs competitively on visio-linguistic datasets proposed by ReferIt3D. Further, we analyze its spatial reasoning task performance decoupled from perception noise, the accuracy of view-dependent utterances, and viewpoint annotations for potential robotics applications. | https://arxiv.org/abs/2107.03438v3 | https://arxiv.org/pdf/2107.03438v3.pdf | null | [
"Junha Roh",
"Karthik Desingh",
"Ali Farhadi",
"Dieter Fox"
] | [
"Language Modelling",
"Visual Grounding"
] | 1,625,616,000,000 | [
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L584",
"description": "The **Gaussian Error Linear Unit**, or **GELU**, is an activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their percentile, rather than gates inputs by their sign as in [ReLUs](https://paperswithcode.com/method/relu) ($x\\mathbf{1}_{x>0}$). Consequently the GELU can be thought of as a smoother ReLU.\r\n\r\n$$\\text{GELU}\\left(x\\right) = x{P}\\left(X\\leq{x}\\right) = x\\Phi\\left(x\\right) = x \\cdot \\frac{1}{2}\\left[1 + \\text{erf}(x/\\sqrt{2})\\right],$$\r\nif $X\\sim \\mathcal{N}(0,1)$.\r\n\r\nOne can approximate the GELU with\r\n$0.5x\\left(1+\\tanh\\left[\\sqrt{2/\\pi}\\left(x + 0.044715x^{3}\\right)\\right]\\right)$ or $x\\sigma\\left(1.702x\\right),$\r\nbut PyTorch's exact implementation is sufficiently fast such that these approximations may be unnecessary. (See also the [SiLU](https://paperswithcode.com/method/silu) $x\\sigma(x)$ which was also coined in the paper that introduced the GELU.)\r\n\r\nGELUs are used in [GPT-3](https://paperswithcode.com/method/gpt-3), [BERT](https://paperswithcode.com/method/bert), and most other Transformers.",
"full_name": "Gaussian Error Linear Units",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "GELU",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v4"
},
{
"code_snippet_url": null,
"description": "**Linear Warmup With Linear Decay** is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.",
"full_name": "Linear Warmup With Linear Decay",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Linear Warmup With Linear Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where elements are randomly dropped out of the [softmax](https://paperswithcode.com/method/softmax) in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$",
"full_name": "Attention Dropout",
"introduced_year": 2018,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Attention Dropout",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/5c0264915ab43485adc576f88971fc3d42b10445/transformer/Modules.py#L7",
"description": "**Scaled dot-product attention** is an attention mechanism where the dot products are scaled down by $\\sqrt{d_k}$. Formally we have a query $Q$, a key $K$ and a value $V$ and calculate the attention as:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$\r\n\r\nIf we assume that $q$ and $k$ are $d_k$-dimensional vectors whose components are independent random variables with mean $0$ and variance $1$, then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} u_iv_i$, has mean $0$ and variance $d_k$. Since we would prefer these values to have variance $1$, we divide by $\\sqrt{d_k}$.",
"full_name": "Scaled Dot-Product Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Mechanisms** are a component used in neural networks to model long-range interaction, for example across a text in NLP. The key idea is to build shortcuts between a context vector and the input, to allow a model to attend to different parts. Below you can find a continuously updating list of attention mechanisms.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Scaled Dot-Product Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "",
"description": "**WordPiece** is a subword segmentation algorithm used in natural language processing. The vocabulary is initialized with individual characters in the language, then the most frequent combinations of symbols in the vocabulary are iteratively added to the vocabulary. The process is:\r\n\r\n1. Initialize the word unit inventory with all the characters in the text.\r\n2. Build a language model on the training data using the inventory from 1.\r\n3. Generate a new word unit by combining two units out of the current word inventory to increment the word unit inventory by one. Choose the new word unit out of all the possible ones that increases the likelihood on the training data the most when added to the model.\r\n4. Goto 2 until a predefined limit of word units is reached or the likelihood increase falls below a certain threshold.\r\n\r\nText: [Source](https://stackoverflow.com/questions/55382596/how-is-wordpiece-tokenization-helpful-to-effectively-deal-with-rare-words-proble/55416944#55416944)\r\n\r\nImage: WordPiece as used in [BERT](https://paperswithcode.com/method/bert)",
"full_name": "WordPiece",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "WordPiece",
"source_title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
"source_url": "http://arxiv.org/abs/1609.08144v2"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "**Weight Decay**, or **$L_{2}$ Regularization**, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the $L\\_{2}$ Norm of the weights:\r\n\r\n$$L\\_{new}\\left(w\\right) = L\\_{original}\\left(w\\right) + \\lambda{w^{T}w}$$\r\n\r\nwhere $\\lambda$ is a value determining the strength of the penalty (encouraging smaller weights). \r\n\r\nWeight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function).\r\n\r\nImage Source: Deep Learning, Goodfellow et al",
"full_name": "Weight Decay",
"introduced_year": 1943,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Weight Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google-research/bert",
"description": "**BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a *next sentence prediction* task that jointly pre-trains text-pair representations. \r\n\r\nThere are two steps in BERT: *pre-training* and *fine-tuning*. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they\r\nare initialized with the same pre-trained parameters.",
"full_name": "BERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "BERT",
"source_title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"source_url": "https://arxiv.org/abs/1810.04805v2"
},
{
"code_snippet_url": null,
"description": "**DistilBERT** is a small, fast, cheap and light [Transformer](https://paperswithcode.com/method/transformer) model based on the [BERT](https://paperswithcode.com/method/bert) architecture. Knowledge distillation is performed during the pre-training phase to reduce the size of a BERT model by 40%. To leverage the inductive biases learned by larger models during pre-training, the authors introduce a triple loss combining language modeling, distillation and cosine-distance losses.",
"full_name": "DistilBERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "DistilBERT",
"source_title": "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter",
"source_url": "https://arxiv.org/abs/1910.01108v4"
}
] | 1,070 |
60,933 | https://paperswithcode.com/paper/image-translation-to-mixed-domain-using-sym | 1811.12362 | Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation | Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network. However, there is still a limit in creating an image of a target domain without a dataset on it. We propose a method that expands the concept of `multi-domain' from data to the loss area and learns the combined characteristics of each domain to dynamically infer translations of images in mixed domains. First, we introduce Sym-parameter and its learning method for variously mixed losses while synchronizing them with input conditions. Then, we propose Sym-parameterized Generative Network (SGN) which is empirically confirmed of learning mixed characteristics of various data and losses, and translating images to any mixed-domain without ground truths, such as 30% Van Gogh and 20% Monet and 40% snowy. | https://arxiv.org/abs/1811.12362v3 | https://arxiv.org/pdf/1811.12362v3.pdf | ICCV 2019 10 | [
"Simyung Chang",
"Seonguk Park",
"John Yang",
"Nojun Kwak"
] | [
"Image-to-Image Translation"
] | 1,543,449,600,000 | [
{
"code_snippet_url": "",
"description": "Mixture model network (MoNet) is a general framework allowing to design convolutional deep architectures on non-Euclidean domains such as graphs and manifolds.\r\n\r\nImage and description from: [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://arxiv.org/pdf/1611.08402.pdf)",
"full_name": "Mixture model network",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "The Graph Methods include neural network architectures for learning on graphs with prior structure information, popularly called as Graph Neural Networks (GNNs).\r\n\r\nRecently, deep learning approaches are being extended to work on graph-structured data, giving rise to a series of graph neural networks addressing different challenges. Graph neural networks are particularly useful in applications where data are generated from non-Euclidean domains and represented as graphs with complex relationships. \r\n\r\nSome tasks where GNNs are widely used include [node classification](https://paperswithcode.com/task/node-classification), [graph classification](https://paperswithcode.com/task/graph-classification), [link prediction](https://paperswithcode.com/task/link-prediction), and much more. \r\n\r\nIn the taxonomy presented by [Wu et al. (2019)](https://paperswithcode.com/paper/a-comprehensive-survey-on-graph-neural), graph neural networks can be divided into four categories: **recurrent graph neural networks**, **convolutional graph neural networks**, **graph autoencoders**, and **spatial-temporal graph neural networks**.\r\n\r\nImage source: [A Comprehensive Survey on Graph NeuralNetworks](https://arxiv.org/pdf/1901.00596.pdf)",
"name": "Graph Models",
"parent": null
},
"name": "MoNet",
"source_title": "Geometric deep learning on graphs and manifolds using mixture model CNNs",
"source_url": "http://arxiv.org/abs/1611.08402v3"
}
] | 96,287 |
139,981 | https://paperswithcode.com/paper/deepcovidexplainer-explainable-covid-19 | 2004.04582 | DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images | Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world. Challenge hospitals are faced with, in the fight against the virus, is the effective screening of incoming patients. One methodology is the assessment of chest radiography(CXR) images, which usually requires expert radiologist's knowledge. In this paper, we propose an explainable deep neural networks(DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call DeepCOVIDExplainer. We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID-19 cases. CXR images are first comprehensively preprocessed, before being augmented and classified with a neural ensemble method, followed by highlighting class-discriminating regions using gradient-guided class activation maps(Grad-CAM++) and layer-wise relevance propagation(LRP). Further, we provide human-interpretable explanations of the predictions. Evaluation results based on hold-out data show that our approach can identify COVID-19 confidently with a positive predictive value(PPV) of 91.6%, 92.45%, and 96.12%; precision, recall, and F1 score of 94.6%, 94.3%, and 94.6%, respectively for normal, pneumonia, and COVID-19 cases, respectively, making it comparable or improved results over recent approaches. We hope that our findings will be a useful contribution to the fight against COVID-19 and, in more general, towards an increasing acceptance and adoption of AI-assisted applications in the clinical practice. | https://arxiv.org/abs/2004.04582v3 | https://arxiv.org/pdf/2004.04582v3.pdf | null | [
"Md. Rezaul Karim",
"Till Döhmen",
"Dietrich Rebholz-Schuhmann",
"Stefan Decker",
"Michael Cochez",
"Oya Beyan"
] | [
"COVID-19 Diagnosis"
] | 1,586,390,400,000 | [] | 42,646 |
229,462 | https://paperswithcode.com/paper/lensid-a-cnn-rnn-based-framework-towards-lens | 2107.00875 | LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos | A critical complication after cataract surgery is the dislocation of the lens implant leading to vision deterioration and eye trauma. In order to reduce the risk of this complication, it is vital to discover the risk factors during the surgery. However, studying the relationship between lens dislocation and its suspicious risk factors using numerous videos is a time-extensive procedure. Hence, the surgeons demand an automatic approach to enable a larger-scale and, accordingly, more reliable study. In this paper, we propose a novel framework as the major step towards lens irregularity detection. In particular, we propose (I) an end-to-end recurrent neural network to recognize the lens-implantation phase and (II) a novel semantic segmentation network to segment the lens and pupil after the implantation phase. The phase recognition results reveal the effectiveness of the proposed surgical phase recognition approach. Moreover, the segmentation results confirm the proposed segmentation network's effectiveness compared to state-of-the-art rival approaches. | https://arxiv.org/abs/2107.00875v1 | https://arxiv.org/pdf/2107.00875v1.pdf | null | [
"Negin Ghamsarian",
"Mario Taschwer",
"Doris Putzgruber-Adamitsch",
"Stephanie Sarny",
"Yosuf El-Shabrawi",
"Klaus Schoeffmann"
] | [
"Semantic Segmentation"
] | 1,625,184,000,000 | [] | 12,155 |
80,800 | https://paperswithcode.com/paper/safe-efficient-and-comfortable-velocity | 1902.00089 | Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving | A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model. Car-following behavior produced by the model were compared with that observed in the empirical NGSIM data, to demonstrate the model's ability to follow a lead vehicle safely, efficiently, and comfortably. Results show that the model demonstrates the capability of safe, efficient, and comfortable velocity control in that it 1) has small percentages (8\%) of dangerous minimum time to collision values (\textless\ 5s) than human drivers in the NGSIM data (35\%); 2) can maintain efficient and safe headways in the range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth acceleration. The results indicate that reinforcement learning methods could contribute to the development of autonomous driving systems. | https://arxiv.org/abs/1902.00089v2 | https://arxiv.org/pdf/1902.00089v2.pdf | null | [
"Meixin Zhu",
"Yinhai Wang",
"Ziyuan Pu",
"Jingyun Hu",
"Xuesong Wang",
"Ruimin Ke"
] | [
"Autonomous Driving",
"reinforcement-learning"
] | 1,548,720,000,000 | [] | 172,521 |
54,622 | https://paperswithcode.com/paper/evaluation-of-neural-networks-for-image | 1809.00216 | Evaluation of Neural Networks for Image Recognition Applications: Designing a 0-1 MILP Model of a CNN to create adversarials | Image Recognition is a central task in computer vision with applications
ranging across search, robotics, self-driving cars and many others. There are
three purposes of this document: 1. We follow up on (Fischetti & Jo, December,
2017) and show how standard convolutional neural network can be optimized to a
more sophisticated capsule architecture. 2. We introduce a MILP model based on
CNN to create adversarials. 3. We compare and evaluate each network for image
recognition tasks. | http://arxiv.org/abs/1809.00216v1 | http://arxiv.org/pdf/1809.00216v1.pdf | null | [
"Lucas Schelkes"
] | [
"Self-Driving Cars"
] | 1,535,760,000,000 | [] | 104,594 |
169,940 | https://paperswithcode.com/paper/a-better-alternative-to-piecewise-linear-time | cs/0605103 | A Better Alternative to Piecewise Linear Time Series Segmentation | Time series are difficult to monitor, summarize and predict. Segmentation organizes time series into few intervals having uniform characteristics (flatness, linearity, modality, monotonicity and so on). For scalability, we require fast linear time algorithms. The popular piecewise linear model can determine where the data goes up or down and at what rate. Unfortunately, when the data does not follow a linear model, the computation of the local slope creates overfitting. We propose an adaptive time series model where the polynomial degree of each interval vary (constant, linear and so on). Given a number of regressors, the cost of each interval is its polynomial degree: constant intervals cost 1 regressor, linear intervals cost 2 regressors, and so on. Our goal is to minimize the Euclidean (l_2) error for a given model complexity. Experimentally, we investigate the model where intervals can be either constant or linear. Over synthetic random walks, historical stock market prices, and electrocardiograms, the adaptive model provides a more accurate segmentation than the piecewise linear model without increasing the cross-validation error or the running time, while providing a richer vocabulary to applications. Implementation issues, such as numerical stability and real-world performance, are discussed. | https://arxiv.org/abs/cs/0605103v8 | https://arxiv.org/pdf/cs/0605103v8.pdf | null | [
"Daniel Lemire"
] | [
"Time Series"
] | 1,148,428,800,000 | [] | 39,121 |
