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Adaptive Traffic Signal Control Using Reinforcement Learning | http://arxiv.org/abs/2408.15751v1 | http://arxiv.org/abs/2408.15751v1 | http://arxiv.org/pdf/2408.15751v1 | 2024-08-28 | 2024-08-28 | [
"Muhammad Tahir Rafique",
"Ahmed Mustafa",
"Hasan Sajid"
] | [
"",
"",
""
] | Traffic demand is continuously increasing, leading to significant congestion
issues in major urban areas. Constructing new infrastructure is a potential
solution but presents a substantial financial burden on national economies. An
alternative approach involves optimizing existing traffic networks through the
dynamic control of traffic signals at intersections. Recent advancements in
Reinforcement Learning (RL) techniques have demonstrated their capability to
address the complexities associated with traffic congestion. In this paper, we
propose a solution to traffic congestion using reinforcement learning. We
define the state as a scalar representing the queue length, demonstrating that
the algorithm can effectively learn from this simplified state representation.
This approach can potentially reduce deployment costs by minimizing the number
of sensors required at intersections. We have developed two RL algorithms: a
turn-based agent, which prioritizes traffic signals for the intersection side
with higher traffic, and a time-based agent, which adheres to a fixed phase
cycle, adjusting the phase duration based on traffic conditions. To assess the
performance of these algorithms, we designed four distinct traffic scenarios
and computed seven evaluation metrics for each. Simulation results indicate
that both algorithms outperform conventional traffic signal control systems. | cs.AI | [
"cs.AI"
] |
|||
Advanced POD-Based Performance Evaluation of Classifiers Applied to
Human Driver Lane Changing Prediction | http://arxiv.org/abs/2408.15722v1 | http://arxiv.org/abs/2408.15722v1 | http://arxiv.org/pdf/2408.15722v1 | 2024-08-28 | 2024-08-28 | [
"Zahra Rastin",
"Dirk Söffker"
] | [
"",
""
] | Machine learning (ML) classifiers serve as essential tools facilitating
classification and prediction across various domains. The performance of these
algorithms should be known to ensure their reliable application. In certain
fields, receiver operating characteristic and precision-recall curves are
frequently employed to assess machine learning algorithms without accounting
for the impact of process parameters. However, it may be essential to evaluate
the performance of these algorithms in relation to such parameters. As a
performance evaluation metric capable of considering the effects of process
parameters, this paper uses a modified probability of detection (POD) approach
to assess the reliability of ML-based algorithms. As an example, the POD-based
approach is employed to assess ML models used for predicting the lane changing
behavior of a vehicle driver. The time remaining to the predicted (and
therefore unknown) lane changing event is considered as process parameter. The
hit/miss approach to POD is taken here and modified by considering the
probability of lane changing derived from ML algorithms at each time step, and
obtaining the final result of the analysis accordingly. This improves the
reliability of results compared to the standard hit/miss approach, which
considers the outcome of the classifiers as either 0 or 1, while also
simplifying evaluation compared to the \^a versus a approach. Performance
evaluation results of the proposed approach are compared with those obtained
with the standard hit/miss approach and a pre-developed \^a versus a approach
to validate the effectiveness of the proposed method. The comparison shows that
this method provides an averaging conservative behavior with the advantage of
enhancing the reliability of the hit/miss approach to POD while retaining its
simplicity. | Manuscript: 8 pages, 6 figures, 4 tables | 10.1109/ACCESS.2024.0429000 | eess.SY | [
"eess.SY",
"cs.AI",
"cs.LG",
"cs.SY"
] |
|
Evaluating Model Robustness Using Adaptive Sparse L0 Regularization | http://arxiv.org/abs/2408.15702v1 | http://arxiv.org/abs/2408.15702v1 | http://arxiv.org/pdf/2408.15702v1 | 2024-08-28 | 2024-08-28 | [
"Weiyou Liu",
"Zhenyang Li",
"Weitong Chen"
] | [
"",
"",
""
] | Deep Neural Networks have demonstrated remarkable success in various domains
but remain susceptible to adversarial examples, which are slightly altered
inputs designed to induce misclassification. While adversarial attacks
typically optimize under Lp norm constraints, attacks based on the L0 norm,
prioritising input sparsity, are less studied due to their complex and non
convex nature. These sparse adversarial examples challenge existing defenses by
altering a minimal subset of features, potentially uncovering more subtle DNN
weaknesses. However, the current L0 norm attack methodologies face a trade off
between accuracy and efficiency either precise but computationally intense or
expedient but imprecise. This paper proposes a novel, scalable, and effective
approach to generate adversarial examples based on the L0 norm, aimed at
refining the robustness evaluation of DNNs against such perturbations. | Accepted by the 20th International Conference on Advanced Data Mining
and Applications (ADMA 2024) | cs.LG | [
"cs.LG",
"cs.AI",
"F.2.2, I.2.7"
] |
||
G-Style: Stylized Gaussian Splatting | http://arxiv.org/abs/2408.15695v1 | http://arxiv.org/abs/2408.15695v1 | http://arxiv.org/pdf/2408.15695v1 | 2024-08-28 | 2024-08-28 | [
"Áron Samuel Kovács",
"Pedro Hermosilla",
"Renata G. Raidou"
] | [
"",
"",
""
] | We introduce G-Style, a novel algorithm designed to transfer the style of an
image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting
is a powerful 3D representation for novel view synthesis, as -- compared to
other approaches based on Neural Radiance Fields -- it provides fast scene
renderings and user control over the scene. Recent pre-prints have demonstrated
that the style of Gaussian Splatting scenes can be modified using an image
exemplar. However, since the scene geometry remains fixed during the
stylization process, current solutions fall short of producing satisfactory
results. Our algorithm aims to address these limitations by following a
three-step process: In a pre-processing step, we remove undesirable Gaussians
with large projection areas or highly elongated shapes. Subsequently, we
combine several losses carefully designed to preserve different scales of the
style in the image, while maintaining as much as possible the integrity of the
original scene content. During the stylization process and following the
original design of Gaussian Splatting, we split Gaussians where additional
detail is necessary within our scene by tracking the gradient of the stylized
color. Our experiments demonstrate that G-Style generates high-quality
stylizations within just a few minutes, outperforming existing methods both
qualitatively and quantitatively. | cs.GR | [
"cs.GR",
"cs.AI",
"cs.CV"
] |
|||
An Extremely Data-efficient and Generative LLM-based Reinforcement
Learning Agent for Recommenders | http://arxiv.org/abs/2408.16032v1 | http://arxiv.org/abs/2408.16032v1 | http://arxiv.org/pdf/2408.16032v1 | 2024-08-28 | 2024-08-28 | [
"Shuang Feng",
"Grace Feng"
] | [
"",
""
] | Recent advancements in large language models (LLMs) have enabled
understanding webpage contexts, product details, and human instructions.
Utilizing LLMs as the foundational architecture for either reward models or
policies in reinforcement learning has gained popularity -- a notable
achievement is the success of InstructGPT. RL algorithms have been instrumental
in maximizing long-term customer satisfaction and avoiding short-term, myopic
goals in industrial recommender systems, which often rely on deep learning
models to predict immediate clicks or purchases.
In this project, several RL methods are implemented and evaluated using the
WebShop benchmark environment, data, simulator, and pre-trained model
checkpoints. The goal is to train an RL agent to maximize the purchase reward
given a detailed human instruction describing a desired product. The RL agents
are developed by fine-tuning a pre-trained BERT model with various objectives,
learning from preferences without a reward model, and employing contemporary
training techniques such as Proximal Policy Optimization (PPO) as used in
InstructGPT, and Direct Preference Optimization (DPO). This report also
evaluates the RL agents trained using generative trajectories. Evaluations were
conducted using Thompson sampling in the WebShop simulator environment.
The simulated online experiments demonstrate that agents trained on generated
trajectories exhibited comparable task performance to those trained using human
trajectories. This has demonstrated an example of an extremely low-cost
data-efficient way of training reinforcement learning agents. Also, with
limited training time (<2hours), without utilizing any images, a DPO agent
achieved a 19% success rate after approximately 3000 steps or 30 minutes of
training on T4 GPUs, compared to a PPO agent, which reached a 15% success rate. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.IR"
] |
|||
EMP: Enhance Memory in Data Pruning | http://arxiv.org/abs/2408.16031v1 | http://arxiv.org/abs/2408.16031v1 | http://arxiv.org/pdf/2408.16031v1 | 2024-08-28 | 2024-08-28 | [
"Jinying Xiao",
"Ping Li",
"Jie Nie",
"Zhe Tang"
] | [
"",
"",
"",
""
] | Recently, large language and vision models have shown strong performance, but
due to high pre-training and fine-tuning costs, research has shifted towards
faster training via dataset pruning. Previous methods used sample loss as an
evaluation criterion, aiming to select the most "difficult" samples for
training. However, when the pruning rate increases, the number of times each
sample is trained becomes more evenly distributed, which causes many critical
or general samples to not be effectively fitted. We refer to this as
Low-Frequency Learning (LFL). In other words, LFL prevents the model from
remembering most samples. In our work, we decompose the scoring function of
LFL, provide a theoretical explanation for the inefficiency of LFL, and propose
adding a memory term to the scoring function to enhance the model's memory
capability, along with an approximation of this memory term. Similarly, we
explore memory in Self-Supervised Learning (SSL), marking the first discussion
on SSL memory. Using contrastive learning, we derive the memory term both
theoretically and experimentally. Finally, we propose Enhance Memory Pruning
(EMP), which addresses the issue of insufficient memory under high pruning
rates by enhancing the model's memory of data, thereby improving its
performance. We evaluated the performance of EMP in tasks such as image
classification, natural language understanding, and model pre-training. The
results show that EMP can improve model performance under extreme pruning
rates. For example, in the CIFAR100-ResNet50 pre-training task, with 70\%
pruning, EMP outperforms current methods by 2.2\%. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
A Deep Learning Approach to Localizing Multi-level Airway Collapse Based
on Snoring Sounds | http://arxiv.org/abs/2408.16030v1 | http://arxiv.org/abs/2408.16030v1 | http://arxiv.org/pdf/2408.16030v1 | 2024-08-28 | 2024-08-28 | [
"Ying-Chieh Hsu",
"Stanley Yung-Chuan Liu",
"Chao-Jung Huang",
"Chi-Wei Wu",
"Ren-Kai Cheng",
"Jane Yung-Jen Hsu",
"Shang-Ran Huang",
"Yuan-Ren Cheng",
"Fu-Shun Hsu"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | This study investigates the application of machine/deep learning to classify
snoring sounds excited at different levels of the upper airway in patients with
obstructive sleep apnea (OSA) using data from drug-induced sleep endoscopy
(DISE). The snoring sounds of 39 subjects were analyzed and labeled according
to the Velum, Oropharynx, Tongue Base, and Epiglottis (VOTE) classification
system. The dataset, comprising 5,173 one-second segments, was used to train
and test models, including Support Vector Machine (SVM), Bidirectional Long
Short-Term Memory (BiLSTM), and ResNet-50. The ResNet-50, a convolutional
neural network (CNN), showed the best overall performance in classifying
snoring acoustics, particularly in identifying multi-level obstructions. The
study emphasizes the potential of integrating snoring acoustics with deep
learning to improve the diagnosis and treatment of OSA. However, challenges
such as limited sample size, data imbalance, and differences between
pharmacologically induced and natural snoring sounds were noted, suggesting
further research to enhance model accuracy and generalizability. | cs.SD | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] |
|||
An Empirical Study on Self-correcting Large Language Models for Data
Science Code Generation | http://arxiv.org/abs/2408.15658v1 | http://arxiv.org/abs/2408.15658v1 | http://arxiv.org/pdf/2408.15658v1 | 2024-08-28 | 2024-08-28 | [
"Thai Tang Quoc",
"Duc Ha Minh",
"Tho Quan Thanh",
"Anh Nguyen-Duc"
] | [
"",
"",
"",
""
] | Large Language Models (LLMs) have recently advanced many applications on
software engineering tasks, particularly the potential for code generation.