214,289 | https://paperswithcode.com/paper/self-supervised-learning-of-remote-sensing | 2104.07070 | Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding | In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and medicine, where it is hard to obtain huge amounts of labeled data. In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification. We analyze the influence of the number and domain of images used for self-supervised pre-training on the performance on downstream tasks. We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes. Besides, we also show that self-supervised pre-training can be easily extended to multispectral images producing even better results on our downstream tasks. | https://arxiv.org/abs/2104.07070v2 | https://arxiv.org/pdf/2104.07070v2.pdf | null | [
"Vladan Stojnić",
"Vladimir Risojević"
] | [
"Image Classification",
"Remote Sensing Image Classification",
"Representation Learning",
"Self-Supervised Learning"
] | 1,618,358,400,000 | [] | 186,430 |
267,264 | https://paperswithcode.com/paper/learning-multiple-gaits-of-quadruped-robot | 2112.04741 | Learning multiple gaits of quadruped robot using hierarchical reinforcement learning | There is a growing interest in learning a velocity command tracking controller of quadruped robot using reinforcement learning due to its robustness and scalability. However, a single policy, trained end-to-end, usually shows a single gait regardless of the command velocity. This could be a suboptimal solution considering the existence of optimal gait according to the velocity for quadruped animals. In this work, we propose a hierarchical controller for quadruped robot that could generate multiple gaits (i.e. pace, trot, bound) while tracking velocity command. Our controller is composed of two policies, each working as a central pattern generator and local feedback controller, and trained with hierarchical reinforcement learning. Experiment results show 1) the existence of optimal gait for specific velocity range 2) the efficiency of our hierarchical controller compared to a controller composed of a single policy, which usually shows a single gait. Codes are publicly available. | https://arxiv.org/abs/2112.04741v1 | https://arxiv.org/pdf/2112.04741v1.pdf | null | [
"Yunho Kim",
"Bukun Son",
"Dongjun Lee"
] | [
"Hierarchical Reinforcement Learning",
"reinforcement-learning"
] | 1,639,008,000,000 | [] | 10,220 |
272,009 | https://paperswithcode.com/paper/adversarially-robust-classification-by | 2201.04733 | Adversarially Robust Classification by Conditional Generative Model Inversion | Most adversarial attack defense methods rely on obfuscating gradients. These methods are successful in defending against gradient-based attacks; however, they are easily circumvented by attacks which either do not use the gradient or by attacks which approximate and use the corrected gradient. Defenses that do not obfuscate gradients such as adversarial training exist, but these approaches generally make assumptions about the attack such as its magnitude. We propose a classification model that does not obfuscate gradients and is robust by construction without assuming prior knowledge about the attack. Our method casts classification as an optimization problem where we "invert" a conditional generator trained on unperturbed, natural images to find the class that generates the closest sample to the query image. We hypothesize that a potential source of brittleness against adversarial attacks is the high-to-low-dimensional nature of feed-forward classifiers which allows an adversary to find small perturbations in the input space that lead to large changes in the output space. On the other hand, a generative model is typically a low-to-high-dimensional mapping. While the method is related to Defense-GAN, the use of a conditional generative model and inversion in our model instead of the feed-forward classifier is a critical difference. Unlike Defense-GAN, which was shown to generate obfuscated gradients that are easily circumvented, we show that our method does not obfuscate gradients. We demonstrate that our model is extremely robust against black-box attacks and has improved robustness against white-box attacks compared to naturally trained, feed-forward classifiers. | https://arxiv.org/abs/2201.04733v1 | https://arxiv.org/pdf/2201.04733v1.pdf | null | [
"Mitra Alirezaei",
"Tolga Tasdizen"
] | [
"Adversarial Attack",
"Classification",
"Robust classification"
] | 1,641,945,600,000 | [] | 181,154 |
122,233 | https://paperswithcode.com/paper/coupling-global-and-local-context-for | null | Coupling Global and Local Context for Unsupervised Aspect Extraction | Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions. To identify aspects, most previous efforts focus on using sequence tagging models trained on human-annotated data. This work studies unsupervised aspect extraction and explores how words appear in global context (on sentence level) and local context (conveyed by neighboring words). We propose a novel neural model, capable of coupling global and local representation to discover aspect words. Experimental results on two benchmarks, laptop and restaurant reviews, show that our model significantly outperforms the state-of-the-art models from previous studies evaluated with varying metrics. Analysis on model output show our ability to learn meaningful and coherent aspect representations. We further investigate how words distribute in global and local context, and find that aspect and non-aspect words do exhibit different context, interpreting our superiority in unsupervised aspect extraction. | https://aclanthology.org/D19-1465 | https://aclanthology.org/D19-1465.pdf | IJCNLP 2019 11 | [
"Ming Liao",
"Jing Li",
"Haisong Zhang",
"Lingzhi Wang",
"Xixin Wu",
"Kam-Fai Wong"
] | [
"Aspect Extraction"
] | 1,572,566,400,000 | [] | 55,900 |
69,441 | https://paperswithcode.com/paper/a-supervised-approach-for-enriching-the | null | A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet | Frame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, is a popular lexical semantic resource. In addition to providing a set of core semantic frames and their frame elements, FrameNet also provides relations between those frames (hence providing a network of frames i.e. FrameNet). We address here the limited coverage of the network of conceptual relations between frames in FrameNet, which has previously been pointed out by others. We present a supervised model using rich features from three different sources: structural features from the existing FrameNet network, information from the WordNet relations between synsets projected into semantic frames, and corpus-collected lexical associations. We show large improvements over baselines consisting of each of the three groups of features in isolation. We then use this model to select frame pairs as candidate relations, and perform evaluation on a sample with good precision. | https://aclanthology.org/C16-1334 | https://aclanthology.org/C16-1334.pdf | COLING 2016 12 | [
"Shafqat Mumtaz Virk",
"Philippe Muller",
"Juliette Conrath"
] | [
"Coreference Resolution",
"Question Answering"
] | 1,480,550,400,000 | [] | 83,177 |
119,030 | https://paperswithcode.com/paper/rosetta-large-scale-system-for-text-detection | 1910.05085 | Rosetta: Large scale system for text detection and recognition in images | In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system. | https://arxiv.org/abs/1910.05085v1 | https://arxiv.org/pdf/1910.05085v1.pdf | null | [
"Fedor Borisyuk",
"Albert Gordo",
"Viswanath Sivakumar"
] | [
"Optical Character Recognition"
] | 1,570,752,000,000 | [] | 20,501 |
288,012 | https://paperswithcode.com/paper/adapting-bigscience-multilingual-model-to | 2204.04873 | Adapting BigScience Multilingual Model to Unseen Languages | We benchmark different strategies of adding new languages (German and Korean) into the BigScience's pretrained multilingual language model with 1.3 billion parameters that currently supports 13 languages. We investigate the factors that affect the language adaptability of the model and the trade-offs between computational costs and expected performance. | https://arxiv.org/abs/2204.04873v1 | https://arxiv.org/pdf/2204.04873v1.pdf | null | [
"Zheng-Xin Yong",
"Vassilina Nikoulina"
] | [
"Language Modelling"
] | 1,649,635,200,000 | [] | 37,913 |
180,075 | https://paperswithcode.com/paper/folding-of-protein-l-with-implications-for | 1602.08889 | Folding of Protein L with implications for collapse in the denatured state ensemble | A fundamental question in protein folding is whether the coil to globule
collapse transition occurs during the initial stages of folding (burst-phase)
or simultaneously with the protein folding transition. Single molecule
fluorescence resonance energy transfer (FRET) and small angle X-ray scattering
(SAXS) experiments disagree on whether Protein L collapse transition occurs
during the burst-phase of folding. We study Protein L folding using a
coarse-grained model and molecular dynamics simulations. The collapse
transition in Protein L is found to be concomitant with the folding transition.
In the burst-phase of folding, we find that FRET experiments overestimate
radius of gyration, $R_g$, of the protein due to the application of Gaussian
polymer chain end-to-end distribution to extract $R_g$ from the FRET
efficiency. FRET experiments estimate $\approx$ 6\AA \ decrease in $R_g$ when
the actual decrease is $\approx$ 3\AA \ on Guanidinium Chloride denaturant
dilution from 7.5M to 1M, and thereby suggesting pronounced compaction in the
protein dimensions in the burst-phase. The $\approx$ 3\AA \ decrease is close
to the statistical uncertainties of the $R_g$ data measured from SAXS
experiments, which suggest no compaction, leading to a disagreement with the
FRET experiments. The transition state ensemble (TSE) structures in Protein L
folding are globular and extensive in agreement with the $\Psi$-analysis
experiments. The results support the hypothesis that the TSE of single domain
proteins depend on protein topology, and are not stabilised by local
interactions alone. | http://arxiv.org/abs/1602.08889v1 | http://arxiv.org/pdf/1602.08889v1.pdf | null | [] | [
"Protein Folding"
] | 1,456,704,000,000 | [] | 84,367 |
291,398 | https://paperswithcode.com/paper/segmentation-of-kidney-stones-in-endoscopic | 2204.14175 | Segmentation of kidney stones in endoscopic video feeds | Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process as possible. For the segmentation task, we adapted and analyzed three baseline deep learning models -- U-Net, U-Net++, and DenseNet -- to predict annotations on the frames of the endoscopic videos with the highest accuracy above 90\%. To show clinical potential for real-time use, we also confirmed that our best trained model can accurately annotate new videos at 30 frames per second. Our results demonstrate that the proposed method justifies continued development and study of image segmentation to annotate ureteroscopic video feeds. | https://arxiv.org/abs/2204.14175v1 | https://arxiv.org/pdf/2204.14175v1.pdf | null | [
"Zachary A Stoebner",
"Daiwei Lu",
"Seok Hee Hong",
"Nicholas L Kavoussi",
"Ipek Oguz"
] | [
"Image Segmentation",
"Semantic Segmentation"
] | 1,651,190,400,000 | [
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/densenet.py#L93",
"description": "A **Dense Block** is a module used in convolutional neural networks that connects *all layers* (with matching feature-map sizes) directly with each other. It was originally proposed as part of the [DenseNet](https://paperswithcode.com/method/densenet) architecture. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers. In contrast to [ResNets](https://paperswithcode.com/method/resnet), we never combine features through summation before they are passed into a layer; instead, we combine features by concatenating them. Hence, the $\\ell^{th}$ layer has $\\ell$ inputs, consisting of the feature-maps of all preceding convolutional blocks. Its own feature-maps are passed on to all $L-\\ell$ subsequent layers. This introduces $\\frac{L(L+1)}{2}$ connections in an $L$-layer network, instead of just $L$, as in traditional architectures: \"dense connectivity\".",
"full_name": "Dense Block",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Model Blocks** are building blocks used in image models such as convolutional neural networks. Below you can find a continuously updating list of image model blocks.",
"name": "Image Model Blocks",
"parent": null
},
"name": "Dense Block",
"source_title": "Densely Connected Convolutional Networks",
"source_url": "http://arxiv.org/abs/1608.06993v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://www.healthnutra.org/es/maxup/",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": null,
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/6db1569c89094cf23f3bc41f79275c45e9fcb3f3/torchvision/models/densenet.py#L126",
"description": "A **DenseNet** is a type of convolutional neural network that utilises [dense connections](https://paperswithcode.com/method/dense-connections) between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.",
"full_name": "DenseNet",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutional Neural Networks** are used to extract features from images (and videos), employing convolutions as their primary operator. Below you can find a continuously updating list of convolutional neural networks.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "DenseNet",
"source_title": "Densely Connected Convolutional Networks",
"source_url": "http://arxiv.org/abs/1608.06993v5"
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/DimTrigkakis/Python-Net/blob/efb81b2f828da5a81b77a141245efdb0d5bcfbf8/incredibleMathFunctions.py#L12-L13",
"description": "**Rectified Linear Units**, or **ReLUs**, are a type of activation function that are linear in the positive dimension, but zero in the negative dimension. The kink in the function is the source of the non-linearity. Linearity in the positive dimension has the attractive property that it prevents non-saturation of gradients (contrast with [sigmoid activations](https://paperswithcode.com/method/sigmoid-activation)), although for half of the real line its gradient is zero.\r\n\r\n$$ f\\left(x\\right) = \\max\\left(0, x\\right) $$",
"full_name": "Rectified Linear Units",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/milesial/Pytorch-UNet/blob/67bf11b4db4c5f2891bd7e8e7f58bcde8ee2d2db/unet/unet_model.py#L8",
"description": "**U-Net** is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit ([ReLU](https://paperswithcode.com/method/relu)) and a 2x2 [max pooling](https://paperswithcode.com/method/max-pooling) operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 [convolution](https://paperswithcode.com/method/convolution) (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.\r\n\r\n[Original MATLAB Code](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz)",
"full_name": "U-Net",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "U-Net",
"source_title": "U-Net: Convolutional Networks for Biomedical Image Segmentation",
"source_url": "http://arxiv.org/abs/1505.04597v1"
}
] | 112,236 |
316,115 | https://paperswithcode.com/paper/adapting-to-non-centered-languages-for-zero | 2209.04138 | Adapting to Non-Centered Languages for Zero-shot Multilingual Translation | Multilingual neural machine translation can translate unseen language pairs during training, i.e. zero-shot translation. However, the zero-shot translation is always unstable. Although prior works attributed the instability to the domination of central language, e.g. English, we supplement this viewpoint with the strict dependence of non-centered languages. In this work, we propose a simple, lightweight yet effective language-specific modeling method by adapting to non-centered languages and combining the shared information and the language-specific information to counteract the instability of zero-shot translation. Experiments with Transformer on IWSLT17, Europarl, TED talks, and OPUS-100 datasets show that our method not only performs better than strong baselines in centered data conditions but also can easily fit non-centered data conditions. By further investigating the layer attribution, we show that our proposed method can disentangle the coupled representation in the correct direction. | https://arxiv.org/abs/2209.04138v1 | https://arxiv.org/pdf/2209.04138v1.pdf | null | [
"Zhi Qu",
"Taro Watanabe"
] | [
"Machine Translation"
] | 1,662,681,600,000 | [
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/5c0264915ab43485adc576f88971fc3d42b10445/transformer/Modules.py#L7",
"description": "**Scaled dot-product attention** is an attention mechanism where the dot products are scaled down by $\\sqrt{d_k}$. Formally we have a query $Q$, a key $K$ and a value $V$ and calculate the attention as:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$\r\n\r\nIf we assume that $q$ and $k$ are $d_k$-dimensional vectors whose components are independent random variables with mean $0$ and variance $1$, then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} u_iv_i$, has mean $0$ and variance $d_k$. Since we would prefer these values to have variance $1$, we divide by $\\sqrt{d_k}$.",
"full_name": "Scaled Dot-Product Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Mechanisms** are a component used in neural networks to model long-range interaction, for example across a text in NLP. The key idea is to build shortcuts between a context vector and the input, to allow a model to attend to different parts. Below you can find a continuously updating list of attention mechanisms.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Scaled Dot-Product Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": null,
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k-1}$ and $1-\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
}
] | 116,431 |
70,804 | https://paperswithcode.com/paper/optimistic-posterior-sampling-for | null | Optimistic posterior sampling for reinforcement learning: worst-case regret bounds | We present an algorithm based on posterior sampling (aka Thompson sampling) that achieves near-optimal worst-case regret bounds when the underlying Markov Decision Process (MDP) is communicating with a finite, though unknown, diameter. Our main result is a high probability regret upper bound of $\tilde{O}(D\sqrt{SAT})$ for any communicating MDP with $S$ states, $A$ actions and diameter $D$, when $T\ge S^5A$. Here, regret compares the total reward achieved by the algorithm to the total expected reward of an optimal infinite-horizon undiscounted average reward policy, in time horizon $T$. This result improves over the best previously known upper bound of $\tilde{O}(DS\sqrt{AT})$ achieved by any algorithm in this setting, and matches the dependence on $S$ in the established lower bound of $\Omega(\sqrt{DSAT})$ for this problem. Our techniques involve proving some novel results about the anti-concentration of Dirichlet distribution, which may be of independent interest. | http://papers.nips.cc/paper/6718-optimistic-posterior-sampling-for-reinforcement-learning-worst-case-regret-bounds | http://papers.nips.cc/paper/6718-optimistic-posterior-sampling-for-reinforcement-learning-worst-case-regret-bounds.pdf | NeurIPS 2017 12 | [
"Shipra Agrawal",
"Randy Jia"
] | [
"reinforcement-learning"
] | 1,512,086,400,000 | [] | 18,086 |
48,947 | https://paperswithcode.com/paper/constrained-fractional-set-programs-and-their | 1306.3409 | Constrained fractional set programs and their application in local clustering and community detection | The (constrained) minimization of a ratio of set functions is a problem
frequently occurring in clustering and community detection. As these
optimization problems are typically NP-hard, one uses convex or spectral
relaxations in practice. While these relaxations can be solved globally
optimally, they are often too loose and thus lead to results far away from the
optimum. In this paper we show that every constrained minimization problem of a
ratio of non-negative set functions allows a tight relaxation into an
unconstrained continuous optimization problem. This result leads to a flexible
framework for solving constrained problems in network analysis. While a
globally optimal solution for the resulting non-convex problem cannot be
guaranteed, we outperform the loose convex or spectral relaxations by a large
margin on constrained local clustering problems. | http://arxiv.org/abs/1306.3409v1 | http://arxiv.org/pdf/1306.3409v1.pdf | null | [
"Thomas Bühler",
"Syama Sundar Rangapuram",
"Simon Setzer",
"Matthias Hein"
] | [
"Community Detection"
] | 1,371,168,000,000 | [] | 51,237 |
51,489 | https://paperswithcode.com/paper/exploiting-statistical-dependencies-of-time | 1807.04119 | Exploiting statistical dependencies of time series with hierarchical correlation reconstruction | While we are usually focused on forecasting future values of time series, it
is often valuable to additionally predict their entire probability
distributions, e.g. to evaluate risk, Monte Carlo simulations. On example of
time series of $\approx$ 30000 Dow Jones Industrial Averages, there will be
presented application of hierarchical correlation reconstruction for this
purpose: MSE estimating polynomial as joint density for (current value,
context), where context is for example a few previous values. Then substituting
the currently observed context and normalizing density to 1, we get predicted
probability distribution for the current value. In contrast to standard machine
learning approaches like neural networks, optimal polynomial coefficients here
have inexpensive direct formula, have controllable accuracy, are unique and
independently calculated, each has a specific cumulant-like interpretation, and
such approximation can asymptotically approach complete description of any real
joint distribution - providing universal tool to quantitatively describe and
exploit statistical dependencies in time series, systematically enhancing
ARMA/ARCH-like approaches, also based on different distributions than Gaussian
which turns out improper for daily log returns. There is also discussed
application for non-stationary time series like calculating linear time trend,
or adapting coefficients to local statistical behavior. | http://arxiv.org/abs/1807.04119v5 | http://arxiv.org/pdf/1807.04119v5.pdf | null | [
"Jarek Duda"
] | [
"Time Series"
] | 1,531,267,200,000 | [] | 131,050 |
63,471 | https://paperswithcode.com/paper/improved-word-representation-learning-with | null | Improved Word Representation Learning with Sememes | Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people manually annotate word sememes and form linguistic common-sense knowledge bases. In this paper, we present that, word sememe information can improve word representation learning (WRL), which maps words into a low-dimensional semantic space and serves as a fundamental step for many NLP tasks. The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately. More specifically, we follow the framework of Skip-gram and present three sememe-encoded models to learn representations of sememes, senses and words, where we apply the attention scheme to detect word senses in various contexts. We conduct experiments on two tasks including word similarity and word analogy, and our models significantly outperform baselines. The results indicate that WRL can benefit from sememes via the attention scheme, and also confirm our models being capable of correctly modeling sememe information. | https://aclanthology.org/P17-1187 | https://aclanthology.org/P17-1187.pdf | ACL 2017 7 | [
"Yilin Niu",
"Ruobing Xie",
"Zhiyuan Liu",
"Maosong Sun"
] | [
"Common Sense Reasoning",
"Language Modelling",
"Machine Translation",
"Representation Learning",
"Sentiment Analysis",
"Word Embeddings",
"Word Sense Disambiguation",
"Word Sense Induction",
"Word Similarity"
] | 1,498,867,200,000 | [] | 162,876 |
239,815 | https://paperswithcode.com/paper/learning-to-ground-visual-objects-for-visual | 2109.06013 | Learning to Ground Visual Objects for Visual Dialog | Visual dialog is challenging since it needs to answer a series of coherent questions based on understanding the visual environment. How to ground related visual objects is one of the key problems. Previous studies utilize the question and history to attend to the image and achieve satisfactory performance, however these methods are not sufficient to locate related visual objects without any guidance. The inappropriate grounding of visual objects prohibits the performance of visual dialog models. In this paper, we propose a novel approach to Learn to Ground visual objects for visual dialog, which employs a novel visual objects grounding mechanism where both prior and posterior distributions over visual objects are used to facilitate visual objects grounding. Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process. Meanwhile, a prior distribution, which is inferred from context only, is used to approximate the posterior distribution so that appropriate visual objects can be grounded even without answers during the inference process. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that our approach improves the previous strong models in both generative and discriminative settings by a significant margin. | https://arxiv.org/abs/2109.06013v3 | https://arxiv.org/pdf/2109.06013v3.pdf | Findings (EMNLP) 2021 11 | [
"Feilong Chen",
"Xiuyi Chen",
"Can Xu",
"Daxin Jiang"
] | [
"Visual Dialog"
] | 1,631,491,200,000 | [] | 126,638 |
64,041 | https://paperswithcode.com/paper/end-to-end-trainable-attentive-decoder-for | null | End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification | We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure. | https://aclanthology.org/E17-2119 | https://aclanthology.org/E17-2119.pdf | EACL 2017 4 | [
"Sanjeev Karn",
"Ulli Waltinger",
"Hinrich Sch{\\\"u}tze"
] | [
"Classification",
"Classification",
"Morphological Tagging"
] | 1,491,004,800,000 | [] | 130,929 |
252,218 | https://paperswithcode.com/paper/enhancing-top-n-item-recommendations-by-peer | 2111.00429 | Enhancing Top-N Item Recommendations by Peer Collaboration | Deep neural networks (DNN) have achieved great success in the recommender systems (RS) domain. However, to achieve remarkable performance, DNN-based recommender models often require numerous parameters, which inevitably bring redundant neurons and weights, a phenomenon referred to as over-parameterization. In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS. Specifically, we propose PCRec, a top-N item \underline{rec}ommendation framework that leverages collaborative training of two DNN-based recommender models with the same network structure, termed \underline{p}eer \underline{c}ollaboration. PCRec can reactivate and strengthen the unimportant (redundant) weights during training, which achieves higher prediction accuracy but maintains its original inference efficiency. To realize this, we first introduce two criteria to identify the importance of weights of a given recommender model. Then, we rejuvenate the unimportant weights by transplanting outside information (i.e., weights) from its peer network. After such an operation and retraining, the original recommender model is endowed with more representation capacity by possessing more functional model parameters. To show its generality, we instantiate PCRec by using three well-known recommender models. We conduct extensive experiments on three real-world datasets, and show that PCRec yields significantly better recommendations than its counterpart with the same model (parameter) size. | https://arxiv.org/abs/2111.00429v3 | https://arxiv.org/pdf/2111.00429v3.pdf | null | [
"Yang Sun",
"Fajie Yuan",
"Min Yang",
"Alexandros Karatzoglou",
"Shen Li",
"Xiaoyan Zhao"
] | [
"Recommendation Systems"
] | 1,635,638,400,000 | [] | 71,814 |
254,760 | https://paperswithcode.com/paper/can-question-generation-debias-question-1 | null | Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap | Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question–context lexical overlap. This hinders QA models from generalizing to under-represented samples such as questions with low lexical overlap. Question generation (QG), a method for augmenting QA datasets, can be a solution for such performance degradation if QG can properly debias QA datasets. However, we discover that recent neural QG models are biased towards generating questions with high lexical overlap, which can amplify the dataset bias. Moreover, our analysis reveals that data augmentation with these QG models frequently impairs the performance on questions with low lexical overlap, while improving that on questions with high lexical overlap. To address this problem, we use a synonym replacement-based approach to augment questions with low lexical overlap. We demonstrate that the proposed data augmentation approach is simple yet effective to mitigate the degradation problem with only 70k synthetic examples. | https://aclanthology.org/2021.mrqa-1.6 | https://aclanthology.org/2021.mrqa-1.6.pdf | EMNLP (MRQA) 2021 11 | [
"Kazutoshi Shinoda",
"Saku Sugawara",
"Akiko Aizawa"
] | [
"Data Augmentation",
"Question Answering",
"Question Generation",
"Reading Comprehension"
] | 1,635,724,800,000 | [] | 163,319 |
57,559 | https://paperswithcode.com/paper/urban-change-detection-for-multispectral | 1810.08468 | Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks | The Copernicus Sentinel-2 program now provides multispectral images at a
global scale with a high revisit rate. In this paper we explore the usage of
convolutional neural networks for urban change detection using such
multispectral images. We first present the new change detection dataset that
was used for training the proposed networks, which will be openly available to
serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is
composed of pairs of multispectral aerial images, and the changes were manually
annotated at pixel level. We then propose two architectures to detect changes,
Siamese and Early Fusion, and compare the impact of using different numbers of
spectral channels as inputs. These architectures are trained from scratch using
the provided dataset. | http://arxiv.org/abs/1810.08468v1 | http://arxiv.org/pdf/1810.08468v1.pdf | null | [
"Rodrigo Caye Daudt",
"Bertrand Le Saux",
"Alexandre Boulch",
"Yann Gousseau"
] | [
"Change Detection"
] | 1,539,907,200,000 | [] | 183,517 |
234,823 | https://paperswithcode.com/paper/autovideo-an-automated-video-action | 2108.04212 | AutoVideo: An Automated Video Action Recognition System | Action recognition is an important task for video understanding with broad applications. However, developing an effective action recognition solution often requires extensive engineering efforts in building and testing different combinations of the modules and their hyperparameters. In this demo, we present AutoVideo, a Python system for automated video action recognition. AutoVideo is featured for 1) highly modular and extendable infrastructure following the standard pipeline language, 2) an exhaustive list of primitives for pipeline construction, 3) data-driven tuners to save the efforts of pipeline tuning, and 4) easy-to-use Graphical User Interface (GUI). AutoVideo is released under MIT license at https://github.com/datamllab/autovideo | https://arxiv.org/abs/2108.04212v4 | https://arxiv.org/pdf/2108.04212v4.pdf | null | [
"Daochen Zha",
"Zaid Pervaiz Bhat",
"Yi-Wei Chen",
"Yicheng Wang",
"Sirui Ding",
"Jiaben Chen",
"Kwei-Herng Lai",
"Mohammad Qazim Bhat",
"Anmoll Kumar Jain",
"Alfredo Costilla Reyes",
"Na Zou",
"Xia Hu"
] | [
"Action Recognition",
"AutoML",
"Temporal Action Localization",
"Video Understanding"
] | 1,628,467,200,000 | [] | 9,282 |
56,414 | https://paperswithcode.com/paper/marrying-tracking-with-elm-a-metric | 1810.01271 | Marrying Tracking with ELM: A Metric Constraint Guided Multiple Feature Fusion Method | Object Tracking is one important problem in computer vision and surveillance
system. The existing models mainly exploit the single-view feature (i.e. color,
texture, shape) to solve the problem, failing to describe the objects
comprehensively. In this paper, we solve the problem from multi-view
perspective by leveraging multi-view complementary and latent information, so
as to be robust to the partial occlusion and background clutter especially when
the objects are similar to the target, meanwhile addressing tracking drift.
However, one big problem is that multi-view fusion strategy can inevitably
result tracking into non-efficiency. To this end, we propose to marry ELM
(Extreme learning machine) to multi-view fusion to train the global hidden
output weight, to effectively exploit the local information from each view.
Following this principle, we propose a novel method to obtain the optimal
sample as the target object, which avoids tracking drift resulting from noisy
samples. Our method is evaluated over 12 challenge image sequences challenged
with different attributes including illumination, occlusion, deformation, etc.,
which demonstrates better performance than several state-of-the-art methods in
terms of effectiveness and robustness. | http://arxiv.org/abs/1810.01271v2 | http://arxiv.org/pdf/1810.01271v2.pdf | null | [
"Jing Zhang",
"Yong-Gong Ren"
] | [
"Object Tracking"
] | 1,538,265,600,000 | [] | 149,355 |
25,233 | https://paperswithcode.com/paper/syntaxnet-models-for-the-conll-2017-shared | 1703.04929 | SyntaxNet Models for the CoNLL 2017 Shared Task | We describe a baseline dependency parsing system for the CoNLL2017 Shared
Task. This system, which we call "ParseySaurus," uses the DRAGNN framework
[Kong et al, 2017] to combine transition-based recurrent parsing and tagging
with character-based word representations. On the v1.3 Universal Dependencies
Treebanks, the new system outpeforms the publicly available, state-of-the-art
"Parsey's Cousins" models by 3.47% absolute Labeled Accuracy Score (LAS) across
52 treebanks. | http://arxiv.org/abs/1703.04929v1 | http://arxiv.org/pdf/1703.04929v1.pdf | null | [
"Chris Alberti",
"Daniel Andor",
"Ivan Bogatyy",
"Michael Collins",
"Dan Gillick",
"Lingpeng Kong",
"Terry Koo",
"Ji Ma",
"Mark Omernick",
"Slav Petrov",
"Chayut Thanapirom",
"Zora Tung",
"David Weiss"
] | [
"Dependency Parsing"
] | 1,489,536,000,000 | [] | 91,998 |
34,519 | https://paperswithcode.com/paper/parametric-object-motion-from-blur | 1604.05933 | Parametric Object Motion from Blur | Motion blur can adversely affect a number of vision tasks, hence it is
generally considered a nuisance. We instead treat motion blur as a useful
signal that allows to compute the motion of objects from a single image.