Among contemporary challenges, code generated by LLMs often suffers from
inaccuracies and hallucinations, requiring external inputs to correct. One
recent strategy to fix these issues is to refine the code generated from LLMs
using the input from the model itself (self-augmented). In this work, we
proposed a novel method, namely CoT-SelfEvolve. CoT-SelfEvolve iteratively and
automatically refines code through a self-correcting process, guided by a chain
of thought constructed from real-world programming problem feedback. Focusing
on data science code, including Python libraries such as NumPy and Pandas, our
evaluations on the DS-1000 dataset demonstrate that CoT-SelfEvolve
significantly outperforms existing models in solving complex problems. The
framework shows substantial improvements in both initial code generation and
subsequent iterations, with the model's accuracy increasing significantly with
each additional iteration. This highlights the effectiveness of using
chain-of-thought prompting to address complexities revealed by program executor
traceback error messages. We also discuss how CoT-SelfEvolve can be integrated
into continuous software engineering environments, providing a practical
solution for improving LLM-based code generation. | cs.SE | [
"cs.SE",
"cs.AI"
] |
|||
Harnessing the Intrinsic Knowledge of Pretrained Language Models for
Challenging Text Classification Settings | http://arxiv.org/abs/2408.15650v1 | http://arxiv.org/abs/2408.15650v1 | http://arxiv.org/pdf/2408.15650v1 | 2024-08-28 | 2024-08-28 | [
"Lingyu Gao"
] | [
""
] | Text classification is crucial for applications such as sentiment analysis
and toxic text filtering, but it still faces challenges due to the complexity
and ambiguity of natural language. Recent advancements in deep learning,
particularly transformer architectures and large-scale pretraining, have
achieved inspiring success in NLP fields. Building on these advancements, this
thesis explores three challenging settings in text classification by leveraging
the intrinsic knowledge of pretrained language models (PLMs). Firstly, to
address the challenge of selecting misleading yet incorrect distractors for
cloze questions, we develop models that utilize features based on
contextualized word representations from PLMs, achieving performance that
rivals or surpasses human accuracy. Secondly, to enhance model generalization
to unseen labels, we create small finetuning datasets with domain-independent
task label descriptions, improving model performance and robustness. Lastly, we
tackle the sensitivity of large language models to in-context learning prompts
by selecting effective demonstrations, focusing on misclassified examples and
resolving model ambiguity regarding test example labels. | PhD thesis | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
Hierarchical Blockmodelling for Knowledge Graphs | http://arxiv.org/abs/2408.15649v1 | http://arxiv.org/abs/2408.15649v1 | http://arxiv.org/pdf/2408.15649v1 | 2024-08-28 | 2024-08-28 | [
"Marcin Pietrasik",
"Marek Reformat",
"Anna Wilbik"
] | [
"",
"",
""
] | In this paper, we investigate the use of probabilistic graphical models,
specifically stochastic blockmodels, for the purpose of hierarchical entity
clustering on knowledge graphs. These models, seldom used in the Semantic Web
community, decompose a graph into a set of probability distributions. The
parameters of these distributions are then inferred allowing for their
subsequent sampling to generate a random graph. In a non-parametric setting,
this allows for the induction of hierarchical clusterings without prior
constraints on the hierarchy's structure. Specifically, this is achieved by the
integration of the Nested Chinese Restaurant Process and the Stick Breaking
Process into the generative model. In this regard, we propose a model
leveraging such integration and derive a collapsed Gibbs sampling scheme for
its inference. To aid in understanding, we describe the steps in this
derivation and provide an implementation for the sampler. We evaluate our model
on synthetic and real-world datasets and quantitatively compare against
benchmark models. We further evaluate our results qualitatively and find that
our model is capable of inducing coherent cluster hierarchies in small scale
settings. The work presented in this paper provides the first step for the
further application of stochastic blockmodels for knowledge graphs on a larger
scale. We conclude the paper with potential avenues for future work on more
scalable inference schemes. | 31 pages, 11 figures | cs.AI | [
"cs.AI"
] |
||
GANs Conditioning Methods: A Survey | http://arxiv.org/abs/2408.15640v2 | http://arxiv.org/abs/2408.15640v2 | http://arxiv.org/pdf/2408.15640v2 | 2024-08-28 | 2024-08-29 | [
"Anis Bourou",
"Auguste Genovesio",
"Valérie Mezger"
] | [
"",
"",
""
] | In recent years, Generative Adversarial Networks (GANs) have seen significant
advancements, leading to their widespread adoption across various fields. The
original GAN architecture enables the generation of images without any specific
control over the content, making it an unconditional generation process.
However, many practical applications require precise control over the generated
output, which has led to the development of conditional GANs (cGANs) that
incorporate explicit conditioning to guide the generation process. cGANs extend
the original framework by incorporating additional information (conditions),
enabling the generation of samples that adhere to that specific criteria.
Various conditioning methods have been proposed, each differing in how they
integrate the conditioning information into both the generator and the
discriminator networks. In this work, we review the conditioning methods
proposed for GANs, exploring the characteristics of each method and
highlighting their unique mechanisms and theoretical foundations. Furthermore,
we conduct a comparative analysis of these methods, evaluating their
performance on various image datasets. Through these analyses, we aim to
provide insights into the strengths and limitations of various conditioning
techniques, guiding future research and application in generative modeling. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Structural Optimization of Lightweight Bipedal Robot via SERL | http://arxiv.org/abs/2408.15632v1 | http://arxiv.org/abs/2408.15632v1 | http://arxiv.org/pdf/2408.15632v1 | 2024-08-28 | 2024-08-28 | [
"Yi Cheng",
"Chenxi Han",
"Yuheng Min",
"Linqi Ye",
"Houde Liu",
"Hang Liu"
] | [
"",
"",
"",
"",
"",
""
] | Designing a bipedal robot is a complex and challenging task, especially when
dealing with a multitude of structural parameters. Traditional design methods
often rely on human intuition and experience. However, such approaches are
time-consuming, labor-intensive, lack theoretical guidance and hard to obtain
optimal design results within vast design spaces, thus failing to full exploit
the inherent performance potential of robots. In this context, this paper
introduces the SERL (Structure Evolution Reinforcement Learning) algorithm,
which combines reinforcement learning for locomotion tasks with evolution
algorithms. The aim is to identify the optimal parameter combinations within a
given multidimensional design space. Through the SERL algorithm, we
successfully designed a bipedal robot named Wow Orin, where the optimal leg
length are obtained through optimization based on body structure and motor
torque. We have experimentally validated the effectiveness of the SERL
algorithm, which is capable of optimizing the best structure within specified
design space and task conditions. Additionally, to assess the performance gap
between our designed robot and the current state-of-the-art robots, we compared
Wow Orin with mainstream bipedal robots Cassie and Unitree H1. A series of
experimental results demonstrate the Outstanding energy efficiency and
performance of Wow Orin, further validating the feasibility of applying the
SERL algorithm to practical design. | eess.SY | [
"eess.SY",
"cs.AI",
"cs.SY"
] |
|||
CodeSift: An LLM-Based Reference-Less Framework for Automatic Code
Validation | http://arxiv.org/abs/2408.15630v1 | http://arxiv.org/abs/2408.15630v1 | http://arxiv.org/pdf/2408.15630v1 | 2024-08-28 | 2024-08-28 | [
"Pooja Aggarwal",
"Oishik Chatterjee",
"Ting Dai",
"Prateeti Mohapatra",
"Brent Paulovicks",
"Brad Blancett",
"Arthur De Magalhaes"
] | [
"",
"",
"",
"",
"",
"",
""
] | The advent of large language models (LLMs) has greatly facilitated code
generation, but ensuring the functional correctness of generated code remains a
challenge. Traditional validation methods are often time-consuming,
error-prone, and impractical for large volumes of code. We introduce CodeSift,
a novel framework that leverages LLMs as the first-line filter of code
validation without the need for execution, reference code, or human feedback,
thereby reducing the validation effort. We assess the effectiveness of our
method across three diverse datasets encompassing two programming languages.
Our results indicate that CodeSift outperforms state-of-the-art code evaluation
methods. Internal testing conducted with subject matter experts reveals that
the output generated by CodeSift is in line with human preference, reinforcing
its effectiveness as a dependable automated code validation tool. | cs.SE | [
"cs.SE",
"cs.AI"
] |
|||
CBF-LLM: Safe Control for LLM Alignment | http://arxiv.org/abs/2408.15625v1 | http://arxiv.org/abs/2408.15625v1 | http://arxiv.org/pdf/2408.15625v1 | 2024-08-28 | 2024-08-28 | [
"Yuya Miyaoka",
"Masaki Inoue"
] | [
"",
""
] | This paper proposes a control-based framework for aligning large language
models (LLMs) by leveraging a control barrier function (CBF) to ensure
user-desirable text generation. The presented framework applies the safety
filter, designed based on the CBF, to the output generation of the baseline
LLM, i.e., the sequence of the token, with the aim of intervening in the
generated text. The overall text-generation system is implemented with Llama 3
and a RoBERTa model, and the source code is available at
https://github.com/Mya-Mya/CBF-LLM. The experiment demonstrates its control
ability and effectiveness in reducing the number of interventions needed for
user-specified alignment tasks. | eess.SY | [
"eess.SY",
"cs.AI",
"cs.CL",
"cs.SY"
] |
|||
CGRA4ML: A Framework to Implement Modern Neural Networks for Scientific
Edge Computing | http://arxiv.org/abs/2408.15561v2 | http://arxiv.org/abs/2408.15561v2 | http://arxiv.org/pdf/2408.15561v2 | 2024-08-28 | 2024-08-29 | [
"G Abarajithan",
"Zhenghua Ma",
"Zepeng Li",
"Shrideep Koparkar",
"Ravidu Munasinghe",
"Francesco Restuccia",
"Ryan Kastner"
] | [
"",
"",
"",
"",
"",
"",
""
] | Scientific edge computing increasingly relies on hardware-accelerated neural
networks to implement complex, near-sensor processing at extremely high
throughputs and low latencies. Existing frameworks like HLS4ML are effective
for smaller models, but struggle with larger, modern neural networks due to
their requirement of spatially implementing the neural network layers and
storing all weights in on-chip memory. CGRA4ML is an open-source, modular
framework designed to bridge the gap between neural network model complexity
and extreme performance requirements. CGRA4ML extends the capabilities of
HLS4ML by allowing off-chip data storage and supporting a broader range of
neural network architectures, including models like ResNet, PointNet, and
transformers. Unlike HLS4ML, CGRA4ML generates SystemVerilog RTL, making it
more suitable for targeting ASIC and FPGA design flows. We demonstrate the
effectiveness of our framework by implementing and scaling larger models that
were previously unattainable with HLS4ML, showcasing its adaptability and
efficiency in handling complex computations. CGRA4ML also introduces an
extensive verification framework, with a generated runtime firmware that
enables its integration into different SoC platforms. CGRA4ML's minimal and
modular infrastructure of Python API, SystemVerilog hardware, Tcl toolflows,
and C runtime, facilitates easy integration and experimentation, allowing
scientists to focus on innovation rather than the intricacies of hardware
design and optimization. | cs.AR | [
"cs.AR",
"cs.AI"
] |
|||
Trustworthy and Responsible AI for Human-Centric Autonomous
Decision-Making Systems | http://arxiv.org/abs/2408.15550v1 | http://arxiv.org/abs/2408.15550v1 | http://arxiv.org/pdf/2408.15550v1 | 2024-08-28 | 2024-08-28 | [
"Farzaneh Dehghani",
"Mahsa Dibaji",
"Fahim Anzum",
"Lily Dey",
"Alican Basdemir",
"Sayeh Bayat",
"Jean-Christophe Boucher",
"Steve Drew",
"Sarah Elaine Eaton",
"Richard Frayne",
"Gouri Ginde",
"Ashley Harris",
"Yani Ioannou",
"Catherine Lebel",
"John Lysack",
"Leslie Salgado Arzuaga",
"Emma Stanley",
"Roberto Souza",
"Ronnie Souza",
"Lana Wells",
"Tyler Williamson",
"Matthias Wilms",
"Zaman Wahid",
"Mark Ungrin",
"Marina Gavrilova",
"Mariana Bento"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Artificial Intelligence (AI) has paved the way for revolutionary
decision-making processes, which if harnessed appropriately, can contribute to
advancements in various sectors, from healthcare to economics. However, its
black box nature presents significant ethical challenges related to bias and
transparency. AI applications are hugely impacted by biases, presenting
inconsistent and unreliable findings, leading to significant costs and
consequences, highlighting and perpetuating inequalities and unequal access to
resources. Hence, developing safe, reliable, ethical, and Trustworthy AI
systems is essential.
Our team of researchers working with Trustworthy and Responsible AI, part of
the Transdisciplinary Scholarship Initiative within the University of Calgary,
conducts research on Trustworthy and Responsible AI, including fairness, bias
mitigation, reproducibility, generalization, interpretability, and
authenticity. In this paper, we review and discuss the intricacies of AI
biases, definitions, methods of detection and mitigation, and metrics for
evaluating bias. We also discuss open challenges with regard to the
trustworthiness and widespread application of AI across diverse domains of
human-centric decision making, as well as guidelines to foster Responsible and
Trustworthy AI models. | 45 pages, 2 figures | cs.AI | [
"cs.AI"
] |
||
An Investigation of Warning Erroneous Chat Translations in Cross-lingual
Communication | http://arxiv.org/abs/2408.15543v1 | http://arxiv.org/abs/2408.15543v1 | http://arxiv.org/pdf/2408.15543v1 | 2024-08-28 | 2024-08-28 | [
"Yunmeng Li",
"Jun Suzuki",
"Makoto Morishita",
"Kaori Abe",
"Kentaro Inui"
] | [
"",
"",
"",
"",
""
] | The complexities of chats pose significant challenges for machine translation
models. Recognizing the need for a precise evaluation metric to address the
issues of chat translation, this study introduces Multidimensional Quality
Metrics for Chat Translation (MQM-Chat). Through the experiments of five models
using MQM-Chat, we observed that all models generated certain fundamental
errors, while each of them has different shortcomings, such as omission, overly
correcting ambiguous source content, and buzzword issues, resulting in the loss
of stylized information. Our findings underscore the effectiveness of MQM-Chat
in evaluating chat translation, emphasizing the importance of stylized content
and dialogue consistency for future studies. | IJCNLP-AACL 2023 Student Research Workshop | 10.18653/v1/2023.ijcnlp-srw.2 | cs.CL | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.HC"
] |
|
Kangaroo: A Powerful Video-Language Model Supporting Long-context Video
Input | http://arxiv.org/abs/2408.15542v1 | http://arxiv.org/abs/2408.15542v1 | http://arxiv.org/pdf/2408.15542v1 | 2024-08-28 | 2024-08-28 | [
"Jiajun Liu",
"Yibing Wang",
"Hanghang Ma",
"Xiaoping Wu",
"Xiaoqi Ma",
"Xiaoming Wei",
"Jianbin Jiao",
"Enhua Wu",
"Jie Hu"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Rapid advancements have been made in extending Large Language Models (LLMs)
to Large Multi-modal Models (LMMs). However, extending input modality of LLMs
to video data remains a challenging endeavor, especially for long videos. Due
to insufficient access to large-scale high-quality video data and the excessive
compression of visual features, current methods exhibit limitations in
effectively processing long videos. In this paper, we introduce Kangaroo, a
powerful Video LMM aimed at addressing these challenges. Confronted with issue
of inadequate training data, we develop a data curation system to build a
large-scale dataset with high-quality annotations for vision-language
pre-training and instruction tuning. In addition, we design a curriculum
training pipeline with gradually increasing resolution and number of input
frames to accommodate long videos. Evaluation results demonstrate that, with 8B
parameters, Kangaroo achieves state-of-the-art performance across a variety of
video understanding benchmarks while exhibiting competitive results on others.
Particularly, on benchmarks specialized for long videos, Kangaroo excels some
larger models with over 10B parameters and proprietary models. | cs.CV | [
"cs.CV",
"cs.AI",
"cs.MM"
] |
|||
TrafficGamer: Reliable and Flexible Traffic Simulation for
Safety-Critical Scenarios with Game-Theoretic Oracles | http://arxiv.org/abs/2408.15538v1 | http://arxiv.org/abs/2408.15538v1 | http://arxiv.org/pdf/2408.15538v1 | 2024-08-28 | 2024-08-28 | [
"Guanren Qiao",
"Guorui Quan",
"Jiawei Yu",
"Shujun Jia",
"Guiliang Liu"
] | [
"",
"",
"",
"",
""
] | While modern Autonomous Vehicle (AV) systems can develop reliable driving
policies under regular traffic conditions, they frequently struggle with
safety-critical traffic scenarios. This difficulty primarily arises from the
rarity of such scenarios in driving datasets and the complexities associated
with predictive modeling among multiple vehicles. To support the testing and
refinement of AV policies, simulating safety-critical traffic events is an
essential challenge to be addressed. In this work, we introduce TrafficGamer,
which facilitates game-theoretic traffic simulation by viewing common road
driving as a multi-agent game. In evaluating the empirical performance across
various real-world datasets, TrafficGamer ensures both fidelity and
exploitability of the simulated scenarios, guaranteeing that they not only
statically align with real-world traffic distribution but also efficiently
capture equilibriums for representing safety-critical scenarios involving
multiple agents. Additionally, the results demonstrate that TrafficGamer
exhibits highly flexible simulation across various contexts. Specifically, we
demonstrate that the generated scenarios can dynamically adapt to equilibriums
of varying tightness by configuring risk-sensitive constraints during
optimization. To the best of our knowledge, TrafficGamer is the first simulator
capable of generating diverse traffic scenarios involving multiple agents. We
have provided a demo webpage for the project at
https://qiaoguanren.github.io/trafficgamer-demo/. | cs.AI | [
"cs.AI",
"cs.MA"
] |
|||
Improving Thompson Sampling via Information Relaxation for Budgeted
Multi-armed Bandits | http://arxiv.org/abs/2408.15535v1 | http://arxiv.org/abs/2408.15535v1 | http://arxiv.org/pdf/2408.15535v1 | 2024-08-28 | 2024-08-28 | [
"Woojin Jeong",
"Seungki Min"
] | [
"",
""
] | We consider a Bayesian budgeted multi-armed bandit problem, in which each arm
consumes a different amount of resources when selected and there is a budget
constraint on the total amount of resources that can be used. Budgeted Thompson
Sampling (BTS) offers a very effective heuristic to this problem, but its
arm-selection rule does not take into account the remaining budget information.
We adopt \textit{Information Relaxation Sampling} framework that generalizes
Thompson Sampling for classical $K$-armed bandit problems, and propose a series
of algorithms that are randomized like BTS but more carefully optimize their
decisions with respect to the budget constraint. In a one-to-one correspondence
with these algorithms, a series of performance benchmarks that improve the
conventional benchmark are also suggested. Our theoretical analysis and
simulation results show that our algorithms (and our benchmarks) make
incremental improvements over BTS (respectively, the conventional benchmark)
across various settings including a real-world example. | accepted | Reinforcement Learning Journal, vol. 1, no. 1, 2024, pp. TBD | cs.LG | [
"cs.LG",
"cs.AI"
] |
|
LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via
Layer-wise Relevance Propagation | http://arxiv.org/abs/2408.15533v2 | http://arxiv.org/abs/2408.15533v2 | http://arxiv.org/pdf/2408.15533v2 | 2024-08-28 | 2024-08-29 | [
"Haichuan Hu",
"Yuhan Sun",
"Quanjun Zhang"
] | [
"",
"",
""
] | Retrieval-Augmented Generation (RAG) has become a primary technique for
mitigating hallucinations in large language models (LLMs). However, incomplete
knowledge extraction and insufficient understanding can still mislead LLMs to
produce irrelevant or even contradictory responses, which means hallucinations
persist in RAG. In this paper, we propose LRP4RAG, a method based on the
Layer-wise Relevance Propagation (LRP) algorithm for detecting hallucinations
in RAG. Specifically, we first utilize LRP to compute the relevance between the
input and output of the RAG generator. We then apply further extraction and
resampling to the relevance matrix. The processed relevance data are input into
multiple classifiers to determine whether the output contains hallucinations.
To the best of our knowledge, this is the first time that LRP has been used for
detecting RAG hallucinations, and extensive experiments demonstrate that
LRP4RAG outperforms existing baselines. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Continual-learning-based framework for structural damage recognition | http://arxiv.org/abs/2408.15513v1 | http://arxiv.org/abs/2408.15513v1 | http://arxiv.org/pdf/2408.15513v1 | 2024-08-28 | 2024-08-28 | [
"Jiangpeng Shu",
"Jiawei Zhang",
"Reachsak Ly",
"Fangzheng Lin",
"Yuanfeng Duan"
] | [
"",
"",
"",
"",
""
] | Multi-damage is common in reinforced concrete structures and leads to the
requirement of large number of neural networks, parameters and data storage, if
convolutional neural network (CNN) is used for damage recognition. In addition,
conventional CNN experiences catastrophic forgetting and training inefficiency
as the number of tasks increases during continual learning, leading to large
accuracy decrease of previous learned tasks. To address these problems, this
study proposes a continuallearning-based damage recognition model (CLDRM) which
integrates the learning without forgetting continual learning method into the
ResNet-34 architecture for the recognition of damages in RC structures as well
as relevant structural components. Three experiments for four recognition tasks
were designed to validate the feasibility and effectiveness of the CLDRM
framework. In this way, it reduces both the prediction time and data storage by
about 75% in four tasks of continuous learning. Three experiments for four
recognition tasks were designed to validate the feasibility and effectiveness
of the CLDRM framework. By gradual feature fusion, CLDRM outperformed other
methods by managed to achieve high accuracy in the damage recognition and
classification. As the number of recognition tasks increased, CLDRM also
experienced smaller decrease of the previous learned tasks. Results indicate
that the CLDRM framework successfully performs damage recognition and
classification with reasonable accuracy and effectiveness. | 18 pages, 12 figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Towards Fully Autonomous Research Powered by LLMs: Case Study on
Simulations | http://arxiv.org/abs/2408.15512v1 | http://arxiv.org/abs/2408.15512v1 | http://arxiv.org/pdf/2408.15512v1 | 2024-08-28 | 2024-08-28 | [
"Zhihan Liu",
"Yubo Chai",
"Jianfeng Li"
] | [
"",
"",
""
] | The advent of Large Language Models (LLMs) has created new opportunities for
the automation of scientific research, spanning both experimental processes and
computational simulations. This study explores the feasibility of constructing
an autonomous simulation agent (ASA) powered by LLM, through sophisticated API
integration, to automate the entire research process, from experimental design,
remote upload and simulation execution, data analysis, to report compilation.