Drawing on the success of joint segmentation and parametric motion models in
the context of optical flow estimation, we propose a parametric object motion
model combined with a segmentation mask to exploit localized, non-uniform
motion blur. Our parametric image formation model is differentiable w.r.t. the
motion parameters, which enables us to generalize marginal-likelihood
techniques from uniform blind deblurring to localized, non-uniform blur. A
two-stage pipeline, first in derivative space and then in image space, allows
to estimate both parametric object motion as well as a motion segmentation from
a single image alone. Our experiments demonstrate its ability to cope with very
challenging cases of object motion blur. | http://arxiv.org/abs/1604.05933v1 | http://arxiv.org/pdf/1604.05933v1.pdf | CVPR 2016 6 | [
"Jochen Gast",
"Anita Sellent",
"Stefan Roth"
] | [
"Deblurring",
"Motion Segmentation",
"Optical Flow Estimation"
] | 1,461,110,400,000 | [] | 43,919 |
260,089 | https://paperswithcode.com/paper/scallop-from-probabilistic-deductive | null | Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning | Deep learning and symbolic reasoning are complementary techniques for an intelligent system. However, principled combinations of these techniques have limited scalability, rendering them ill-suited for real-world applications. We propose Scallop, a system that builds upon probabilistic deductive databases, to bridge this gap. The key insight underlying Scallop is a provenance framework that introduces a tunable parameter to specify the level of reasoning granularity. Scallop thereby i) generalizes exact probabilistic reasoning, ii) asymptotically reduces computational cost, and iii) provides relative accuracy guarantees. On a suite of tasks that involve mathematical and logical reasoning, Scallop scales significantly better without sacrificing accuracy compared to DeepProbLog, a principled neural logic programming approach. We also create and evaluate on a real-world Visual Question Answering (VQA) benchmark that requires multi-hop reasoning. Scallop outperforms two VQA-tailored models, a Neural Module Networks based and a transformer based model, by 12.42% and 21.66% respectively. | http://proceedings.neurips.cc/paper/2021/hash/d367eef13f90793bd8121e2f675f0dc2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d367eef13f90793bd8121e2f675f0dc2-Paper.pdf | NeurIPS 2021 12 | [
"Jiani Huang",
"Ziyang Li",
"Binghong Chen",
"Karan Samel",
"Mayur Naik",
"Le Song",
"Xujie Si"
] | [
"Logical Reasoning",
"Question Answering",
"Visual Question Answering",
"Visual Question Answering"
] | 1,638,316,800,000 | [] | 4,111 |
169,947 | https://paperswithcode.com/paper/robust-random-cut-forest-based-anomaly | null | Robust random cut forest based anomaly detection on streams | In this paper we focus on the anomaly detection problem for dynamic data streams through the lens of random cut forests. We investigate a robust random cut data structure that can be used as a sketch or synopsis of the input stream. We provide a plausible definition of non-parametric anomalies based on the influence of an unseen point on the remainder of the data, i.e., the exter-nality imposed by that point. We show how the sketch can be efficiently updated in a dynamic data stream. We demonstrate the viability of the algorithm on publicly available real data. | http://proceedings.mlr.press/v48/guha16.html | http://proceedings.mlr.press/v48/guha16.pdf | null | [
"Sudipto Guha",
"Nina Mishra",
"Gourav Roy",
"Okke Schrijvers"
] | [
"Anomaly Detection"
] | 1,466,294,400,000 | [] | 72,810 |
271,259 | https://paperswithcode.com/paper/coin-counterfactual-image-generation-for-vqa | 2201.03342 | COIN: Counterfactual Image Generation for VQA Interpretation | Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models' behaviour. | https://arxiv.org/abs/2201.03342v1 | https://arxiv.org/pdf/2201.03342v1.pdf | null | [
"Zeyd Boukhers",
"Timo Hartmann",
"Jan Jürjens"
] | [
"Image Generation",
"Question Answering",
"Visual Question Answering",
"Visual Question Answering"
] | 1,641,772,800,000 | [] | 12,010 |
210,462 | https://paperswithcode.com/paper/3d-human-pose-and-shape-regression-with | 2103.16507 | PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop | Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images. By directly mapping raw pixels to model parameters, these methods can produce parametric models in a feed-forward manner via neural networks. However, minor deviation in parameters may lead to noticeable misalignment between the estimated meshes and image evidences. To address this issue, we propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop to leverage a feature pyramid and rectify the predicted parameters explicitly based on the mesh-image alignment status in our deep regressor. In PyMAF, given the currently predicted parameters, mesh-aligned evidences will be extracted from finer-resolution features accordingly and fed back for parameter rectification. To reduce noise and enhance the reliability of these evidences, an auxiliary pixel-wise supervision is imposed on the feature encoder, which provides mesh-image correspondence guidance for our network to preserve the most related information in spatial features. The efficacy of our approach is validated on several benchmarks, including Human3.6M, 3DPW, LSP, and COCO, where experimental results show that our approach consistently improves the mesh-image alignment of the reconstruction. The project page with code and video results can be found at https://hongwenzhang.github.io/pymaf. | https://arxiv.org/abs/2103.16507v4 | https://arxiv.org/pdf/2103.16507v4.pdf | ICCV 2021 10 | [
"Hongwen Zhang",
"Yating Tian",
"Xinchi Zhou",
"Wanli Ouyang",
"Yebin Liu",
"LiMin Wang",
"Zhenan Sun"
] | [
"3D human pose and shape estimation",
"3D Human Pose Estimation",
"3D Human Reconstruction",
"Human Mesh Recovery"
] | 1,617,062,400,000 | [] | 122,487 |
286,648 | https://paperswithcode.com/paper/a-statistical-decision-theoretical | 2204.00036 | A Statistical Decision-Theoretical Perspective on the Two-Stage Approach to Parameter Estimation | One of the most important problems in system identification and statistics is how to estimate the unknown parameters of a given model. Optimization methods and specialized procedures, such as Empirical Minimization (EM) can be used in case the likelihood function can be computed. For situations where one can only simulate from a parametric model, but the likelihood is difficult or impossible to evaluate, a technique known as the Two-Stage (TS) Approach can be applied to obtain reliable parametric estimates. Unfortunately, there is currently a lack of theoretical justification for TS. In this paper, we propose a statistical decision-theoretical derivation of TS, which leads to Bayesian and Minimax estimators. We also show how to apply the TS approach on models for independent and identically distributed samples, by computing quantiles of the data as a first step, and using a linear function as the second stage. The proposed method is illustrated via numerical simulations. | https://arxiv.org/abs/2204.00036v2 | https://arxiv.org/pdf/2204.00036v2.pdf | null | [
"Braghadeesh Lakshminarayanan",
"Cristian R. Rojas"
] | [
"Model Optimization"
] | 1,648,684,800,000 | [
{
"code_snippet_url": "https://github.com/mchelali/TemporalStability",
"description": "Spatio-temporal features extraction that measure the stabilty. The proposed method is based on a compression algorithm named Run Length Encoding. The workflow of the method is presented bellow.",
"full_name": "Spatio-temporal stability analysis",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Feature Extractors** for object detection are modules used to construct features that can be used for detecting objects. They address issues such as the need to detect multiple-sized objects in an image (and the need to have representations that are suitable for the different scales).",
"name": "Feature Extractors",
"parent": null
},
"name": "TS",
"source_title": null,
"source_url": null
}
] | 33,281 |
176,258 | https://paperswithcode.com/paper/registration-of-serial-sections-an-evaluation | 2011.11060 | Registration of serial sections: An evaluation method based on distortions of the ground truths | Registration of histological serial sections is a challenging task. Serial sections exhibit distortions and damage from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations extremely difficult. This work proposes methods for ground-truth-based evaluation of registrations. Firstly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Secondly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. Both under- and over-registration become evident in our evaluations. We also survey existing validation efforts. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available. | https://arxiv.org/abs/2011.11060v2 | https://arxiv.org/pdf/2011.11060v2.pdf | null | [
"Oleg Lobachev",
"Takuya Funatomi",
"Alexander Pfaffenroth",
"Reinhold Förster",
"Lars Knudsen",
"Christoph Wrede",
"Michael Guthe",
"David Haberthür",
"Ruslan Hlushchuk",
"Thomas Salaets",
"Jaan Toelen",
"Simone Gaffling",
"Christian Mühlfeld",
"Roman Grothausmann"
] | [
"Medical Image Registration"
] | 1,606,003,200,000 | [] | 70,404 |
259,248 | https://paperswithcode.com/paper/findings-of-the-sentiment-analysis-of | 2111.09811 | Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text | We present the results of the Dravidian-CodeMix shared task held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year's Dravidian-CodeMix shared task held at FIRE 2020. This year's tasks included code-mixing at the intra-token and inter-token levels. Additionally, apart from Tamil and Malayalam, Kannada was also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top system for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs more improvement. | https://arxiv.org/abs/2111.09811v1 | https://arxiv.org/pdf/2111.09811v1.pdf | null | [
"Bharathi Raja Chakravarthi",
"Ruba Priyadharshini",
"Sajeetha Thavareesan",
"Dhivya Chinnappa",
"Durairaj Thenmozhi",
"Elizabeth Sherly",
"John P. McCrae",
"Adeep Hande",
"Rahul Ponnusamy",
"Shubhanker Banerjee",
"Charangan Vasantharajan"
] | [
"Sentiment Analysis"
] | 1,637,193,600,000 | [] | 59,967 |
318,125 | https://paperswithcode.com/paper/superpixel-generation-and-clustering-for | 2209.09930 | Superpixel Generation and Clustering for Weakly Supervised Brain Tumor Segmentation in MR Images | Training Machine Learning (ML) models to segment tumors and other anomalies in medical images is an increasingly popular area of research but generally requires manually annotated ground truth segmentations which necessitates significant time and resources to create. This work proposes a pipeline of ML models that utilize binary classification labels, which can be easily acquired, to segment ROIs without requiring ground truth annotations. We used 2D slices of Magnetic Resonance Imaging (MRI) brain scans from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset and labels indicating the presence of high-grade glioma (HGG) tumors to train the pipeline. Our pipeline also introduces a novel variation of deep learning-based superpixel generation, which enables training guided by clustered superpixels and simultaneously trains a superpixel clustering model. On our test set, our pipeline's segmentations achieved a Dice coefficient of 61.7%, which is a substantial improvement over the 42.8% Dice coefficient acquired when the popular Local Interpretable Model-Agnostic Explanations (LIME) method was used. | https://arxiv.org/abs/2209.09930v1 | https://arxiv.org/pdf/2209.09930v1.pdf | null | [
"Jay J. Yoo",
"Khashayar Namdar",
"Farzad Khalvati"
] | [
"Brain Tumor Segmentation",
"Superpixels",
"Tumor Segmentation"
] | 1,663,632,000,000 | [] | 71,065 |
19,086 | https://paperswithcode.com/paper/protecting-genomic-privacy-by-a-sequence | 1708.02629 | Protecting Genomic Privacy by a Sequence-Similarity Based Obfuscation Method | In the post-genomic era, large-scale personal DNA sequences are produced and
collected for genetic medical diagnoses and new drug discovery, which, however,
simultaneously poses serious challenges to the protection of personal genomic
privacy. Existing genomic privacy-protection methods are either time-consuming
or with low accuracy. To tackle these problems, this paper proposes a sequence
similarity-based obfuscation method, namely IterMegaBLAST, for fast and
reliable protection of personal genomic privacy. Specifically, given a randomly
selected sequence from a dataset of DNA sequences, we first use MegaBLAST to
find its most similar sequence from the dataset. These two aligned sequences
form a cluster, for which an obfuscated sequence was generated via a DNA
generalization lattice scheme. These procedures are iteratively performed until
all of the sequences in the dataset are clustered and their obfuscated
sequences are generated. Experimental results on two benchmark datasets
demonstrate that under the same degree of anonymity, IterMegaBLAST
significantly outperforms existing state-of-the-art approaches in terms of both
utility accuracy and time complexity. | http://arxiv.org/abs/1708.02629v1 | http://arxiv.org/pdf/1708.02629v1.pdf | null | [
"Shibiao Wan",
"Man-Wai Mak",
"Sun-Yuan Kung"
] | [
"Drug Discovery"
] | 1,502,150,400,000 | [] | 125,763 |
234,365 | https://paperswithcode.com/paper/active-reinforcement-learning-over-mdps | 2108.02323 | Active Reinforcement Learning over MDPs | The past decade has seen the rapid development of Reinforcement Learning, which acquires impressive performance with numerous training resources. However, one of the greatest challenges in RL is generalization efficiency (i.e., generalization performance in a unit time). This paper proposes a framework of Active Reinforcement Learning (ARL) over MDPs to improve generalization efficiency in a limited resource by instance selection. Given a number of instances, the algorithm chooses out valuable instances as training sets while training the policy, thereby costing fewer resources. Unlike existing approaches, we attempt to actively select and use training data rather than train on all the given data, thereby costing fewer resources. Furthermore, we introduce a general instance evaluation metrics and selection mechanism into the framework. Experiments results reveal that the proposed framework with Proximal Policy Optimization as policy optimizer can effectively improve generalization efficiency than unselect-ed and unbiased selected methods. | https://arxiv.org/abs/2108.02323v3 | https://arxiv.org/pdf/2108.02323v3.pdf | null | [
"Qi Yang",
"Peng Yang",
"Ke Tang"
] | [
"reinforcement-learning"
] | 1,628,121,600,000 | [] | 175,870 |
283,831 | https://paperswithcode.com/paper/partitioning-image-representation-in | 2203.10454 | Partitioning Image Representation in Contrastive Learning | In contrastive learning in the image domain, the anchor and positive samples are forced to have as close representations as possible. However, forcing the two samples to have the same representation could be misleading because the data augmentation techniques make the two samples different. In this paper, we introduce a new representation, partitioned representation, which can learn both common and unique features of the anchor and positive samples in contrastive learning. The partitioned representation consists of two parts: the content part and the style part. The content part represents common features of the class, and the style part represents the own features of each sample, which can lead to the representation of the data augmentation method. We can achieve the partitioned representation simply by decomposing a loss function of contrastive learning into two terms on the two separate representations, respectively. To evaluate our representation with two parts, we take two framework models: Variational AutoEncoder (VAE) and BootstrapYour Own Latent(BYOL) to show the separability of content and style, and to confirm the generalization ability in classification, respectively. Based on the experiments, we show that our approach can separate two types of information in the VAE framework and outperforms the conventional BYOL in linear separability and a few-shot learning task as downstream tasks. | https://arxiv.org/abs/2203.10454v3 | https://arxiv.org/pdf/2203.10454v3.pdf | null | [
"Hyunsub Lee",
"Heeyoul Choi"
] | [
"Contrastive Learning",
"Data Augmentation",
"Few-Shot Learning"
] | 1,647,734,400,000 | [
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "BYOL (Bootstrap Your Own Latent) is a new approach to self-supervised learning. BYOL’s goal is to learn a representation $y_θ$ which can then be used for downstream tasks. BYOL uses two neural networks to learn: the online and target networks. The online network is defined by a set of weights $θ$ and is comprised of three stages: an encoder $f_θ$, a projector $g_θ$ and a predictor $q_θ$. The target network has the same architecture\r\nas the online network, but uses a different set of weights $ξ$. The target network provides the regression\r\ntargets to train the online network, and its parameters $ξ$ are an exponential moving average of the\r\nonline parameters $θ$.\r\n\r\nGiven the architecture diagram on the right, BYOL minimizes a similarity loss between $q_θ(z_θ)$ and $sg(z'{_ξ})$, where $θ$ are the trained weights, $ξ$ are an exponential moving average of $θ$ and $sg$ means stop-gradient. At the end of training, everything but $f_θ$ is discarded, and $y_θ$ is used as the image representation.\r\n\r\nSource: [Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning](https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to-1)\r\n\r\nImage credit: [Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning](https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to-1)",
"full_name": "Bootstrap Your Own Latent",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Self-Supervised Learning** refers to a category of methods where we learn representations in a self-supervised way (i.e without labels). These methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. Below you can find a continuously updating list of self-supervised methods.",
"name": "Self-Supervised Learning",
"parent": null
},
"name": "BYOL",
"source_title": "Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning",
"source_url": "http://proceedings.neurips.cc/paper/2020/hash/f3ada80d5c4ee70142b17b8192b2958e-Abstract.html"
},
{
"code_snippet_url": "https://github.com/AntixK/PyTorch-VAE/blob/8700d245a9735640dda458db4cf40708caf2e77f/models/vanilla_vae.py#L8",
"description": "A **Variational Autoencoder** is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$, and a decoder, that takes a latent representation $z$ and returns a reconstruction $\\hat{x}$. Inference is performed via variational inference to approximate the posterior of the model.",