Using a simulation problem of polymer chain conformations as a case study, we
assessed the performance of ASAs powered by different LLMs including
GPT-4-Turbo. Our findings revealed that ASA-GPT-4o achieved near-flawless
execution on designated research missions, underscoring the potential of LLMs
to manage complete scientific investigations autonomously. The outlined
automation can be iteratively performed up to twenty cycles without human
intervention, illustrating the potential of LLMs for large-scale autonomous
research endeavors. Additionally, we discussed the intrinsic traits of ASAs in
managing extensive tasks, focusing on self-validation mechanisms and the
balance between local attention and global oversight. | For additional code and data, please visit our GitHub repository:
https://github.com/zokaraa/autonomous_simulation_agent | cs.AI | [
"cs.AI",
"cs.CL",
"physics.chem-ph"
] |
||
AeroVerse: UAV-Agent Benchmark Suite for Simulating, Pre-training,
Finetuning, and Evaluating Aerospace Embodied World Models | http://arxiv.org/abs/2408.15511v1 | http://arxiv.org/abs/2408.15511v1 | http://arxiv.org/pdf/2408.15511v1 | 2024-08-28 | 2024-08-28 | [
"Fanglong Yao",
"Yuanchang Yue",
"Youzhi Liu",
"Xian Sun",
"Kun Fu"
] | [
"",
"",
"",
"",
""
] | Aerospace embodied intelligence aims to empower unmanned aerial vehicles
(UAVs) and other aerospace platforms to achieve autonomous perception,
cognition, and action, as well as egocentric active interaction with humans and
the environment. The aerospace embodied world model serves as an effective
means to realize the autonomous intelligence of UAVs and represents a necessary
pathway toward aerospace embodied intelligence. However, existing embodied
world models primarily focus on ground-level intelligent agents in indoor
scenarios, while research on UAV intelligent agents remains unexplored. To
address this gap, we construct the first large-scale real-world image-text
pre-training dataset, AerialAgent-Ego10k, featuring urban drones from a
first-person perspective. We also create a virtual image-text-pose alignment
dataset, CyberAgent Ego500k, to facilitate the pre-training of the aerospace
embodied world model. For the first time, we clearly define 5 downstream tasks,
i.e., aerospace embodied scene awareness, spatial reasoning, navigational
exploration, task planning, and motion decision, and construct corresponding
instruction datasets, i.e., SkyAgent-Scene3k, SkyAgent-Reason3k, SkyAgent-Nav3k
and SkyAgent-Plan3k, and SkyAgent-Act3k, for fine-tuning the aerospace
embodiment world model. Simultaneously, we develop SkyAgentEval, the downstream
task evaluation metrics based on GPT-4, to comprehensively, flexibly, and
objectively assess the results, revealing the potential and limitations of
2D/3D visual language models in UAV-agent tasks. Furthermore, we integrate over
10 2D/3D visual-language models, 2 pre-training datasets, 5 finetuning
datasets, more than 10 evaluation metrics, and a simulator into the benchmark
suite, i.e., AeroVerse, which will be released to the community to promote
exploration and development of aerospace embodied intelligence. | cs.RO | [
"cs.RO",
"cs.AI"
] |
|||
Measuring the Reliability of Causal Probing Methods: Tradeoffs,
Limitations, and the Plight of Nullifying Interventions | http://arxiv.org/abs/2408.15510v1 | http://arxiv.org/abs/2408.15510v1 | http://arxiv.org/pdf/2408.15510v1 | 2024-08-28 | 2024-08-28 | [
"Marc Canby",
"Adam Davies",
"Chirag Rastogi",
"Julia Hockenmaier"
] | [
"",
"",
"",
""
] | Causal probing is an approach to interpreting foundation models, such as
large language models, by training probes to recognize latent properties of
interest from embeddings, intervening on probes to modify this representation,
and analyzing the resulting changes in the model's behavior. While some recent
works have cast doubt on the theoretical basis of several leading causal
probing intervention methods, it has been unclear how to systematically and
empirically evaluate their effectiveness in practice. To address this problem,
we propose a general empirical analysis framework to evaluate the reliability
of causal probing interventions, formally defining and quantifying two key
causal probing desiderata: completeness (fully transforming the representation
of the target property) and selectivity (minimally impacting other properties).
Our formalism allows us to make the first direct comparisons between different
families of causal probing methods (e.g., linear vs. nonlinear or
counterfactual vs. nullifying interventions). We conduct extensive experiments
across several leading methods, finding that (1) there is an inherent tradeoff
between these criteria, and no method is able to consistently satisfy both at
once; and (2) across the board, nullifying interventions are always far less
complete than counterfactual interventions, indicating that nullifying methods
may not be an effective approach to causal probing. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
|||
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal
Sentiment Analysis | http://arxiv.org/abs/2408.16029v1 | http://arxiv.org/abs/2408.16029v1 | http://arxiv.org/pdf/2408.16029v1 | 2024-08-28 | 2024-08-28 | [
"Sijie Mai",
"Yu Zhao",
"Ying Zeng",
"Jianhua Yao",
"Haifeng Hu"
] | [
"",
"",
"",
"",
""
] | Multimodal sentiment analysis aims to effectively integrate information from
various sources to infer sentiment, where in many cases there are no
annotations for unimodal labels. Therefore, most works rely on multimodal
labels for training. However, there exists the noisy label problem for the
learning of unimodal signals as multimodal annotations are not always the ideal
substitutes for the unimodal ones, failing to achieve finer optimization for
individual modalities. In this paper, we explore the learning of unimodal
labels under the weak supervision from the annotated multimodal labels.
Specifically, we propose a novel meta uni-label generation (MUG) framework to
address the above problem, which leverages the available multimodal labels to
learn the corresponding unimodal labels by the meta uni-label correction
network (MUCN). We first design a contrastive-based projection module to bridge
the gap between unimodal and multimodal representations, so as to use
multimodal annotations to guide the learning of MUCN. Afterwards, we propose
unimodal and multimodal denoising tasks to train MUCN with explicit supervision
via a bi-level optimization strategy. We then jointly train unimodal and
multimodal learning tasks to extract discriminative unimodal features for
multimodal inference. Experimental results suggest that MUG outperforms
competitive baselines and can learn accurate unimodal labels. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
EmoAttack: Utilizing Emotional Voice Conversion for Speech Backdoor
Attacks on Deep Speech Classification Models | http://arxiv.org/abs/2408.15508v1 | http://arxiv.org/abs/2408.15508v1 | http://arxiv.org/pdf/2408.15508v1 | 2024-08-28 | 2024-08-28 | [
"Wenhan Yao",
"Zedong XingXiarun Chen",
"Jia Liu",
"yongqiang He",
"Weiping Wen"
] | [
"",
"",
"",
"",
""
] | Deep speech classification tasks, mainly including keyword spotting and
speaker verification, play a crucial role in speech-based human-computer
interaction. Recently, the security of these technologies has been demonstrated
to be vulnerable to backdoor attacks. Specifically speaking, speech samples are
attacked by noisy disruption and component modification in present triggers. We
suggest that speech backdoor attacks can strategically focus on emotion, a
higher-level subjective perceptual attribute inherent in speech. Furthermore,
we proposed that emotional voice conversion technology can serve as the speech
backdoor attack trigger, and the method is called EmoAttack. Based on this, we
conducted attack experiments on two speech classification tasks, showcasing
that EmoAttack method owns impactful trigger effectiveness and its remarkable
attack success rate and accuracy variance. Additionally, the ablation
experiments found that speech with intensive emotion is more suitable to be
targeted for attacks. | Submitted to ICASSP 2025 | cs.SD | [
"cs.SD",
"cs.AI",
"eess.AS"
] |
||
What Machine Learning Tells Us About the Mathematical Structure of
Concepts | http://arxiv.org/abs/2408.15507v1 | http://arxiv.org/abs/2408.15507v1 | http://arxiv.org/pdf/2408.15507v1 | 2024-08-28 | 2024-08-28 | [
"Jun Otsuka"
] | [
""
] | This paper examines the connections among various approaches to understanding
concepts in philosophy, cognitive science, and machine learning, with a
particular focus on their mathematical nature. By categorizing these approaches
into Abstractionism, the Similarity Approach, the Functional Approach, and the
Invariance Approach, the study highlights how each framework provides a
distinct mathematical perspective for modeling concepts. The synthesis of these
approaches bridges philosophical theories and contemporary machine learning
models, providing a comprehensive framework for future research. This work
emphasizes the importance of interdisciplinary dialogue, aiming to enrich our
understanding of the complex relationship between human cognition and
artificial intelligence. | 25 pages, 3 figures | cs.AI | [
"cs.AI"
] |
||
RoboSense: Large-scale Dataset and Benchmark for Multi-sensor Low-speed
Autonomous Driving | http://arxiv.org/abs/2408.15503v1 | http://arxiv.org/abs/2408.15503v1 | http://arxiv.org/pdf/2408.15503v1 | 2024-08-28 | 2024-08-28 | [
"Haisheng Su",
"Feixiang Song",
"Cong Ma",
"Panpan Cai",
"Wei Wu",
"Cewu Lu"
] | [
"",
"",
"",
"",
"",
""
] | Robust object detection and tracking under arbitrary sight of view is
challenging yet essential for the development of Autonomous Vehicle technology.
With the growing demand of unmanned function vehicles, near-field scene
understanding becomes an important research topic in the areas of low-speed
autonomous driving. Due to the complexity of driving conditions and diversity
of near obstacles such as blind spots and high occlusion, the perception
capability of near-field environment is still inferior than its farther
counterpart. To further enhance the intelligent ability of unmanned vehicles,
in this paper, we construct a multimodal data collection platform based on 3
main types of sensors (Camera, LiDAR and Fisheye), which supports flexible
sensor configurations to enable dynamic sight of view for ego vehicle, either
global view or local view. Meanwhile, a large-scale multi-sensor dataset is
built, named RoboSense, to facilitate near-field scene understanding. RoboSense
contains more than 133K synchronized data with 1.4M 3D bounding box and IDs
annotated in the full $360^{\circ}$ view, forming 216K trajectories across 7.6K
temporal sequences. It has $270\times$ and $18\times$ as many annotations of
near-field obstacles within 5$m$ as the previous single-vehicle datasets such
as KITTI and nuScenes. Moreover, we define a novel matching criterion for
near-field 3D perception and prediction metrics. Based on RoboSense, we
formulate 6 popular tasks to facilitate the future development of related
research, where the detailed data analysis as well as benchmarks are also
provided accordingly. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
MODULI: Unlocking Preference Generalization via Diffusion Models for
Offline Multi-Objective Reinforcement Learning | http://arxiv.org/abs/2408.15501v1 | http://arxiv.org/abs/2408.15501v1 | http://arxiv.org/pdf/2408.15501v1 | 2024-08-28 | 2024-08-28 | [
"Yifu Yuan",
"Zhenrui Zheng",
"Zibin Dong",
"Jianye Hao"
] | [
"",
"",
"",
""
] | Multi-objective Reinforcement Learning (MORL) seeks to develop policies that
simultaneously optimize multiple conflicting objectives, but it requires
extensive online interactions. Offline MORL provides a promising solution by
training on pre-collected datasets to generalize to any preference upon
deployment. However, real-world offline datasets are often conservatively and
narrowly distributed, failing to comprehensively cover preferences, leading to
the emergence of out-of-distribution (OOD) preference areas. Existing offline
MORL algorithms exhibit poor generalization to OOD preferences, resulting in
policies that do not align with preferences. Leveraging the excellent
expressive and generalization capabilities of diffusion models, we propose
MODULI (Multi-objective Diffusion Planner with Sliding Guidance), which employs
a preference-conditioned diffusion model as a planner to generate trajectories
that align with various preferences and derive action for decision-making. To
achieve accurate generation, MODULI introduces two return normalization methods
under diverse preferences for refining guidance. To further enhance
generalization to OOD preferences, MODULI proposes a novel sliding guidance
mechanism, which involves training an additional slider adapter to capture the
direction of preference changes. Incorporating the slider, it transitions from
in-distribution (ID) preferences to generating OOD preferences, patching, and
extending the incomplete Pareto front. Extensive experiments on the D4MORL
benchmark demonstrate that our algorithm outperforms state-of-the-art Offline
MORL baselines, exhibiting excellent generalization to OOD preferences. | 23 pages, 7 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Deep Learning to Predict Late-Onset Breast Cancer Metastasis: the Single
Hyperparameter Grid Search (SHGS) Strategy for Meta Tuning Concerning Deep
Feed-forward Neural Network | http://arxiv.org/abs/2408.15498v1 | http://arxiv.org/abs/2408.15498v1 | http://arxiv.org/pdf/2408.15498v1 | 2024-08-28 | 2024-08-28 | [
"Yijun Zhou",
"Om Arora-Jain",
"Xia Jiang"
] | [
"",
"",
""
] | While machine learning has advanced in medicine, its widespread use in
clinical applications, especially in predicting breast cancer metastasis, is
still limited. We have been dedicated to constructing a DFNN model to predict
breast cancer metastasis n years in advance. However, the challenge lies in
efficiently identifying optimal hyperparameter values through grid search,
given the constraints of time and resources. Issues such as the infinite
possibilities for continuous hyperparameters like l1 and l2, as well as the
time-consuming and costly process, further complicate the task. To address
these challenges, we developed Single Hyperparameter Grid Search (SHGS)
strategy, serving as a preselection method before grid search. Our experiments
with SHGS applied to DFNN models for breast cancer metastasis prediction focus
on analyzing eight target hyperparameters: epochs, batch size, dropout, L1, L2,
learning rate, decay, and momentum. We created three figures, each depicting
the experiment results obtained from three LSM-I-10-Plus-year datasets. These
figures illustrate the relationship between model performance and the target
hyperparameter values. For each hyperparameter, we analyzed whether changes in
this hyperparameter would affect model performance, examined if there were
specific patterns, and explored how to choose values for the particular
hyperparameter. Our experimental findings reveal that the optimal value of a
hyperparameter is not only dependent on the dataset but is also significantly
influenced by the settings of other hyperparameters. Additionally, our
experiments suggested some reduced range of values for a target hyperparameter,
which may be helpful for low-budget grid search. This approach serves as a
prior experience and foundation for subsequent use of grid search to enhance
model performance. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.NE",
"q-bio.QM"
] |
|||
Remove Symmetries to Control Model Expressivity | http://arxiv.org/abs/2408.15495v1 | http://arxiv.org/abs/2408.15495v1 | http://arxiv.org/pdf/2408.15495v1 | 2024-08-28 | 2024-08-28 | [
"Liu Ziyin",
"Yizhou Xu",
"Isaac Chuang"
] | [
"",
"",
""
] | When symmetry is present in the loss function, the model is likely to be
trapped in a low-capacity state that is sometimes known as a "collapse." Being
trapped in these low-capacity states can be a major obstacle to training across
many scenarios where deep learning technology is applied. We first prove two
concrete mechanisms through which symmetries lead to reduced capacities and
ignored features during training. We then propose a simple and theoretically
justified algorithm, syre, to remove almost all symmetry-induced low-capacity
states in neural networks. The proposed method is shown to improve the training
of neural networks in scenarios when this type of entrapment is especially a
concern. A remarkable merit of the proposed method is that it is model-agnostic
and does not require any knowledge of the symmetry. | preprint | cs.LG | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
||
CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural
Networks | http://arxiv.org/abs/2408.15462v1 | http://arxiv.org/abs/2408.15462v1 | http://arxiv.org/pdf/2408.15462v1 | 2024-08-28 | 2024-08-28 | [
"Alejandro Mayorga",
"Alexander Yuan",
"Andrew Yuan",
"Tyler Wooldridge",
"Xiaodi Wang"
] | [
"",
"",
"",
"",
""
] | Neural networks have continued to gain prevalence in the modern era for their
ability to model complex data through pattern recognition and behavior
remodeling. However, the static construction of traditional neural networks
inhibits dynamic intelligence. This makes them inflexible to temporal changes
in data and unfit to capture complex dependencies. With the advent of quantum
technology, there has been significant progress in creating quantum algorithms.
In recent years, researchers have developed quantum neural networks that
leverage the capabilities of qubits to outperform classical networks. However,
their current formulation exhibits a static construction limiting the system's
dynamic intelligence. To address these weaknesses, we develop a Liquid Quantum
Neural Network (LQNet) and a Continuous Time Recurrent Quantum Neural Network
(CTRQNet). Both models demonstrate a significant improvement in accuracy
compared to existing quantum neural networks (QNNs), achieving accuracy
increases as high as 40\% on CIFAR 10 through binary classification. We propose
LQNets and CTRQNets might shine a light on quantum machine learning's black
box. | quant-ph | [
"quant-ph",
"cs.AI",
"cs.LG",
"cs.NE"
] |
|||
Pathfinding with Lazy Successor Generation | http://arxiv.org/abs/2408.15443v1 | http://arxiv.org/abs/2408.15443v1 | http://arxiv.org/pdf/2408.15443v1 | 2024-08-27 | 2024-08-27 | [
"Keisuke Okumura"
] | [
""
] | We study a pathfinding problem where only locations (i.e., vertices) are
given, and edges are implicitly defined by an oracle answering the connectivity
of two locations. Despite its simple structure, this problem becomes
non-trivial with a massive number of locations, due to posing a huge branching
factor for search algorithms. Limiting the number of successors, such as with
nearest neighbors, can reduce search efforts but compromises completeness.
Instead, we propose a novel LaCAS* algorithm, which does not generate
successors all at once but gradually generates successors as the search
progresses. This scheme is implemented with k-nearest neighbors search on a k-d
tree. LaCAS* is a complete and anytime algorithm that eventually converges to
the optima. Extensive evaluations demonstrate the efficacy of LaCAS*, e.g.,
solving complex pathfinding instances quickly, where conventional methods
falter. | 14 pages | cs.AI | [
"cs.AI",
"cs.RO"
] |
||
Online Event-Triggered Switching for Frequency Control in Power Grids
with Variable Inertia | http://arxiv.org/abs/2408.15436v1 | http://arxiv.org/abs/2408.15436v1 | http://arxiv.org/pdf/2408.15436v1 | 2024-08-27 | 2024-08-27 | [
"Jie Feng",
"Wenqi Cui",
"Jorge Cortés",
"Yuanyuan Shi"
] | [
"",
"",
"",
""
] | The increasing integration of renewable energy resources into power grids has
led to time-varying system inertia and consequent degradation in frequency
dynamics. A promising solution to alleviate performance degradation is using
power electronics interfaced energy resources, such as renewable generators and
battery energy storage for primary frequency control, by adjusting their power
output set-points in response to frequency deviations. However, designing a
frequency controller under time-varying inertia is challenging. Specifically,
the stability or optimality of controllers designed for time-invariant systems
can be compromised once applied to a time-varying system. We model the
frequency dynamics under time-varying inertia as a nonlinear switching system,
where the frequency dynamics under each mode are described by the nonlinear
swing equations and different modes represent different inertia levels. We
identify a key controller structure, named Neural Proportional-Integral
(Neural-PI) controller, that guarantees exponential input-to-state stability
for each mode. To further improve performance, we present an online
event-triggered switching algorithm to select the most suitable controller from
a set of Neural-PI controllers, each optimized for specific inertia levels.
Simulations on the IEEE 39-bus system validate the effectiveness of the
proposed online switching control method with stability guarantees and
optimized performance for frequency control under time-varying inertia. | eess.SY | [
"eess.SY",
"cs.AI",
"cs.SY"
] |
|||
Fast and Modular Autonomy Software for Autonomous Racing Vehicles | http://arxiv.org/abs/2408.15425v1 | http://arxiv.org/abs/2408.15425v1 | http://arxiv.org/pdf/2408.15425v1 | 2024-08-27 | 2024-08-27 | [
"Andrew Saba",
"Aderotimi Adetunji",
"Adam Johnson",
"Aadi Kothari",
"Matthew Sivaprakasam",
"Joshua Spisak",
"Prem Bharatia",
"Arjun Chauhan",
"Brendan Duff Jr.",
"Noah Gasparro",
"Charles King",
"Ryan Larkin",
"Brian Mao",
"Micah Nye",
"Anjali Parashar",
"Joseph Attias",
"Aurimas Balciunas",
"Austin Brown",
"Chris Chang",
"Ming Gao",
"Cindy Heredia",
"Andrew Keats",
"Jose Lavariega",
"William Muckelroy III",
"Andre Slavescu",
"Nickolas Stathas",
"Nayana Suvarna",
"Chuan Tian Zhang",
"Sebastian Scherer",
"Deva Ramanan"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Autonomous motorsports aim to replicate the human racecar driver with
software and sensors. As in traditional motorsports, Autonomous Racing Vehicles
(ARVs) are pushed to their handling limits in multi-agent scenarios at
extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD)
presents unique challenges across the autonomy stack. The Indy Autonomous
Challenge (IAC) is an international competition aiming to advance autonomous
vehicle development through ARV competitions. While far from challenging what a
human racecar driver can do, the IAC is pushing the state of the art by
facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW
Team's approach to autonomous racing in the IAC. In this work, we present our
modular and fast approach to agent detection, motion planning and controls to
create an autonomy stack. We also provide analysis of the performance of the
software stack in single and multi-agent scenarios for rapid deployment in a
fast-paced competition environment. We also cover what did and did not work
when deployed on a physical system the Dallara AV-21 platform and potential
improvements to address these shortcomings. Finally, we convey lessons learned
and discuss limitations and future directions for improvement. | Published in Journal of Field Robotics | Field Robotics Volume 4 (2024) 1-45 | 10.55417/fr.2024001 | cs.RO | [
"cs.RO",
"cs.AI",
"cs.SE"
] |
Simultaneous Training of First- and Second-Order Optimizers in
Population-Based Reinforcement Learning | http://arxiv.org/abs/2408.15421v1 | http://arxiv.org/abs/2408.15421v1 | http://arxiv.org/pdf/2408.15421v1 | 2024-08-27 | 2024-08-27 | [
"Felix Pfeiffer",
"Shahram Eivazi"
] | [
"",
""
] | The tuning of hyperparameters in reinforcement learning (RL) is critical, as
these parameters significantly impact an agent's performance and learning
efficiency. Dynamic adjustment of hyperparameters during the training process
can significantly enhance both the performance and stability of learning.