"full_name": "Variational Autoencoder",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "VAE",
"source_title": "Auto-Encoding Variational Bayes",
"source_url": "http://arxiv.org/abs/1312.6114v10"
},
{
"code_snippet_url": "https://github.com/L1aoXingyu/pytorch-beginner/blob/9c86be785c7c318a09cf29112dd1f1a58613239b/08-AutoEncoder/simple_autoencoder.py#L38",
"description": "An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and then performs a reconstruction of the input with this latent code (the decoder).\r\n\r\nImage: [Michael Massi](https://en.wikipedia.org/wiki/Autoencoder#/media/File:Autoencoder_schema.png)",
"full_name": "AutoEncoder",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "AutoEncoder",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] | 89,198 |
30,005 | https://paperswithcode.com/paper/chinese-event-extraction-using-deepneural | 1610.00842 | Chinese Event Extraction Using DeepNeural Network with Word Embedding | A lot of prior work on event extraction has exploited a variety of features
to represent events. Such methods have several drawbacks: 1) the features are
often specific for a particular domain and do not generalize well; 2) the
features are derived from various linguistic analyses and are error-prone; and
3) some features may be expensive and require domain expert. In this paper, we
develop a Chinese event extraction system that uses word embedding vectors to
represent language, and deep neural networks to learn the abstract feature
representation in order to greatly reduce the effort of feature engineering. In
addition, in this framework, we leverage large amount of unlabeled data, which
can address the problem of limited labeled corpus for this task. Our
experiments show that our proposed method performs better compared to the
system using rich language features, and using unlabeled data benefits the word
embeddings. This study suggests the potential of DNN and word embedding for the
event extraction task. | http://arxiv.org/abs/1610.00842v1 | http://arxiv.org/pdf/1610.00842v1.pdf | null | [
"Yandi Xia",
"Yang Liu"
] | [
"Event Extraction",
"Feature Engineering",
"Word Embeddings"
] | 1,475,539,200,000 | [] | 136,979 |
113,800 | https://paperswithcode.com/paper/generator-evaluator-selector-net-a-modular | 1908.09108 | Generator evaluator-selector net for panoptic image segmentation and splitting unfamiliar objects into parts | In machine learning and other fields, suggesting a good solution to a problem is usually a harder task than evaluating the quality of such a solution. This asymmetry is the basis for a large number of selection oriented methods that use a generator system to guess a set of solutions and an evaluator system to rank and select the best solutions. This work examines the use of this approach to the problem of panoptic image segmentation and class agnostic parts segmentation. The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects, stuff and parts regions in the image, and an evaluator net that chooses the best segments to be merged into the segmentation map. The result is a trial and error evolutionary approach in which a generator that guesses segments with low average accuracy, but with wide variability, can still produce good results when coupled with an accurate evaluator. The generator consists of a Pointer net that receives an image and a point in the image, and predicts the region of the segment containing the point. Generating and evaluating each segment separately is essential in this case since it demands exponentially fewer guesses compared to a system that guesses and evaluates the full segmentation map in each try. The classification of the selected segments is done by an independent region-specific classification net. This allows the segmentation to be class agnostic and hence, capable of segmenting unfamiliar categories that were not part of the training set. The method was examined on the COCO Panoptic segmentation benchmark and gave results comparable to those of the basic semantic segmentation and Mask-RCNN methods. In addition, the system was used for the task of splitting objects of unseen classes (that did not appear in the training set) into parts. | https://arxiv.org/abs/1908.09108v4 | https://arxiv.org/pdf/1908.09108v4.pdf | null | [
"Sagi Eppel",
"Alan Aspuru-Guzik"
] | [
"Image Segmentation",
"Instance Segmentation",
"Panoptic Segmentation",
"Semantic Segmentation"
] | 1,566,604,800,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/intelpro/trajectory/blob/967131ca5c16af5f6ab09fe724eae4077e1e4596/trajectron/model/components/additive_attention.py#L6",
"description": "**Additive Attention**, also known as **Bahdanau Attention**, uses a one-hidden layer feed-forward network to calculate the attention alignment score:\r\n\r\n$$f_{att}\\left(\\textbf{h}_{i}, \\textbf{s}\\_{j}\\right) = v\\_{a}^{T}\\tanh\\left(\\textbf{W}\\_{a}\\left[\\textbf{h}\\_{i};\\textbf{s}\\_{j}\\right]\\right)$$\r\n\r\nwhere $\\textbf{v}\\_{a}$ and $\\textbf{W}\\_{a}$ are learned attention parameters. Here $\\textbf{h}$ refers to the hidden states for the encoder, and $\\textbf{s}$ is the hidden states for the decoder. The function above is thus a type of alignment score function. We can use a matrix of alignment scores to show the correlation between source and target words, as the Figure to the right shows.\r\n\r\nWithin a neural network, once we have the alignment scores, we calculate the final scores using a [softmax](https://paperswithcode.com/method/softmax) function of these alignment scores (ensuring it sums to 1).",
"full_name": "Additive Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Mechanisms** are a component used in neural networks to model long-range interaction, for example across a text in NLP. The key idea is to build shortcuts between a context vector and the input, to allow a model to attend to different parts. Below you can find a continuously updating list of attention mechanisms.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Additive Attention",
"source_title": "Neural Machine Translation by Jointly Learning to Align and Translate",
"source_url": "http://arxiv.org/abs/1409.0473v7"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Pointer Networks** tackle problems where input and output data are sequential data, but can't be solved by seq2seq type models because discrete categories of output elements depend on the variable input size (and are not decided in advance).\r\n\r\nA Pointer Network learns the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. They solve the problem of variable size output dictionaries using [additive attention](https://paperswithcode.com/method/additive-attention). But instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, Pointer Networks use attention as a pointer to select a member of the input sequence as the output. \r\n\r\nPointer-Nets can be used to learn approximate solutions to challenging geometric problems such as finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem.",
"full_name": "Pointer Network",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Sequence To Sequence Models",
"parent": null
},
"name": "Pointer Network",
"source_title": "Pointer Networks",
"source_url": "http://arxiv.org/abs/1506.03134v2"
}
] | 69,268 |
290,518 | https://paperswithcode.com/paper/gabor-is-enough-interpretable-deep-denoising | 2204.11146 | Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Prior | Image processing neural networks, natural and artificial, have a long history with orientation-selectivity, often described mathematically as Gabor filters. Gabor-like filters have been observed in the early layers of CNN classifiers and even throughout low-level image processing networks. In this work, we take this observation to the extreme and explicitly constrain the filters of a natural-image denoising CNN to be learned 2D real Gabor filters. Surprisingly, we find that the proposed network (GDLNet) can achieve near state-of-the-art denoising performance amongst popular fully convolutional neural networks, with only a fraction of the learned parameters. We further verify that this parameterization maintains the noise-level generalization (training vs. inference mismatch) characteristics of the base network, and investigate the contribution of individual Gabor filter parameters to the performance of the denoiser. We present positive findings for the interpretation of dictionary learning networks as performing accelerated sparse-coding via the importance of untied learned scale parameters between network layers. Our network's success suggests that representations used by low-level image processing CNNs can be as simple and interpretable as Gabor filterbanks. | https://arxiv.org/abs/2204.11146v1 | https://arxiv.org/pdf/2204.11146v1.pdf | null | [
"Nikola Janjušević",
"Amirhossein Khalilian-Gourtani",
"Yao Wang"
] | [
"Denoising",
"Dictionary Learning",
"Image Denoising"
] | 1,650,672,000,000 | [
{
"code_snippet_url": null,
"description": "",
"full_name": "Balanced Selection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Active Learning",
"parent": null
},
"name": "BASE",
"source_title": "Active Learning at the ImageNet Scale",
"source_url": "https://arxiv.org/abs/2111.12880v1"
}
] | 130,519 |
275,825 | https://paperswithcode.com/paper/classification-on-sentence-embeddings-for | 2202.02639 | Classification on Sentence Embeddings for Legal Assistance | Legal proceedings take plenty of time and also cost a lot. The lawyers have to do a lot of work in order to identify the different sections of prior cases and statutes. The paper tries to solve the first tasks in AILA2021 (Artificial Intelligence for Legal Assistance) that will be held in FIRE2021 (Forum for Information Retrieval Evaluation). The task is to semantically segment the document into different assigned one of the 7 predefined labels or "rhetorical roles." The paper uses BERT to obtain the sentence embeddings from a sentence, and then a linear classifier is used to output the final prediction. The experiments show that when more weightage is assigned to the class with the highest frequency, the results are better than those when more weightage is given to the class with a lower frequency. In task 1, the team legalNLP obtained a F1 score of 0.22. | https://arxiv.org/abs/2202.02639v1 | https://arxiv.org/pdf/2202.02639v1.pdf | null | [
"Arka Mitra"
] | [
"Classification",
"Information Retrieval",
"Sentence Embedding"
] | 1,644,019,200,000 | [
{
"code_snippet_url": null,
"description": "**Linear Warmup With Linear Decay** is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.",
"full_name": "Linear Warmup With Linear Decay",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Linear Warmup With Linear Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where elements are randomly dropped out of the [softmax](https://paperswithcode.com/method/softmax) in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$",
"full_name": "Attention Dropout",
"introduced_year": 2018,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Attention Dropout",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L584",
"description": "The **Gaussian Error Linear Unit**, or **GELU**, is an activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their percentile, rather than gates inputs by their sign as in [ReLUs](https://paperswithcode.com/method/relu) ($x\\mathbf{1}_{x>0}$). Consequently the GELU can be thought of as a smoother ReLU.\r\n\r\n$$\\text{GELU}\\left(x\\right) = x{P}\\left(X\\leq{x}\\right) = x\\Phi\\left(x\\right) = x \\cdot \\frac{1}{2}\\left[1 + \\text{erf}(x/\\sqrt{2})\\right],$$\r\nif $X\\sim \\mathcal{N}(0,1)$.\r\n\r\nOne can approximate the GELU with\r\n$0.5x\\left(1+\\tanh\\left[\\sqrt{2/\\pi}\\left(x + 0.044715x^{3}\\right)\\right]\\right)$ or $x\\sigma\\left(1.702x\\right),$\r\nbut PyTorch's exact implementation is sufficiently fast such that these approximations may be unnecessary. (See also the [SiLU](https://paperswithcode.com/method/silu) $x\\sigma(x)$ which was also coined in the paper that introduced the GELU.)\r\n\r\nGELUs are used in [GPT-3](https://paperswithcode.com/method/gpt-3), [BERT](https://paperswithcode.com/method/bert), and most other Transformers.",
"full_name": "Gaussian Error Linear Units",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "GELU",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v4"
},
{
"code_snippet_url": "",
"description": "**WordPiece** is a subword segmentation algorithm used in natural language processing. The vocabulary is initialized with individual characters in the language, then the most frequent combinations of symbols in the vocabulary are iteratively added to the vocabulary. The process is:\r\n\r\n1. Initialize the word unit inventory with all the characters in the text.\r\n2. Build a language model on the training data using the inventory from 1.\r\n3. Generate a new word unit by combining two units out of the current word inventory to increment the word unit inventory by one. Choose the new word unit out of all the possible ones that increases the likelihood on the training data the most when added to the model.\r\n4. Goto 2 until a predefined limit of word units is reached or the likelihood increase falls below a certain threshold.\r\n\r\nText: [Source](https://stackoverflow.com/questions/55382596/how-is-wordpiece-tokenization-helpful-to-effectively-deal-with-rare-words-proble/55416944#55416944)\r\n\r\nImage: WordPiece as used in [BERT](https://paperswithcode.com/method/bert)",
"full_name": "WordPiece",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "WordPiece",
"source_title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
"source_url": "http://arxiv.org/abs/1609.08144v2"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/5c0264915ab43485adc576f88971fc3d42b10445/transformer/Modules.py#L7",
"description": "**Scaled dot-product attention** is an attention mechanism where the dot products are scaled down by $\\sqrt{d_k}$. Formally we have a query $Q$, a key $K$ and a value $V$ and calculate the attention as:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$\r\n\r\nIf we assume that $q$ and $k$ are $d_k$-dimensional vectors whose components are independent random variables with mean $0$ and variance $1$, then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} u_iv_i$, has mean $0$ and variance $d_k$. Since we would prefer these values to have variance $1$, we divide by $\\sqrt{d_k}$.",
"full_name": "Scaled Dot-Product Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Mechanisms** are a component used in neural networks to model long-range interaction, for example across a text in NLP. The key idea is to build shortcuts between a context vector and the input, to allow a model to attend to different parts. Below you can find a continuously updating list of attention mechanisms.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Scaled Dot-Product Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "",
"description": "**Weight Decay**, or **$L_{2}$ Regularization**, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the $L\\_{2}$ Norm of the weights:\r\n\r\n$$L\\_{new}\\left(w\\right) = L\\_{original}\\left(w\\right) + \\lambda{w^{T}w}$$\r\n\r\nwhere $\\lambda$ is a value determining the strength of the penalty (encouraging smaller weights). \r\n\r\nWeight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function).\r\n\r\nImage Source: Deep Learning, Goodfellow et al",
"full_name": "Weight Decay",
"introduced_year": 1943,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Weight Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google-research/bert",
"description": "**BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a *next sentence prediction* task that jointly pre-trains text-pair representations. \r\n\r\nThere are two steps in BERT: *pre-training* and *fine-tuning*. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they\r\nare initialized with the same pre-trained parameters.",
"full_name": "BERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "BERT",
"source_title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"source_url": "https://arxiv.org/abs/1810.04805v2"
}
] | 2,904 |
26,974 | https://paperswithcode.com/paper/visual-inertial-monocular-slam-with-map-reuse | 1610.05949 | Visual-Inertial Monocular SLAM with Map Reuse | In recent years there have been excellent results in Visual-Inertial Odometry
techniques, which aim to compute the incremental motion of the sensor with high
accuracy and robustness. However these approaches lack the capability to close
loops, and trajectory estimation accumulates drift even if the sensor is
continually revisiting the same place. In this work we present a novel
tightly-coupled Visual-Inertial Simultaneous Localization and Mapping system
that is able to close loops and reuse its map to achieve zero-drift
localization in already mapped areas. While our approach can be applied to any
camera configuration, we address here the most general problem of a monocular
camera, with its well-known scale ambiguity. We also propose a novel IMU
initialization method, which computes the scale, the gravity direction, the
velocity, and gyroscope and accelerometer biases, in a few seconds with high
accuracy. We test our system in the 11 sequences of a recent micro-aerial
vehicle public dataset achieving a typical scale factor error of 1% and
centimeter precision. We compare to the state-of-the-art in visual-inertial
odometry in sequences with revisiting, proving the better accuracy of our
method due to map reuse and no drift accumulation. | http://arxiv.org/abs/1610.05949v2 | http://arxiv.org/pdf/1610.05949v2.pdf | null | [
"Raul Mur-Artal",
"Juan D. Tardos"
] | [
"Simultaneous Localization and Mapping"
] | 1,476,835,200,000 | [] | 137,553 |
124,202 | https://paperswithcode.com/paper/t-ss3-a-text-classifier-with-dynamic-n-grams | 1911.06147 | t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams | A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF's eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important "on the fly". This paper introduces t-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that t-SS3 is able to improve both current results and the richness of visual explanations. | https://arxiv.org/abs/1911.06147v2 | https://arxiv.org/pdf/1911.06147v2.pdf | null | [
"Sergio G. Burdisso",
"Marcelo Errecalde",
"Manuel Montes-y-Gómez"
] | [
"Anorexia Detection",
"Classification",
"Document Classification",
"Classification",
"Multi-Label Text Classification",
"Sentence Classification",
"Text Categorization",
"Text Classification"
] | 1,573,430,400,000 | [] | 157,581 |