Population-based training (PBT) provides a method to achieve this by
continuously tuning hyperparameters throughout the training. This ongoing
adjustment enables models to adapt to different learning stages, resulting in
faster convergence and overall improved performance. In this paper, we propose
an enhancement to PBT by simultaneously utilizing both first- and second-order
optimizers within a single population. We conducted a series of experiments
using the TD3 algorithm across various MuJoCo environments. Our results, for
the first time, empirically demonstrate the potential of incorporating
second-order optimizers within PBT-based RL. Specifically, the combination of
the K-FAC optimizer with Adam led to up to a 10% improvement in overall
performance compared to PBT using only Adam. Additionally, in environments
where Adam occasionally fails, such as the Swimmer environment, the mixed
population with K-FAC exhibited more reliable learning outcomes, offering a
significant advantage in training stability without a substantial increase in
computational time. | 8 pages, 5 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Intertwined Biases Across Social Media Spheres: Unpacking Correlations
in Media Bias Dimensions | http://arxiv.org/abs/2408.15406v1 | http://arxiv.org/abs/2408.15406v1 | http://arxiv.org/pdf/2408.15406v1 | 2024-08-27 | 2024-08-27 | [
"Yifan Liu",
"Yike Li",
"Dong Wang"
] | [
"",
"",
""
] | Media bias significantly shapes public perception by reinforcing stereotypes
and exacerbating societal divisions. Prior research has often focused on
isolated media bias dimensions such as \textit{political bias} or
\textit{racial bias}, neglecting the complex interrelationships among various
bias dimensions across different topic domains. Moreover, we observe that
models trained on existing media bias benchmarks fail to generalize effectively
on recent social media posts, particularly in certain bias identification
tasks. This shortfall primarily arises because these benchmarks do not
adequately reflect the rapidly evolving nature of social media content, which
is characterized by shifting user behaviors and emerging trends. In response to
these limitations, our research introduces a novel dataset collected from
YouTube and Reddit over the past five years. Our dataset includes automated
annotations for YouTube content across a broad spectrum of bias dimensions,
such as gender, racial, and political biases, as well as hate speech, among
others. It spans diverse domains including politics, sports, healthcare,
education, and entertainment, reflecting the complex interplay of biases across
different societal sectors. Through comprehensive statistical analysis, we
identify significant differences in bias expression patterns and intra-domain
bias correlations across these domains. By utilizing our understanding of the
correlations among various bias dimensions, we lay the groundwork for creating
advanced systems capable of detecting multiple biases simultaneously. Overall,
our dataset advances the field of media bias identification, contributing to
the development of tools that promote fairer media consumption. The
comprehensive awareness of existing media bias fosters more ethical journalism,
promotes cultural sensitivity, and supports a more informed and equitable
public discourse. | Accepted to ASONAM 2024 | cs.SI | [
"cs.SI",
"cs.AI",
"cs.CL",
"I.2.7"
] |
||
A Statistical Framework for Data-dependent Retrieval-Augmented Models | http://arxiv.org/abs/2408.15399v1 | http://arxiv.org/abs/2408.15399v1 | http://arxiv.org/pdf/2408.15399v1 | 2024-08-27 | 2024-08-27 | [
"Soumya Basu",
"Ankit Singh Rawat",
"Manzil Zaheer"
] | [
"",
"",
""
] | Modern ML systems increasingly augment input instances with additional
relevant information to enhance final prediction. Despite growing interest in
such retrieval-augmented models, their fundamental properties and training are
not well understood. We propose a statistical framework to study such models
with two components: 1) a {\em retriever} to identify the relevant information
out of a large corpus via a data-dependent metric; and 2) a {\em predictor}
that consumes the input instances along with the retrieved information to make
the final predictions. We present a principled method for end-to-end training
of both components and draw connections with various training approaches in the
literature. Furthermore, we establish excess risk bounds for
retrieval-augmented models while delineating the contributions of both
retriever and predictor towards the model performance. We validate the utility
of our proposed training methods along with the key takeaways from our
statistical analysis on open domain question answering task where retrieval
augmentation is important. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
|||
SCAN-Edge: Finding MobileNet-speed Hybrid Networks for Diverse Edge
Devices via Hardware-Aware Evolutionary Search | http://arxiv.org/abs/2408.15395v1 | http://arxiv.org/abs/2408.15395v1 | http://arxiv.org/pdf/2408.15395v1 | 2024-08-27 | 2024-08-27 | [
"Hung-Yueh Chiang",
"Diana Marculescu"
] | [
"",
""
] | Designing low-latency and high-efficiency hybrid networks for a variety of
low-cost commodity edge devices is both costly and tedious, leading to the
adoption of hardware-aware neural architecture search (NAS) for finding optimal
architectures. However, unifying NAS for a wide range of edge devices presents
challenges due to the variety of hardware designs, supported operations, and
compilation optimizations. Existing methods often fix the search space of
architecture choices (e.g., activation, convolution, or self-attention) and
estimate latency using hardware-agnostic proxies (e.g., FLOPs), which fail to
achieve proclaimed latency across various edge devices. To address this issue,
we propose SCAN-Edge, a unified NAS framework that jointly searches for
self-attention, convolution, and activation to accommodate the wide variety of
edge devices, including CPU-, GPU-, and hardware accelerator-based systems. To
handle the large search space, SCAN-Edge relies on with a hardware-aware
evolutionary algorithm that improves the quality of the search space to
accelerate the sampling process. Experiments on large-scale datasets
demonstrate that our hybrid networks match the actual MobileNetV2 latency for
224x224 input resolution on various commodity edge devices. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
On Stateful Value Factorization in Multi-Agent Reinforcement Learning | http://arxiv.org/abs/2408.15381v1 | http://arxiv.org/abs/2408.15381v1 | http://arxiv.org/pdf/2408.15381v1 | 2024-08-27 | 2024-08-27 | [
"Enrico Marchesini",
"Andrea Baisero",
"Rupali Bathi",
"Christopher Amato"
] | [
"",
"",
"",
""
] | Value factorization is a popular paradigm for designing scalable multi-agent
reinforcement learning algorithms. However, current factorization methods make
choices without full justification that may limit their performance. For
example, the theory in prior work uses stateless (i.e., history) functions,
while the practical implementations use state information -- making the
motivating theory a mismatch for the implementation. Also, methods have built
off of previous approaches, inheriting their architectures without exploring
other, potentially better ones. To address these concerns, we formally analyze
the theory of using the state instead of the history in current methods --
reconnecting theory and practice. We then introduce DuelMIX, a factorization
algorithm that learns distinct per-agent utility estimators to improve
performance and achieve full expressiveness. Experiments on StarCraft II
micromanagement and Box Pushing tasks demonstrate the benefits of our
intuitions. | 22 pages, 9 figures, 4 tables | cs.AI | [
"cs.AI"
] |
||
Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing | http://arxiv.org/abs/2408.16027v1 | http://arxiv.org/abs/2408.16027v1 | http://arxiv.org/pdf/2408.16027v1 | 2024-08-27 | 2024-08-27 | [
"Ziyu Sun",
"Haoyang Su",
"Hanqi Sun",
"En Wang",
"Wenbin Liu"
] | [
"",
"",
"",
"",
""
] | Mobile Crowd Sensing (MCS) is a promising paradigm that leverages mobile
users and their smart portable devices to perform various real-world tasks.
However, due to budget constraints and the inaccessibility of certain areas,
Sparse MCS has emerged as a more practical alternative, collecting data from a
limited number of target subareas and utilizing inference algorithms to
complete the full sensing map. While existing approaches typically assume a
time-discrete setting with data remaining constant within each sensing cycle,
this simplification can introduce significant errors, especially when dealing
with long cycles, as real-world sensing data often changes continuously. In
this paper, we go from fine-grained completion, i.e., the subdivision of
sensing cycles into minimal time units, towards a more accurate,
time-continuous completion. We first introduce Deep Matrix Factorization (DMF)
as a neural network-enabled framework and enhance it with a Recurrent Neural
Network (RNN-DMF) to capture temporal correlations in these finer time slices.
To further deal with the continuous data, we propose TIME-DMF, which captures
temporal information across unequal intervals, enabling time-continuous
completion. Additionally, we present the Query-Generate (Q-G) strategy within
TIME-DMF to model the infinite states of continuous data. Extensive experiments
across five types of sensing tasks demonstrate the effectiveness of our models
and the advantages of time-continuous completion. | 11 pages, 11 figures | cs.LG | [
"cs.LG",
"cs.AI",
"cs.NI"
] |
||
Handling Geometric Domain Shifts in Semantic Segmentation of Surgical
RGB and Hyperspectral Images | http://arxiv.org/abs/2408.15373v1 | http://arxiv.org/abs/2408.15373v1 | http://arxiv.org/pdf/2408.15373v1 | 2024-08-27 | 2024-08-27 | [
"Silvia Seidlitz",
"Jan Sellner",
"Alexander Studier-Fischer",
"Alessandro Motta",
"Berkin Özdemir",
"Beat P. Müller-Stich",
"Felix Nickel",
"Lena Maier-Hein"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Robust semantic segmentation of intraoperative image data holds promise for
enabling automatic surgical scene understanding and autonomous robotic surgery.