222,405 | https://paperswithcode.com/paper/apes-audiovisual-person-search-in-untrimmed | 2106.01667 | APES: Audiovisual Person Search in Untrimmed Video | Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous efforts in the person reidentification and retrieval domains, few works have developed audiovisual search strategies. In this paper, we present the Audiovisual Person Search dataset (APES), a new dataset composed of untrimmed videos whose audio (voices) and visual (faces) streams are densely annotated. APES contains over 1.9K identities labeled along 36 hours of video, making it the largest dataset available for untrimmed audiovisual person search. A key property of APES is that it includes dense temporal annotations that link faces to speech segments of the same identity. To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval. Our study shows that modeling audiovisual cues benefits the recognition of people's identities. To enable reproducibility and promote future research, the dataset annotations and baseline code are available at: https://github.com/fuankarion/audiovisual-person-search | https://arxiv.org/abs/2106.01667v1 | https://arxiv.org/pdf/2106.01667v1.pdf | null | [
"Juan Leon Alcazar",
"Long Mai",
"Federico Perazzi",
"Joon-Young Lee",
"Pablo Arbelaez",
"Bernard Ghanem",
"Fabian Caba Heilbron"
] | [
"Person Retrieval",
"Person Search",
"Video Summarization"
] | 1,622,678,400,000 | [] | 129,135 |
58,543 | https://paperswithcode.com/paper/textbook-question-answering-with-knowledge | 1811.00232 | Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension | In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of the TQA dataset. First, solving the TQA problems requires to comprehend multi-modal contexts in complicated input data. To tackle this issue of extracting knowledge features from long text lessons and merging them with visual features, we establish a context graph from texts and images, and propose a new module f-GCN based on graph convolutional networks (GCN). Second, scientific terms are not spread over the chapters and subjects are split in the TQA dataset. To overcome this so called "out-of-domain" issue, before learning QA problems, we introduce a novel self-supervised open-set learning process without any annotations. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems. | https://arxiv.org/abs/1811.00232v2 | https://arxiv.org/pdf/1811.00232v2.pdf | ACL 2019 7 | [
"Daesik Kim",
"Seonhoon Kim",
"Nojun Kwak"
] | [
"Open Set Learning",
"Question Answering",
"Reading Comprehension",
"Self-Supervised Learning"
] | 1,541,030,400,000 | [
{
"code_snippet_url": "",
"description": "A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs.\r\n\r\nImage source: [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/pdf/1609.02907v4.pdf)",
"full_name": "Graph Convolutional Networks",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "The Graph Methods include neural network architectures for learning on graphs with prior structure information, popularly called as Graph Neural Networks (GNNs).\r\n\r\nRecently, deep learning approaches are being extended to work on graph-structured data, giving rise to a series of graph neural networks addressing different challenges. Graph neural networks are particularly useful in applications where data are generated from non-Euclidean domains and represented as graphs with complex relationships. \r\n\r\nSome tasks where GNNs are widely used include [node classification](https://paperswithcode.com/task/node-classification), [graph classification](https://paperswithcode.com/task/graph-classification), [link prediction](https://paperswithcode.com/task/link-prediction), and much more. \r\n\r\nIn the taxonomy presented by [Wu et al. (2019)](https://paperswithcode.com/paper/a-comprehensive-survey-on-graph-neural), graph neural networks can be divided into four categories: **recurrent graph neural networks**, **convolutional graph neural networks**, **graph autoencoders**, and **spatial-temporal graph neural networks**.\r\n\r\nImage source: [A Comprehensive Survey on Graph NeuralNetworks](https://arxiv.org/pdf/1901.00596.pdf)",
"name": "Graph Models",
"parent": null
},
"name": "Graph Convolutional Networks",
"source_title": "Semi-Supervised Classification with Graph Convolutional Networks",
"source_url": "http://arxiv.org/abs/1609.02907v4"
}
] | 89,016 |
133,412 | https://paperswithcode.com/paper/differentiable-graph-module-dgm-graph | 2002.04999 | Differentiable Graph Module (DGM) for Graph Convolutional Networks | Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising results on a broad spectrum of applications ranging from social science, biomedicine, and particle physics to computer vision, graphics, and chemistry. One of the limitations of the majority of current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is {\em known} and {\em fixed}. Often, this assumption is not true since the graph may be noisy, or partially and even completely unknown. In such cases, it would be helpful to infer the graph directly from the data, especially in inductive settings where some nodes were not present in the graph at training time. Furthermore, learning a graph may become an end in itself, as the inferred structure may provide complementary insights next to the downstream task. In this paper, we introduce Differentiable Graph Module (DGM), a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. DGM can be combined with convolutional graph neural network layers and trained in an end-to-end fashion. We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning). We show that our model provides a significant improvement over baselines both in transductive and inductive settings and achieves state-of-the-art results. | https://arxiv.org/abs/2002.04999v4 | https://arxiv.org/pdf/2002.04999v4.pdf | null | [
"Anees Kazi",
"Luca Cosmo",
"Seyed-Ahmad Ahmadi",
"Nassir Navab",
"Michael Bronstein"
] | [
"Disease Prediction",
"Point Cloud Segmentation",
"Zero-Shot Learning"
] | 1,581,379,200,000 | [] | 165,182 |
6,739 | https://paperswithcode.com/paper/conformal-prediction-in-learning-under | 1803.11136 | Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery | This paper explores conformal prediction in the learning under privileged
information (LUPI) paradigm. We use the SVM+ realization of LUPI in an
inductive conformal predictor, and apply it to the MNIST benchmark dataset and
three datasets in drug discovery. The results show that using privileged
information produces valid models and improves efficiency compared to standard
SVM, however the improvement varies between the tested datasets and is not
substantial in the drug discovery applications. More importantly, using SVM+ in
a conformal prediction framework enables valid prediction intervals at
specified significance levels. | http://arxiv.org/abs/1803.11136v2 | http://arxiv.org/pdf/1803.11136v2.pdf | null | [
"Niharika Gauraha",
"Lars Carlsson",
"Ola Spjuth"
] | [
"Drug Discovery",
"Prediction Intervals"
] | 1,522,281,600,000 | [] | 137,643 |
67,445 | https://paperswithcode.com/paper/enhancing-drug-drug-interaction | null | Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble | The study of drug-drug interaction (DDI) is important in the drug discovering. Both PubMed and DrugBank are rich resources to retrieve DDI information which is usually represented in plain text. Automatically extracting DDI pairs from text improves the quality of drug discov-ering. In this paper, we presented a study that focuses on the DDI classification. We normalized the drug names, and developed both sentence-level and corpus-level features for DDI classification. A classifier ensemble approach is used for the unbalance DDI labels problem. Our approach achieved an F-score of 65.4{\%} on SemEval 2013 DDI test set. The experimental results also show the effects of proposed corpus-level features in the DDI task. | https://aclanthology.org/W17-5808 | https://aclanthology.org/W17-5808.pdf | WS 2017 11 | [
"Jing Cyun Tu",
"Po-Ting Lai",
"Richard Tzong-Han Tsai"
] | [
"Classification",
"Feature Engineering",
"Classification",
"Named Entity Recognition"
] | 1,509,494,400,000 | [] | 168,364 |
107,556 | https://paperswithcode.com/paper/a-winograd-based-integrated-photonics | 1906.10487 | A Winograd-based Integrated Photonics Accelerator for Convolutional Neural Networks | Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition. This success in part is due to the widespread availability of capable underlying hardware platforms. Applications have always been a driving factor for design of such hardware architectures. Hardware specialization can expose us to novel architectural solutions, which can outperform general purpose computers for tasks at hand. Although different applications demand for different performance measures, they all share speed and energy efficiency as high priorities. Meanwhile, photonics processing has seen a resurgence due to its inherited high speed and low power nature. Here, we investigate the potential of using photonics in CNNs by proposing a CNN accelerator design based on Winograd filtering algorithm. Our evaluation results show that while a photonic accelerator can compete with current-state-of-the-art electronic platforms in terms of both speed and power, it has the potential to improve the energy efficiency by up to three orders of magnitude. | https://arxiv.org/abs/1906.10487v2 | https://arxiv.org/pdf/1906.10487v2.pdf | null | [
"Armin Mehrabian",
"Mario Miscuglio",
"Yousra Alkabani",
"Volker J. Sorger",
"Tarek El-Ghazawi"
] | [
"Speech Recognition",
"Speech Recognition"
] | 1,561,420,800,000 | [] | 89,883 |
297,794 | https://paperswithcode.com/paper/multimodal-masked-autoencoders-learn | 2205.14204 | Multimodal Masked Autoencoders Learn Transferable Representations | Building scalable models to learn from diverse, multimodal data remains an open challenge. For vision-language data, the dominant approaches are based on contrastive learning objectives that train a separate encoder for each modality. While effective, contrastive learning approaches introduce sampling bias depending on the data augmentations used, which can degrade performance on downstream tasks. Moreover, these methods are limited to paired image-text data, and cannot leverage widely-available unpaired data. In this paper, we investigate whether a large multimodal model trained purely via masked token prediction, without using modality-specific encoders or contrastive learning, can learn transferable representations for downstream tasks. We propose a simple and scalable network architecture, the Multimodal Masked Autoencoder (M3AE), which learns a unified encoder for both vision and language data via masked token prediction. We provide an empirical study of M3AE trained on a large-scale image-text dataset, and find that M3AE is able to learn generalizable representations that transfer well to downstream tasks. Surprisingly, we find that M3AE benefits from a higher text mask ratio (50-90%), in contrast to BERT whose standard masking ratio is 15%, due to the joint training of two data modalities. We also provide qualitative analysis showing that the learned representation incorporates meaningful information from both image and language. Lastly, we demonstrate the scalability of M3AE with larger model size and training time, and its flexibility to train on both paired image-text data as well as unpaired data. | https://arxiv.org/abs/2205.14204v2 | https://arxiv.org/pdf/2205.14204v2.pdf | null | [
"Xinyang Geng",
"Hao liu",
"Lisa Lee",
"Dale Schuurmans",
"Sergey Levine",
"Pieter Abbeel"
] | [
"Contrastive Learning"
] | 1,653,609,600,000 | [
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L584",
"description": "The **Gaussian Error Linear Unit**, or **GELU**, is an activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their percentile, rather than gates inputs by their sign as in [ReLUs](https://paperswithcode.com/method/relu) ($x\\mathbf{1}_{x>0}$). Consequently the GELU can be thought of as a smoother ReLU.\r\n\r\n$$\\text{GELU}\\left(x\\right) = x{P}\\left(X\\leq{x}\\right) = x\\Phi\\left(x\\right) = x \\cdot \\frac{1}{2}\\left[1 + \\text{erf}(x/\\sqrt{2})\\right],$$\r\nif $X\\sim \\mathcal{N}(0,1)$.\r\n\r\nOne can approximate the GELU with\r\n$0.5x\\left(1+\\tanh\\left[\\sqrt{2/\\pi}\\left(x + 0.044715x^{3}\\right)\\right]\\right)$ or $x\\sigma\\left(1.702x\\right),$\r\nbut PyTorch's exact implementation is sufficiently fast such that these approximations may be unnecessary. (See also the [SiLU](https://paperswithcode.com/method/silu) $x\\sigma(x)$ which was also coined in the paper that introduced the GELU.)\r\n\r\nGELUs are used in [GPT-3](https://paperswithcode.com/method/gpt-3), [BERT](https://paperswithcode.com/method/bert), and most other Transformers.",
"full_name": "Gaussian Error Linear Units",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "GELU",
"source_title": "Gaussian Error Linear Units (GELUs)",
"source_url": "https://arxiv.org/abs/1606.08415v4"
},
{
"code_snippet_url": "",
"description": "**Weight Decay**, or **$L_{2}$ Regularization**, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the $L\\_{2}$ Norm of the weights:\r\n\r\n$$L\\_{new}\\left(w\\right) = L\\_{original}\\left(w\\right) + \\lambda{w^{T}w}$$\r\n\r\nwhere $\\lambda$ is a value determining the strength of the penalty (encouraging smaller weights). \r\n\r\nWeight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function).\r\n\r\nImage Source: Deep Learning, Goodfellow et al",
"full_name": "Weight Decay",
"introduced_year": 1943,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Weight Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/5c0264915ab43485adc576f88971fc3d42b10445/transformer/Modules.py#L7",
"description": "**Scaled dot-product attention** is an attention mechanism where the dot products are scaled down by $\\sqrt{d_k}$. Formally we have a query $Q$, a key $K$ and a value $V$ and calculate the attention as:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$\r\n\r\nIf we assume that $q$ and $k$ are $d_k$-dimensional vectors whose components are independent random variables with mean $0$ and variance $1$, then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} u_iv_i$, has mean $0$ and variance $d_k$. Since we would prefer these values to have variance $1$, we divide by $\\sqrt{d_k}$.",
"full_name": "Scaled Dot-Product Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Mechanisms** are a component used in neural networks to model long-range interaction, for example across a text in NLP. The key idea is to build shortcuts between a context vector and the input, to allow a model to attend to different parts. Below you can find a continuously updating list of attention mechanisms.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Scaled Dot-Product Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "",
"description": "**WordPiece** is a subword segmentation algorithm used in natural language processing. The vocabulary is initialized with individual characters in the language, then the most frequent combinations of symbols in the vocabulary are iteratively added to the vocabulary. The process is:\r\n\r\n1. Initialize the word unit inventory with all the characters in the text.\r\n2. Build a language model on the training data using the inventory from 1.\r\n3. Generate a new word unit by combining two units out of the current word inventory to increment the word unit inventory by one. Choose the new word unit out of all the possible ones that increases the likelihood on the training data the most when added to the model.\r\n4. Goto 2 until a predefined limit of word units is reached or the likelihood increase falls below a certain threshold.\r\n\r\nText: [Source](https://stackoverflow.com/questions/55382596/how-is-wordpiece-tokenization-helpful-to-effectively-deal-with-rare-words-proble/55416944#55416944)\r\n\r\nImage: WordPiece as used in [BERT](https://paperswithcode.com/method/bert)",
"full_name": "WordPiece",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "WordPiece",
"source_title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
"source_url": "http://arxiv.org/abs/1609.08144v2"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where elements are randomly dropped out of the [softmax](https://paperswithcode.com/method/softmax) in the attention equation. For example, for scaled-dot product attention, we would drop elements from the first term:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$",
"full_name": "Attention Dropout",
"introduced_year": 2018,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Attention Dropout",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Linear Warmup With Linear Decay** is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.",
"full_name": "Linear Warmup With Linear Decay",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Linear Warmup With Linear Decay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google-research/bert",
"description": "**BERT**, or Bidirectional Encoder Representations from Transformers, improves upon standard [Transformers](http://paperswithcode.com/method/transformer) by removing the unidirectionality constraint by using a *masked language model* (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a *next sentence prediction* task that jointly pre-trains text-pair representations. \r\n\r\nThere are two steps in BERT: *pre-training* and *fine-tuning*. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they\r\nare initialized with the same pre-trained parameters.",
"full_name": "BERT",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "BERT",
"source_title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"source_url": "https://arxiv.org/abs/1810.04805v2"
},
{
"code_snippet_url": "https://github.com/L1aoXingyu/pytorch-beginner/blob/9c86be785c7c318a09cf29112dd1f1a58613239b/08-AutoEncoder/simple_autoencoder.py#L38",
"description": "An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and then performs a reconstruction of the input with this latent code (the decoder).\r\n\r\nImage: [Michael Massi](https://en.wikipedia.org/wiki/Autoencoder#/media/File:Autoencoder_schema.png)",
"full_name": "AutoEncoder",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "AutoEncoder",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
},
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null,
"source_url": null
}
] | 194,755 |
102,830 | https://paperswithcode.com/paper/persistent-homology-detects-curvature | 1905.13196 | Persistent homology detects curvature | In topological data analysis, persistent homology is used to study the "shape of data". Persistent homology computations are completely characterized by a set of intervals called a bar code. It is often said that the long intervals represent the "topological signal" and the short intervals represent "noise". We give evidence to dispute this thesis, showing that the short intervals encode geometric information. Specifically, we prove that persistent homology detects the curvature of disks from which points have been sampled. We describe a general computational framework for solving inverse problems using the average persistence landscape, a continuous mapping from metric spaces with a probability measure to a Hilbert space. In the present application, the average persistence landscapes of points sampled from disks of constant curvature results in a path in this Hilbert space which may be learned using standard tools from statistical and machine learning. | https://arxiv.org/abs/1905.13196v3 | https://arxiv.org/pdf/1905.13196v3.pdf | null | [
"Peter Bubenik",
"Michael Hull",
"Dhruv Patel",
"Benjamin Whittle"
] | [
"Topological Data Analysis"
] | 1,559,174,400,000 | [] | 108,890 |
157,901 | https://paperswithcode.com/paper/word-embeddings-stability-and-semantic-change | 2007.16006 | Word Embeddings: Stability and Semantic Change | Word embeddings are computed by a class of techniques within natural language processing (NLP), that create continuous vector representations of words in a language from a large text corpus. The stochastic nature of the training process of most embedding techniques can lead to surprisingly strong instability, i.e. subsequently applying the same technique to the same data twice, can produce entirely different results. In this work, we present an experimental study on the instability of the training process of three of the most influential embedding techniques of the last decade: word2vec, GloVe and fastText. Based on the experimental results, we propose a statistical model to describe the instability of embedding techniques and introduce a novel metric to measure the instability of the representation of an individual word. Finally, we propose a method to minimize the instability - by computing a modified average over multiple runs - and apply it to a specific linguistic problem: The detection and quantification of semantic change, i.e. measuring changes in the meaning and usage of words over time. | https://arxiv.org/abs/2007.16006v1 | https://arxiv.org/pdf/2007.16006v1.pdf | null | [
"Lucas Rettenmeier"
] | [
"Word Embeddings"
] | 1,595,462,400,000 | [
{
"code_snippet_url": "",
"description": "**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two words and the logarithm of their number of co-occurrences:\r\n\r\n$$ J=\\sum\\_{i, j=1}^{V}f\\left(𝑋\\_{i j}\\right)(w^{T}\\_{i}\\tilde{w}_{j} + b\\_{i} + \\tilde{b}\\_{j} - \\log{𝑋}\\_{ij})^{2} $$\r\n\r\nwhere $w\\_{i}$ and $b\\_{i}$ are the word vector and bias respectively of word $i$, $\\tilde{w}_{j}$ and $b\\_{j}$ are the context word vector and bias respectively of word $j$, $X\\_{ij}$ is the number of times word $i$ occurs in the context of word $j$, and $f$ is a weighting function that assigns lower weights to rare and frequent co-occurrences.",
"full_name": "GloVe Embeddings",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "GloVe",
"source_title": "GloVe: Global Vectors for Word Representation",
"source_url": "https://aclanthology.org/D14-1162"
},
{
"code_snippet_url": null,
"description": "**fastText** embeddings exploit subword information to construct word embeddings. Representations are learnt of character $n$-grams, and words represented as the sum of the $n$-gram vectors. This extends the word2vec type models with subword information. This helps the embeddings understand suffixes and prefixes. Once a word is represented using character $n$-grams, a skipgram model is trained to learn the embeddings.",
"full_name": "fastText",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "fastText",
"source_title": "Enriching Word Vectors with Subword Information",
"source_url": "http://arxiv.org/abs/1607.04606v2"
}
] | 5,608 |
194,302 | https://paperswithcode.com/paper/generating-adversarial-disturbances-for | 2012.06695 | Generating Adversarial Disturbances for Controller Verification | We consider the problem of generating maximally adversarial disturbances for a given controller assuming only blackbox access to it. We propose an online learning approach to this problem that \emph{adaptively} generates disturbances based on control inputs chosen by the controller. The goal of the disturbance generator is to minimize \emph{regret} versus a benchmark disturbance-generating policy class, i.e., to maximize the cost incurred by the controller as well as possible compared to the best possible disturbance generator \emph{in hindsight} (chosen from a benchmark policy class). In the setting where the dynamics are linear and the costs are quadratic, we formulate our problem as an online trust region (OTR) problem with memory and present a new online learning algorithm (\emph{MOTR}) for this problem. We prove that this method competes with the best disturbance generator in hindsight (chosen from a rich class of benchmark policies that includes linear-dynamical disturbance generating policies). We demonstrate our approach on two simulated examples: (i) synthetically generated linear systems, and (ii) generating wind disturbances for the popular PX4 controller in the AirSim simulator. On these examples, we demonstrate that our approach outperforms several baseline approaches, including $H_{\infty}$ disturbance generation and gradient-based methods. | https://arxiv.org/abs/2012.06695v2 | https://arxiv.org/pdf/2012.06695v2.pdf | null | [
"Udaya Ghai",
"David Snyder",
"Anirudha Majumdar",
"Elad Hazan"
] | [
"online learning"
] | 1,607,731,200,000 | [] | 68,424 |
196,091 | https://paperswithcode.com/paper/neural-document-expansion-for-ad-hoc | 2012.14005 | Neural document expansion for ad-hoc information retrieval | Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task. However, this approach needs a large amount of in-domain training data. In this paper, we show that this neural document expansion approach can be effectively adapted to standard IR tasks, where labels are scarce and many long documents are present. | https://arxiv.org/abs/2012.14005v1 | https://arxiv.org/pdf/2012.14005v1.pdf | null | [
"Cheng Tang",
"Andrew Arnold"
] | [
"Ad-Hoc Information Retrieval",
"Information Retrieval"
] | 1,609,027,200,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Activation functions** are functions that we apply in neural networks after (typically) applying an affine transformation combining weights and input features. They are typically non-linear functions. The rectified linear unit, or ReLU, has been the most popular in the past decade, although the choice is architecture dependent and many alternatives have emerged in recent years. In this section, you will find a constantly updating list of activation functions.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Seq2Seq**, or **Sequence To Sequence**, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one [LSTM](https://paperswithcode.com/method/lstm), the *encoder*, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the *decoder*, to extract the output sequence\r\nfrom that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.\r\n\r\n(Note that this page refers to the original seq2seq not general sequence-to-sequence models)",
"full_name": "Sequence to Sequence",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Sequence To Sequence Models",
"parent": null
},
"name": "Seq2Seq",
"source_title": "Sequence to Sequence Learning with Neural Networks",
"source_url": "http://arxiv.org/abs/1409.3215v3"
}
] | 62,960 |
76,046 | https://paperswithcode.com/paper/predicting-aircraft-trajectories-a-deep | 1812.11670 | Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach | Reliable 4D aircraft trajectory prediction, whether in a real-time setting or
for analysis of counterfactuals, is important to the efficiency of the aviation
system. Toward this end, we first propose a highly generalizable efficient
tree-based matching algorithm to construct image-like feature maps from
high-fidelity meteorological datasets - wind, temperature and convective
weather. We then model the track points on trajectories as conditional Gaussian
mixtures with parameters to be learned from our proposed deep generative model,
which is an end-to-end convolutional recurrent neural network that consists of
a long short-term memory (LSTM) encoder network and a mixture density LSTM
decoder network. The encoder network embeds last-filed flight plan information
into fixed-size hidden state variables and feeds the decoder network, which
further learns the spatiotemporal correlations from the historical flight
tracks and outputs the parameters of Gaussian mixtures. Convolutional layers
are integrated into the pipeline to learn representations from the
high-dimension weather features. During the inference process, beam search,
adaptive Kalman filter, and Rauch-Tung-Striebel smoother algorithms are used to
prune the variance of generated trajectories. | http://arxiv.org/abs/1812.11670v1 | http://arxiv.org/pdf/1812.11670v1.pdf | null | [
"Yulin Liu",
"Mark Hansen"
] | [
"Trajectory Prediction"
] | 1,546,214,400,000 | [] | 808 |
301,088 | https://paperswithcode.com/paper/transvg-end-to-end-visual-grounding-with-1 | 2206.06619 | TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformer | In this work, we explore neat yet effective Transformer-based frameworks for visual grounding. The previous methods generally address the core problem of visual grounding, i.e., multi-modal fusion and reasoning, with manually-designed mechanisms. Such heuristic designs are not only complicated but also make models easily overfit specific data distributions. To avoid this, we first propose TransVG, which establishes multi-modal correspondences by Transformers and localizes referred regions by directly regressing box coordinates. We empirically show that complicated fusion modules can be replaced by a simple stack of Transformer encoder layers with higher performance. However, the core fusion Transformer in TransVG is stand-alone against uni-modal encoders, and thus should be trained from scratch on limited visual grounding data, which makes it hard to be optimized and leads to sub-optimal performance. To this end, we further introduce TransVG++ to make two-fold improvements. For one thing, we upgrade our framework to a purely Transformer-based one by leveraging Vision Transformer (ViT) for vision feature encoding. For another, we devise Language Conditioned Vision Transformer that removes external fusion modules and reuses the uni-modal ViT for vision-language fusion at the intermediate layers. We conduct extensive experiments on five prevalent datasets, and report a series of state-of-the-art records. | https://arxiv.org/abs/2206.06619v1 | https://arxiv.org/pdf/2206.06619v1.pdf | null | [
"Jiajun Deng",
"Zhengyuan Yang",
"Daqing Liu",
"Tianlang Chen",
"Wengang Zhou",
"Yanyong Zhang",
"Houqiang Li",
"Wanli Ouyang"
] | [
"Visual Grounding"
] | 1,655,164,800,000 | [
{
"code_snippet_url": null,
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k-1}$ and $1-\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/5c0264915ab43485adc576f88971fc3d42b10445/transformer/Modules.py#L7",
"description": "**Scaled dot-product attention** is an attention mechanism where the dot products are scaled down by $\\sqrt{d_k}$. Formally we have a query $Q$, a key $K$ and a value $V$ and calculate the attention as:\r\n\r\n$$ {\\text{Attention}}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^{T}}{\\sqrt{d_k}}\\right)V $$\r\n\r\nIf we assume that $q$ and $k$ are $d_k$-dimensional vectors whose components are independent random variables with mean $0$ and variance $1$, then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} u_iv_i$, has mean $0$ and variance $d_k$. Since we would prefer these values to have variance $1$, we divide by $\\sqrt{d_k}$.",
"full_name": "Scaled Dot-Product Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Mechanisms** are a component used in neural networks to model long-range interaction, for example across a text in NLP. The key idea is to build shortcuts between a context vector and the input, to allow a model to attend to different parts. Below you can find a continuously updating list of attention mechanisms.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Scaled Dot-Product Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "http://arxiv.org/abs/1706.03762v5"
},
{
"code_snippet_url": "",
"description": "The **Vision Transformer**, or **ViT**, is a model for image classification that employs a [Transformer](https://paperswithcode.com/method/transformer)-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard [Transformer](https://paperswithcode.com/method/transformer) encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.",
"full_name": "Vision Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Vision Transformer",
"source_title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
"source_url": "https://arxiv.org/abs/2010.11929v2"
}
] | 68,310 |
219,361 | https://paperswithcode.com/paper/automatic-fake-news-detection-are-models | 2105.07698 | Automatic Fake News Detection: Are Models Learning to Reason? | Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence. When these models perform well, it is generally assumed to be due to the models having learned to reason over the evidence with regards to the claim. In this paper, we investigate this assumption of reasoning, by exploring the relationship and importance of both claim and evidence. Surprisingly, we find on political fact checking datasets that most often the highest effectiveness is obtained by utilizing only the evidence, as the impact of including the claim is either negligible or harmful to the effectiveness. This highlights an important problem in what constitutes evidence in existing approaches for automatic fake news detection. | https://arxiv.org/abs/2105.07698v1 | https://arxiv.org/pdf/2105.07698v1.pdf | ACL 2021 5 | [
"Casper Hansen",
"Christian Hansen",
"Lucas Chaves Lima"
] | [
"Fact Checking",
"Fake News Detection"
] | 1,621,209,600,000 | [] | 70,839 |
121,054 | https://paperswithcode.com/paper/integrating-overlapping-datasets-using | 1910.11356 | Integrating overlapping datasets using bivariate causal discovery | Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the assumption that all variables have been jointly measured in a single dataset. In many cases this assumption fails. Previous approaches to overcoming this shortcoming devised algorithms that returned all joint causal structures consistent with the conditional independence information contained in each individual dataset. But, as conditional independence tests only determine causal structure up to Markov equivalence, the number of consistent joint structures returned by these approaches can be quite large. The last decade has seen the development of elegant algorithms for discovering causal relations beyond conditional independence, which can distinguish among Markov equivalent structures. In this work we adapt and extend these so-called bivariate causal discovery algorithms to the problem of learning consistent causal structures from multiple datasets with overlapping variables belonging to the same generating process, providing a sound and complete algorithm that outperforms previous approaches on synthetic and real data. | https://arxiv.org/abs/1910.11356v2 | https://arxiv.org/pdf/1910.11356v2.pdf | null | [
"Anish Dhir",
"Ciarán M. Lee"
] | [
"Causal Discovery"
] | 1,571,875,200,000 | [] | 43,378 |
202,075 | https://paperswithcode.com/paper/diverse-auto-curriculum-is-critical-for | 2102.07659 | Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems | Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum framework, which shapes the learning process by continually creating new challenging tasks for agents to adapt to, thereby facilitating the acquisition of new skills. In order to extend MARL methods to real-world domains outside of video games, we envision in this blue sky paper that maintaining a diversity-aware auto-curriculum is critical for successful MARL applications. Specifically, we argue that \emph{behavioural diversity} is a pivotal, yet under-explored, component for real-world multiagent learning systems, and that significant work remains in understanding how to design a diversity-aware auto-curriculum. We list four open challenges for auto-curriculum techniques, which we believe deserve more attention from this community. Towards validating our vision, we recommend modelling realistic interactive behaviours in autonomous driving as an important test bed, and recommend the SMARTS/ULTRA benchmark. | https://arxiv.org/abs/2102.07659v2 | https://arxiv.org/pdf/2102.07659v2.pdf | null | [
"Yaodong Yang",
"Jun Luo",
"Ying Wen",
"Oliver Slumbers",
"Daniel Graves",
"Haitham Bou Ammar",
"Jun Wang",
"Matthew E. Taylor"
] | [
"Autonomous Driving"
] | 1,613,347,200,000 | [] | 16,882 |
161,926 | https://paperswithcode.com/paper/optimization-driven-machine-learning-for | 2008.12938 | Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks | Intelligent reflecting surface (IRS) has been recently employed to reshape the wireless channels by controlling individual scattering elements' phase shifts, namely, passive beamforming. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information. In this article, we focus on machine learning (ML) approaches for performance maximization in IRS-assisted wireless networks. In general, ML approaches provide enhanced flexibility and robustness against uncertain information and imprecise modeling. Practical challenges still remain mainly due to the demand for a large dataset in offline training and slow convergence in online learning. These observations motivate us to design a novel optimization-driven ML framework for IRS-assisted wireless networks, which takes both advantages of the efficiency in model-based optimization and the robustness in model-free ML approaches. By splitting the decision variables into two parts, one part is obtained by the outer-loop ML approach, while the other part is optimized efficiently by solving an approximate problem. Numerical results verify that the optimization-driven ML approach can improve both the convergence and the reward performance compared to conventional model-free learning approaches. | https://arxiv.org/abs/2008.12938v1 | https://arxiv.org/pdf/2008.12938v1.pdf | null | [
"Shimin Gong",
"Jiaye Lin",
"Jinbei Zhang",
"Dusit Niyato",
"Dong In Kim",
"Mohsen Guizani"
] | [
"online learning"
] | 1,598,659,200,000 | [] | 184,701 |
3,113 | https://paperswithcode.com/paper/discovering-discrete-latent-topics-with | 1706.00359 | Discovering Discrete Latent Topics with Neural Variational Inference | Topic models have been widely explored as probabilistic generative models of
documents. Traditional inference methods have sought closed-form derivations
for updating the models, however as the expressiveness of these models grows,
so does the difficulty of performing fast and accurate inference over their
parameters. This paper presents alternative neural approaches to topic
modelling by providing parameterisable distributions over topics which permit
training by backpropagation in the framework of neural variational inference.