While model development and validation are primarily conducted on idealistic
scenes, geometric domain shifts, such as occlusions of the situs, are common in
real-world open surgeries. To close this gap, we (1) present the first analysis
of state-of-the-art (SOA) semantic segmentation models when faced with
geometric out-of-distribution (OOD) data, and (2) propose an augmentation
technique called "Organ Transplantation", to enhance generalizability. Our
comprehensive validation on six different OOD datasets, comprising 600 RGB and
hyperspectral imaging (HSI) cubes from 33 pigs, each annotated with 19 classes,
reveals a large performance drop in SOA organ segmentation models on geometric
OOD data. This performance decline is observed not only in conventional RGB
data (with a dice similarity coefficient (DSC) drop of 46 %) but also in HSI
data (with a DSC drop of 45 %), despite the richer spectral information
content. The performance decline increases with the spatial granularity of the
input data. Our augmentation technique improves SOA model performance by up to
67 % for RGB data and 90 % for HSI data, achieving performance at the level of
in-distribution performance on real OOD test data. Given the simplicity and
effectiveness of our augmentation method, it is a valuable tool for addressing
geometric domain shifts in surgical scene segmentation, regardless of the
underlying model. Our code and pre-trained models are publicly available at
https://github.com/IMSY-DKFZ/htc. | Silvia Seidlitz and Jan Sellner contributed equally | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
||
What Is Required for Empathic AI? It Depends, and Why That Matters for
AI Developers and Users | http://arxiv.org/abs/2408.15354v1 | http://arxiv.org/abs/2408.15354v1 | http://arxiv.org/pdf/2408.15354v1 | 2024-08-27 | 2024-08-27 | [
"Jana Schaich Borg",
"Hannah Read"
] | [
"",
""
] | Interest is growing in artificial empathy, but so is confusion about what
artificial empathy is or needs to be. This confusion makes it challenging to
navigate the technical and ethical issues that accompany empathic AI
development. Here, we outline a framework for thinking about empathic AI based
on the premise that different constellations of capabilities associated with
empathy are important for different empathic AI applications. We describe
distinctions of capabilities that we argue belong under the empathy umbrella,
and show how three medical empathic AI use cases require different sets of
these capabilities. We conclude by discussing why appreciation of the diverse
capabilities under the empathy umbrella is important for both AI creators and
users. | To appear at the 7th AAAI/ACM Conference on AI, Ethics, and Society,
2024 | cs.AI | [
"cs.AI",
"cs.CY",
"cs.HC"
] |
||
What makes math problems hard for reinforcement learning: a case study | http://arxiv.org/abs/2408.15332v1 | http://arxiv.org/abs/2408.15332v1 | http://arxiv.org/pdf/2408.15332v1 | 2024-08-27 | 2024-08-27 | [
"Ali Shehper",
"Anibal M. Medina-Mardones",
"Bartłomiej Lewandowski",
"Angus Gruen",
"Piotr Kucharski",
"Sergei Gukov"
] | [
"",
"",
"",
"",
"",
""
] | Using a long-standing conjecture from combinatorial group theory, we explore,
from multiple angles, the challenges of finding rare instances carrying
disproportionately high rewards. Based on lessons learned in the mathematical
context defined by the Andrews-Curtis conjecture, we propose algorithmic
improvements that can be relevant in other domains with ultra-sparse reward
problems. Although our case study can be formulated as a game, its shortest
winning sequences are potentially $10^6$ or $10^9$ times longer than those
encountered in chess. In the process of our study, we demonstrate that one of
the potential counterexamples due to Akbulut and Kirby, whose status escaped
direct mathematical methods for 39 years, is stably AC-trivial. | 39 pages, 18 figures, 1 table | cs.LG | [
"cs.LG",
"cs.AI",
"math.CO",
"math.GR",
"math.GT"
] |
||
The Mamba in the Llama: Distilling and Accelerating Hybrid Models | http://arxiv.org/abs/2408.15237v1 | http://arxiv.org/abs/2408.15237v1 | http://arxiv.org/pdf/2408.15237v1 | 2024-08-27 | 2024-08-27 | [
"Junxiong Wang",
"Daniele Paliotta",
"Avner May",
"Alexander M. Rush",
"Tri Dao"
] | [
"",
"",
"",
"",
""
] | Linear RNN architectures, like Mamba, can be competitive with Transformer
models in language modeling while having advantageous deployment
characteristics. Given the focus on training large-scale Transformer models, we
consider the challenge of converting these pretrained models for deployment. We
demonstrate that it is feasible to distill large Transformers into linear RNNs
by reusing the linear projection weights from attention layers with academic
GPU resources. The resulting hybrid model, which incorporates a quarter of the
attention layers, achieves performance comparable to the original Transformer
in chat benchmarks and outperforms open-source hybrid Mamba models trained from
scratch with trillions of tokens in both chat benchmarks and general
benchmarks. Moreover, we introduce a hardware-aware speculative decoding
algorithm that accelerates the inference speed of Mamba and hybrid models.
Overall we show how, with limited computation resources, we can remove many of
the original attention layers and generate from the resulting model more
efficiently. Our top-performing model, distilled from Llama3-8B-Instruct,
achieves a 29.61 length-controlled win rate on AlpacaEval 2 against GPT-4 and
7.35 on MT-Bench, surpassing the best instruction-tuned linear RNN model. | Code is open-sourced at https://github.com/jxiw/MambaInLlama | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Into the Unknown Unknowns: Engaged Human Learning through Participation
in Language Model Agent Conversations | http://arxiv.org/abs/2408.15232v1 | http://arxiv.org/abs/2408.15232v1 | http://arxiv.org/pdf/2408.15232v1 | 2024-08-27 | 2024-08-27 | [
"Yucheng Jiang",
"Yijia Shao",
"Dekun Ma",
"Sina J. Semnani",
"Monica S. Lam"
] | [
"",
"",
"",
"",
""
] | While language model (LM)-powered chatbots and generative search engines
excel at answering concrete queries, discovering information in the terrain of
unknown unknowns remains challenging for users. To emulate the common
educational scenario where children/students learn by listening to and
participating in conversations of their parents/teachers, we create
Collaborative STORM (Co-STORM). Unlike QA systems that require users to ask all
the questions, Co-STORM lets users observe and occasionally steer the discourse
among several LM agents. The agents ask questions on the user's behalf,
allowing the user to discover unknown unknowns serendipitously. To facilitate
user interaction, Co-STORM assists users in tracking the discourse by
organizing the uncovered information into a dynamic mind map, ultimately
generating a comprehensive report as takeaways. For automatic evaluation, we
construct the WildSeek dataset by collecting real information-seeking records
with user goals. Co-STORM outperforms baseline methods on both discourse trace
and report quality. In a further human evaluation, 70% of participants prefer
Co-STORM over a search engine, and 78% favor it over a RAG chatbot. | cs.CL | [
"cs.CL",
"cs.AI",
"cs.IR",
"I.2.7; H.5.2; H.3.3"
] |
|||
Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in
Language Models | http://arxiv.org/abs/2408.15313v1 | http://arxiv.org/abs/2408.15313v1 | http://arxiv.org/pdf/2408.15313v1 | 2024-08-27 | 2024-08-27 | [
"Wenxuan Zhang",
"Philip H. S. Torr",
"Mohamed Elhoseiny",
"Adel Bibi"
] | [
"",
"",
"",
""
] | Fine-tuning large language models (LLMs) on human preferences, typically
through reinforcement learning from human feedback (RLHF), has proven
successful in enhancing their capabilities. However, ensuring the safety of
LLMs during the fine-tuning remains a critical concern, and mitigating the
potential conflicts in safety and helpfulness is costly in RLHF. To address
this issue, we propose a supervised learning framework called Bi-Factorial
Preference Optimization (BFPO), which re-parameterizes a joint RLHF objective
of both safety and helpfulness into a single supervised learning objective. In
the supervised optimization, a labeling function is used to capture global
preferences ranking to balance both safety and helpfulness. To evaluate BFPO,
we develop a benchmark including comprehensive discriminative and generative
tasks for helpfulness and harmlessness. The results indicate that our method
significantly outperforms existing approaches in both safety and helpfulness.
Moreover, BFPO eliminates the need for human prompting and annotation in LLM
fine-tuning while achieving the same level of safety as methods that heavily
rely on human labor, with less than 10% of the computational resources. The
training recipes and models will be released. | cs.AI | [
"cs.AI",
"cs.CL",
"cs.LG"
] |
|||
Fundus2Video: Cross-Modal Angiography Video Generation from Static
Fundus Photography with Clinical Knowledge Guidance | http://arxiv.org/abs/2408.15217v1 | http://arxiv.org/abs/2408.15217v1 | http://arxiv.org/pdf/2408.15217v1 | 2024-08-27 | 2024-08-27 | [
"Weiyi Zhang",
"Siyu Huang",
"Jiancheng Yang",
"Ruoyu Chen",
"Zongyuan Ge",
"Yingfeng Zheng",
"Danli Shi",
"Mingguang He"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Fundus Fluorescein Angiography (FFA) is a critical tool for assessing retinal
vascular dynamics and aiding in the diagnosis of eye diseases. However, its
invasive nature and less accessibility compared to Color Fundus (CF) images
pose significant challenges. Current CF to FFA translation methods are limited
to static generation. In this work, we pioneer dynamic FFA video generation
from static CF images. We introduce an autoregressive GAN for smooth,
memory-saving frame-by-frame FFA synthesis. To enhance the focus on dynamic
lesion changes in FFA regions, we design a knowledge mask based on clinical
experience. Leveraging this mask, our approach integrates innovative knowledge
mask-guided techniques, including knowledge-boosted attention, knowledge-aware
discriminators, and mask-enhanced patchNCE loss, aimed at refining generation
in critical areas and addressing the pixel misalignment challenge. Our method
achieves the best FVD of 1503.21 and PSNR of 11.81 compared to other common
video generation approaches. Human assessment by an ophthalmologist confirms
its high generation quality. Notably, our knowledge mask surpasses supervised
lesion segmentation masks, offering a promising non-invasive alternative to
traditional FFA for research and clinical applications. The code is available
at https://github.com/Michi-3000/Fundus2Video. | The paper has been accepted by Medical Image Computing and Computer
Assisted Intervention Society (MICCAI) 2024 | eess.IV | [
"eess.IV",
"cs.AI",
"cs.CV"
] |
||
Can Unconfident LLM Annotations Be Used for Confident Conclusions? | http://arxiv.org/abs/2408.15204v1 | http://arxiv.org/abs/2408.15204v1 | http://arxiv.org/pdf/2408.15204v1 | 2024-08-27 | 2024-08-27 | [
"Kristina Gligorić",
"Tijana Zrnic",
"Cinoo Lee",
"Emmanuel J. Candès",
"Dan Jurafsky"
] | [
"",
"",
"",
"",
""
] | Large language models (LLMs) have shown high agreement with human raters
across a variety of tasks, demonstrating potential to ease the challenges of
human data collection. In computational social science (CSS), researchers are
increasingly leveraging LLM annotations to complement slow and expensive human
annotations. Still, guidelines for collecting and using LLM annotations,
without compromising the validity of downstream conclusions, remain limited. We
introduce Confidence-Driven Inference: a method that combines LLM annotations
and LLM confidence indicators to strategically select which human annotations
should be collected, with the goal of producing accurate statistical estimates
and provably valid confidence intervals while reducing the number of human
annotations needed. Our approach comes with safeguards against LLM annotations
of poor quality, guaranteeing that the conclusions will be both valid and no
less accurate than if we only relied on human annotations. We demonstrate the
effectiveness of Confidence-Driven Inference over baselines in statistical
estimation tasks across three CSS settings--text politeness, stance, and
bias--reducing the needed number of human annotations by over 25% in each.
Although we use CSS settings for demonstration, Confidence-Driven Inference can
be used to estimate most standard quantities across a broad range of NLP
problems. | cs.CL | [
"cs.CL",
"cs.AI",
"cs.HC"
] |
|||
Automatic 8-tissue Segmentation for 6-month Infant Brains | http://arxiv.org/abs/2408.15198v1 | http://arxiv.org/abs/2408.15198v1 | http://arxiv.org/pdf/2408.15198v1 | 2024-08-27 | 2024-08-27 | [
"Yilan Dong",
"Vanessa Kyriakopoulou",
"Irina Grigorescu",
"Grainne McAlonan",
"Dafnis Batalle",
"Maria Deprez"
] | [
"",
"",
"",
"",
"",
""
] | Numerous studies have highlighted that atypical brain development,
particularly during infancy and toddlerhood, is linked to an increased
likelihood of being diagnosed with a neurodevelopmental condition, such as
autism. Accurate brain tissue segmentations for morphological analysis are
essential in numerous infant studies. However, due to ongoing white matter (WM)
myelination changing tissue contrast in T1- and T2-weighted images, automatic
tissue segmentation in 6-month infants is particularly difficult. On the other
hand, manual labelling by experts is time-consuming and labor-intensive. In
this study, we propose the first 8-tissue segmentation pipeline for
six-month-old infant brains. This pipeline utilizes domain adaptation (DA)
techniques to leverage our longitudinal data, including neonatal images
segmented with the neonatal Developing Human Connectome Project structural
pipeline. Our pipeline takes raw 6-month images as inputs and generates the
8-tissue segmentation as outputs, forming an end-to-end segmentation pipeline.