In addition, with the help of a stick-breaking construction, we propose a
recurrent network that is able to discover a notionally unbounded number of
topics, analogous to Bayesian non-parametric topic models. Experimental results
on the MXM Song Lyrics, 20NewsGroups and Reuters News datasets demonstrate the
effectiveness and efficiency of these neural topic models. | http://arxiv.org/abs/1706.00359v2 | http://arxiv.org/pdf/1706.00359v2.pdf | ICML 2017 8 | [
"Yishu Miao",
"Edward Grefenstette",
"Phil Blunsom"
] | [
"Topic Models",
"Variational Inference"
] | 1,496,275,200,000 | [] | 133,113 |
131,521 | https://paperswithcode.com/paper/pmindia-a-collection-of-parallel-corpora-of | 2001.09907 | PMIndia -- A Collection of Parallel Corpora of Languages of India | Parallel text is required for building high-quality machine translation (MT) systems, as well as for other multilingual NLP applications. For many South Asian languages, such data is in short supply. In this paper, we described a new publicly available corpus (PMIndia) consisting of parallel sentences which pair 13 major languages of India with English. The corpus includes up to 56000 sentences for each language pair. We explain how the corpus was constructed, including an assessment of two different automatic sentence alignment methods, and present some initial NMT results on the corpus. | https://arxiv.org/abs/2001.09907v1 | https://arxiv.org/pdf/2001.09907v1.pdf | null | [
"Barry Haddow",
"Faheem Kirefu"
] | [
"Machine Translation",
"Multilingual NLP"
] | 1,580,083,200,000 | [] | 118,790 |
223,298 | https://paperswithcode.com/paper/multi-agent-battery-storage-management-using | 2106.03541 | Multi-agent Battery Storage Management using MPC-based Reinforcement Learning | In this paper, we present the use of Model Predictive Control (MPC) based on Reinforcement Learning (RL) to find the optimal policy for a multi-agent battery storage system. A time-varying prediction of the power price and production-demand uncertainty are considered. We focus on optimizing an economic objective cost while avoiding very low or very high state of charge, which can damage the battery. We consider the bounded power provided by the main grid and the constraints on the power input and state of each agent. A parametrized MPC-scheme is used as a function approximator for the deterministic policy gradient method and RL optimizes the closed-loop performance by updating the parameters. Simulation results demonstrate that the proposed method is able to tackle the constraints and deliver the optimal policy. | https://arxiv.org/abs/2106.03541v1 | https://arxiv.org/pdf/2106.03541v1.pdf | null | [
"A. Bahari Kordabad",
"W. Cai",
"S. Gros"
] | [
"reinforcement-learning"
] | 1,623,024,000,000 | [] | 92,193 |
211,819 | https://paperswithcode.com/paper/robust-classification-under-ell-0-attack-for | 2104.02189 | Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model | It is well-known that machine learning models are vulnerable to small but cleverly-designed adversarial perturbations that can cause misclassification. While there has been major progress in designing attacks and defenses for various adversarial settings, many fundamental and theoretical problems are yet to be resolved. In this paper, we consider classification in the presence of $\ell_0$-bounded adversarial perturbations, a.k.a. sparse attacks. This setting is significantly different from other $\ell_p$-adversarial settings, with $p\geq 1$, as the $\ell_0$-ball is non-convex and highly non-smooth. Under the assumption that data is distributed according to the Gaussian mixture model, our goal is to characterize the optimal robust classifier and the corresponding robust classification error as well as a variety of trade-offs between robustness, accuracy, and the adversary's budget. To this end, we develop a novel classification algorithm called FilTrun that has two main modules: Filtration and Truncation. The key idea of our method is to first filter out the non-robust coordinates of the input and then apply a carefully-designed truncated inner product for classification. By analyzing the performance of FilTrun, we derive an upper bound on the optimal robust classification error. We also find a lower bound by designing a specific adversarial strategy that enables us to derive the corresponding robust classifier and its achieved error. For the case that the covariance matrix of the Gaussian mixtures is diagonal, we show that as the input's dimension gets large, the upper and lower bounds converge; i.e. we characterize the asymptotically-optimal robust classifier. Throughout, we discuss several examples that illustrate interesting behaviors such as the existence of a phase transition for adversary's budget determining whether the effect of adversarial perturbation can be fully neutralized. | https://arxiv.org/abs/2104.02189v1 | https://arxiv.org/pdf/2104.02189v1.pdf | null | [
"Payam Delgosha",
"Hamed Hassani",
"Ramtin Pedarsani"
] | [
"Classification",
"Classification",
"Robust classification"
] | 1,617,580,800,000 | [] | 177,387 |
43,000 | https://paperswithcode.com/paper/consistent-optimization-of-ams-by-logistic | 1412.2106 | Consistent optimization of AMS by logistic loss minimization | In this paper, we theoretically justify an approach popular among
participants of the Higgs Boson Machine Learning Challenge to optimize
approximate median significance (AMS). The approach is based on the following
two-stage procedure. First, a real-valued function is learned by minimizing a
surrogate loss for binary classification, such as logistic loss, on the
training sample. Then, a threshold is tuned on a separate validation sample, by
direct optimization of AMS. We show that the regret of the resulting
(thresholded) classifier measured with respect to the squared AMS, is
upperbounded by the regret of the underlying real-valued function measured with
respect to the logistic loss. Hence, we prove that minimizing logistic
surrogate is a consistent method of optimizing AMS. | http://arxiv.org/abs/1412.2106v1 | http://arxiv.org/pdf/1412.2106v1.pdf | null | [
"Wojciech Kotłowski"
] | [
"Classification"
] | 1,417,737,600,000 | [] | 144,736 |
203,250 | https://paperswithcode.com/paper/a-hierarchical-conditional-random-field-based | 2102.10499 | A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification | In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field. | https://arxiv.org/abs/2102.10499v2 | https://arxiv.org/pdf/2102.10499v2.pdf | null | [
"Yixin Li",
"Xinran Wu",
"Chen Li",
"Changhao Sun",
"Md Rahaman",
"HaoYuan Chen",
"YuDong Yao",
"Xiaoyan Li",
"Yong Zhang",
"Tao Jiang"
] | [
"Classification",
"Ensemble Learning",
"Classification",
"Image Classification"
] | 1,613,865,600,000 | [] | 57,866 |
76,510 | https://paperswithcode.com/paper/quantized-epoch-sgd-for-communication | 1901.03040 | Quantized Epoch-SGD for Communication-Efficient Distributed Learning | Due to its efficiency and ease to implement, stochastic gradient descent
(SGD) has been widely used in machine learning. In particular, SGD is one of
the most popular optimization methods for distributed learning. Recently,
quantized SGD (QSGD), which adopts quantization to reduce the communication
cost in SGD-based distributed learning, has attracted much attention. Although
several QSGD methods have been proposed, some of them are heuristic without
theoretical guarantee, and others have high quantization variance which makes
the convergence become slow. In this paper, we propose a new method, called
Quantized Epoch-SGD (QESGD), for communication-efficient distributed learning.
QESGD compresses (quantizes) the parameter with variance reduction, so that it
can get almost the same performance as that of SGD with less communication
cost. QESGD is implemented on the Parameter Server framework, and empirical
results on distributed deep learning show that QESGD can outperform other
state-of-the-art quantization methods to achieve the best performance. | http://arxiv.org/abs/1901.03040v1 | http://arxiv.org/pdf/1901.03040v1.pdf | null | [
"Shen-Yi Zhao",
"Hao Gao",
"Wu-Jun Li"
] | [
"Quantization"
] | 1,547,078,400,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "SGD",
"source_title": null,
"source_url": null
}
] | 116,180 |
175,005 | https://paperswithcode.com/paper/online-topology-identification-from-vector | 1904.01864 | Online Topology Identification from Vector Autoregressive Time Series | Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these algorithms appealing for big-data scenarios. Despite using data sequentially, both algorithms are shown to asymptotically attain the same average performance as a batch estimator which uses the entire data set at once. To this end, sublinear (static) regret bounds are established. Performance is also characterized in time-varying setups by means of dynamic regret analysis. Numerical results with real and synthetic data further support the merits of the proposed algorithms in static and dynamic scenarios. | https://arxiv.org/abs/1904.01864v2 | https://arxiv.org/pdf/1904.01864v2.pdf | null | [
"Bakht Zaman",
"Luis Miguel Lopez Ramos",
"Daniel Romero",
"Baltasar Beferull-Lozano"
] | [
"Anomaly Detection",
"Time Series"
] | 1,554,249,600,000 | [] | 126,316 |
28,808 | https://paperswithcode.com/paper/unsupervised-learning-of-object-semantic | 1511.06855 | Unsupervised learning of object semantic parts from internal states of CNNs by population encoding | We address the key question of how object part representations can be found
from the internal states of CNNs that are trained for high-level tasks, such as
object classification. This work provides a new unsupervised method to learn
semantic parts and gives new understanding of the internal representations of
CNNs. Our technique is based on the hypothesis that semantic parts are
represented by populations of neurons rather than by single filters. We propose
a clustering technique to extract part representations, which we call Visual
Concepts. We show that visual concepts are semantically coherent in that they
represent semantic parts, and visually coherent in that corresponding image
patches appear very similar. Also, visual concepts provide full spatial
coverage of the parts of an object, rather than a few sparse parts as is
typically found in keypoint annotations. Furthermore, We treat single visual
concept as part detector and evaluate it for keypoint detection using the
PASCAL3D+ dataset and for part detection using our newly annotated ImageNetPart
dataset. The experiments demonstrate that visual concepts can be used to detect
parts. We also show that some visual concepts respond to several semantic
parts, provided these parts are visually similar. Thus visual concepts have the
essential properties: semantic meaning and detection capability. Note that our
ImageNetPart dataset gives rich part annotations which cover the whole object,
making it useful for other part-related applications. | http://arxiv.org/abs/1511.06855v3 | http://arxiv.org/pdf/1511.06855v3.pdf | null | [
"Jianyu Wang",
"Zhishuai Zhang",
"Cihang Xie",
"Vittal Premachandran",
"Alan Yuille"
] | [
"Keypoint Detection"
] | 1,448,064,000,000 | [] | 109,246 |
266,603 | https://paperswithcode.com/paper/ltt-gan-looking-through-turbulence-by | 2112.02379 | LTT-GAN: Looking Through Turbulence by Inverting GANs | In many applications of long-range imaging, we are faced with a scenario where a person appearing in the captured imagery is often degraded by atmospheric turbulence. However, restoring such degraded images for face verification is difficult since the degradation causes images to be geometrically distorted and blurry. To mitigate the turbulence effect, in this paper, we propose the first turbulence mitigation method that makes use of visual priors encapsulated by a well-trained GAN. Based on the visual priors, we propose to learn to preserve the identity of restored images on a spatial periodic contextual distance. Such a distance can keep the realism of restored images from the GAN while considering the identity difference at the network learning. In addition, hierarchical pseudo connections are proposed for facilitating the identity-preserving learning by introducing more appearance variance without identity changing. Extensive experiments show that our method significantly outperforms prior art in both the visual quality and face verification accuracy of restored results. | https://arxiv.org/abs/2112.02379v1 | https://arxiv.org/pdf/2112.02379v1.pdf | null | [
"Kangfu Mei",
"Vishal M. Patel"
] | [
"Face Verification"
] | 1,638,576,000,000 | [] | 139,038 |
63,924 | https://paperswithcode.com/paper/learning-and-knowledge-transfer-with-memory | null | Learning and Knowledge Transfer with Memory Networks for Machine Comprehension | Enabling machines to read and comprehend unstructured text remains an unfulfilled goal for NLP research. Recent research efforts on the {``}machine comprehension{''} task have managed to achieve close to ideal performance on simulated data. However, achieving similar levels of performance on small real world datasets has proved difficult; major challenges stem from the large vocabulary size, complex grammar, and, the frequent ambiguities in linguistic structure. On the other hand, the requirement of human generated annotations for training, in order to ensure a sufficiently diverse set of questions is prohibitively expensive. Motivated by these practical issues, we propose a novel curriculum inspired training procedure for Memory Networks to improve the performance for machine comprehension with relatively small volumes of training data. Additionally, we explore various training regimes for Memory Networks to allow knowledge transfer from a closely related domain having larger volumes of labelled data. We also suggest the use of a loss function to incorporate the asymmetric nature of knowledge transfer. Our experiments demonstrate improvements on Dailymail, CNN, and MCTest datasets. | https://aclanthology.org/E17-1080 | https://aclanthology.org/E17-1080.pdf | EACL 2017 4 | [
"Mohit Yadav",
"Lovekesh Vig",
"Gautam Shroff"
] | [
"Question Answering",
"Reading Comprehension",
"Transfer Learning"
] | 1,491,004,800,000 | [] | 154,795 |
25,137 | https://paperswithcode.com/paper/online-multilinear-dictionary-learning-for | 1703.02492 | Online Multilinear Dictionary Learning | A method for online tensor dictionary learning is proposed. With the assumption of separable dictionaries, tensor contraction is used to diminish a $N$-way model of $\mathcal{O}\left(L^N\right)$ into a simple matrix equation of $\mathcal{O}\left(NL^2\right)$ with a real-time capability. To avoid numerical instability due to inversion of sparse matrix, a class of stochastic gradient with memory is formulated via a least-square solution to guarantee convergence and robustness. Both gradient descent with exact line search and Newton's method are discussed and realized. Extensions onto how to deal with bad initialization and outliers are also explained in detail. Experiments on two synthetic signals confirms an impressive performance of our proposed method. | https://arxiv.org/abs/1703.02492v5 | https://arxiv.org/pdf/1703.02492v5.pdf | null | [
"Thiernithi Variddhisai",
"Danilo Mandic"
] | [
"Dictionary Learning"
] | 1,488,844,800,000 | [] | 128,526 |
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