The segmented tissues include WM, gray matter (GM), cerebrospinal fluid (CSF),
ventricles, cerebellum, basal ganglia, brainstem, and hippocampus/amygdala.
Cycle-Consistent Generative Adversarial Network (CycleGAN) and Attention U-Net
were employed to achieve the image contrast transformation between neonatal and
6-month images and perform tissue segmentation on the synthesized 6-month
images (neonatal images with 6-month intensity contrast), respectively.
Moreover, we incorporated the segmentation outputs from Infant Brain Extraction
and Analysis Toolbox (iBEAT) and another Attention U-Net to further enhance the
performance and construct the end-to-end segmentation pipeline. Our evaluation
with real 6-month images achieved a DICE score of 0.92, an HD95 of 1.6, and an
ASSD of 0.42. | 11 pages, 4 figures, to be published in MICCAI PIPPI workshop | eess.IV | [
"eess.IV",
"cs.AI",
"cs.LG"
] |
||
PoseWatch: A Transformer-based Architecture for Human-centric Video
Anomaly Detection Using Spatio-temporal Pose Tokenization | http://arxiv.org/abs/2408.15185v1 | http://arxiv.org/abs/2408.15185v1 | http://arxiv.org/pdf/2408.15185v1 | 2024-08-27 | 2024-08-27 | [
"Ghazal Alinezhad Noghre",
"Armin Danesh Pazho",
"Hamed Tabkhi"
] | [
"",
"",
""
] | Video Anomaly Detection (VAD) presents a significant challenge in computer
vision, particularly due to the unpredictable and infrequent nature of
anomalous events, coupled with the diverse and dynamic environments in which
they occur. Human-centric VAD, a specialized area within this domain, faces
additional complexities, including variations in human behavior, potential
biases in data, and substantial privacy concerns related to human subjects.
These issues complicate the development of models that are both robust and
generalizable. To address these challenges, recent advancements have focused on
pose-based VAD, which leverages human pose as a high-level feature to mitigate
privacy concerns, reduce appearance biases, and minimize background
interference. In this paper, we introduce PoseWatch, a novel transformer-based
architecture designed specifically for human-centric pose-based VAD. PoseWatch
features an innovative Spatio-Temporal Pose and Relative Pose (ST-PRP)
tokenization method that enhances the representation of human motion over time,
which is also beneficial for broader human behavior analysis tasks. The
architecture's core, a Unified Encoder Twin Decoders (UETD) transformer,
significantly improves the detection of anomalous behaviors in video data.
Extensive evaluations across multiple benchmark datasets demonstrate that
PoseWatch consistently outperforms existing methods, establishing a new
state-of-the-art in pose-based VAD. This work not only demonstrates the
efficacy of PoseWatch but also highlights the potential of integrating Natural
Language Processing techniques with computer vision to advance human behavior
analysis. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language
Instruction Tuning for Semiconductor Electron Micrograph Analysis | http://arxiv.org/abs/2408.15305v1 | http://arxiv.org/abs/2408.15305v1 | http://arxiv.org/pdf/2408.15305v1 | 2024-08-27 | 2024-08-27 | [
"Sakhinana Sagar Srinivas",
"Chidaksh Ravuru",
"Geethan Sannidhi",
"Venkataramana Runkana"
] | [
"",
"",
"",
""
] | Semiconductors, crucial to modern electronics, are generally under-researched
in foundational models. It highlights the need for research to enhance the
semiconductor device technology portfolio and aid in high-end device
fabrication. In this paper, we introduce sLAVA, a small-scale vision-language
assistant tailored for semiconductor manufacturing, with a focus on electron
microscopy image analysis. It addresses challenges of data scarcity and
acquiring high-quality, expert-annotated data. We employ a teacher-student
paradigm, using a foundational vision language model like GPT-4 as a teacher to
create instruction-following multimodal data for customizing the student model,
sLAVA, for electron microscopic image analysis tasks on consumer hardware with
limited budgets. Our approach allows enterprises to further fine-tune the
proposed framework with their proprietary data securely within their own
infrastructure, protecting intellectual property. Rigorous experiments validate
that our framework surpasses traditional methods, handles data shifts, and
enables high-throughput screening. | Paper published at ICML 2024 Workshop on Foundation Models in the
Wild | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
||
TCNFormer: Temporal Convolutional Network Former for Short-Term Wind
Speed Forecasting | http://arxiv.org/abs/2408.15737v1 | http://arxiv.org/abs/2408.15737v1 | http://arxiv.org/pdf/2408.15737v1 | 2024-08-27 | 2024-08-27 | [
"Abid Hasan Zim",
"Aquib Iqbal",
"Asad Malik",
"Zhicheng Dong",
"Hanzhou Wu"
] | [
"",
"",
"",
"",
""
] | Global environmental challenges and rising energy demands have led to
extensive exploration of wind energy technologies. Accurate wind speed
forecasting (WSF) is crucial for optimizing wind energy capture and ensuring
system stability. However, predicting wind speed remains challenging due to its
inherent randomness, fluctuation, and unpredictability. This study proposes the
Temporal Convolutional Network Former (TCNFormer) for short-term (12-hour) wind
speed forecasting. The TCNFormer integrates the Temporal Convolutional Network
(TCN) and transformer encoder to capture the spatio-temporal features of wind
speed. The transformer encoder consists of two distinct attention mechanisms:
causal temporal multi-head self-attention (CT-MSA) and temporal external
attention (TEA). CT-MSA ensures that the output of a step derives only from
previous steps, i.e., causality. Locality is also introduced to improve
efficiency. TEA explores potential relationships between different sample
sequences in wind speed data. This study utilizes wind speed data from the NASA
Prediction of Worldwide Energy Resources (NASA POWER) of Patenga Sea Beach,
Chittagong, Bangladesh (latitude 22.2352{\deg} N, longitude 91.7914{\deg} E)
over a year (six seasons). The findings indicate that the TCNFormer outperforms
state-of-the-art models in prediction accuracy. The proposed TCNFormer presents
a promising method for spatio-temporal WSF and may achieve desirable
performance in real-world applications of wind power systems. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Evaluating the Energy Consumption of Machine Learning: Systematic
Literature Review and Experiments | http://arxiv.org/abs/2408.15128v1 | http://arxiv.org/abs/2408.15128v1 | http://arxiv.org/pdf/2408.15128v1 | 2024-08-27 | 2024-08-27 | [
"Charlotte Rodriguez",
"Laura Degioanni",
"Laetitia Kameni",
"Richard Vidal",
"Giovanni Neglia"
] | [
"",
"",
"",
"",
""
] | Monitoring, understanding, and optimizing the energy consumption of Machine
Learning (ML) are various reasons why it is necessary to evaluate the energy
usage of ML. However, there exists no universal tool that can answer this
question for all use cases, and there may even be disagreement on how to
evaluate energy consumption for a specific use case. Tools and methods are
based on different approaches, each with their own advantages and drawbacks,
and they need to be mapped out and explained in order to select the most
suitable one for a given situation. We address this challenge through two
approaches. First, we conduct a systematic literature review of all tools and
methods that permit to evaluate the energy consumption of ML (both at training
and at inference), irrespective of whether they were originally designed for
machine learning or general software. Second, we develop and use an
experimental protocol to compare a selection of these tools and methods. The
comparison is both qualitative and quantitative on a range of ML tasks of
different nature (vision, language) and computational complexity. The
systematic literature review serves as a comprehensive guide for understanding
the array of tools and methods used in evaluating energy consumption of ML, for
various use cases going from basic energy monitoring to consumption
optimization. Two open-source repositories are provided for further
exploration. The first one contains tools that can be used to replicate this
work or extend the current review. The second repository houses the
experimental protocol, allowing users to augment the protocol with new ML
computing tasks and additional energy evaluation tools. | 52 pages, | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CY",
"I.2"
] |
||
The Uniqueness of LLaMA3-70B with Per-Channel Quantization: An Empirical
Study | http://arxiv.org/abs/2408.15301v1 | http://arxiv.org/abs/2408.15301v1 | http://arxiv.org/pdf/2408.15301v1 | 2024-08-27 | 2024-08-27 | [
"Minghai Qin"
] | [
""
] | We have observed a distinctive quantization-related behavior in the
LLaMA3/3.1-70B models that is absent in both the LLaMA2-70B and
LLaMA3/3.1-8B/405B models. Quantization is a crucial technique for deploying
large language models (LLMs) efficiently. Among various bit widths and
representations for weights and activations, the 8-bit integer weight and 8-bit
integer activation (W8A8) configuration is particularly popular due to its
widespread hardware support. However, the impact of W8A8 post-training
quantization on model accuracy remains contentious. While several studies have
suggested calibrating either weights or activations to mitigate accuracy
degradation, a comprehensive solution has yet to be identified. In this paper,
we empirically investigate multiple LLMs featured on an open LLM leaderboard,
discovering that the LLaMA3-70B model series have a unique accuracy degradation
behavior with W8A8 per-channel post-training quantization. In contrast, other
model series such as LLaMA2, LLaMA3-8B, Qwen, Mixtral, Mistral, Phi-3, and
Falcon demonstrate robust performance with W8A8, sometimes surpassing their
FP16 counterparts. Contrary to previous assertions attributing degradation to
the large dynamic range of activations, our findings indicate that the weight
distribution of the LLaMA3-70B is the primary factor behind the vulnerability.
By meticulously analyzing the distinct characteristics of weight distributions
across Transformer blocks, we propose a mixed strategy with less than 3% of the
layers enabling finer W8A8 quantization granularity, while the remaining 97% of
layers retain the per-channel configuration. As a result, the average accuracy
of LLaMA3-70B-W8A8 is increased from 45.5% to 73.4% (just 0.7% shy of
LLaMA3-70B-FP16) across eight reasoning tasks. Notably, our method requires
neither calibration nor fine-tuning. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Aligning XAI with EU Regulations for Smart Biomedical Devices: A
Methodology for Compliance Analysis | http://arxiv.org/abs/2408.15121v1 | http://arxiv.org/abs/2408.15121v1 | http://arxiv.org/pdf/2408.15121v1 | 2024-08-27 | 2024-08-27 | [
"Francesco Sovrano",
"Michael Lognoul",
"Giulia Vilone"
] | [
"",
"",
""
] | Significant investment and development have gone into integrating Artificial
Intelligence (AI) in medical and healthcare applications, leading to advanced
control systems in medical technology. However, the opacity of AI systems
raises concerns about essential characteristics needed in such sensitive
applications, like transparency and trustworthiness. Our study addresses these
concerns by investigating a process for selecting the most adequate Explainable
AI (XAI) methods to comply with the explanation requirements of key EU
regulations in the context of smart bioelectronics for medical devices. The
adopted methodology starts with categorising smart devices by their control
mechanisms (open-loop, closed-loop, and semi-closed-loop systems) and delving
into their technology. Then, we analyse these regulations to define their
explainability requirements for the various devices and related goals.
Simultaneously, we classify XAI methods by their explanatory objectives. This
allows for matching legal explainability requirements with XAI explanatory
goals and determining the suitable XAI algorithms for achieving them. Our
findings provide a nuanced understanding of which XAI algorithms align better
with EU regulations for different types of medical devices. We demonstrate this
through practical case studies on different neural implants, from chronic
disease management to advanced prosthetics. This study fills a crucial gap in
aligning XAI applications in bioelectronics with stringent provisions of EU
regulations. It provides a practical framework for developers and researchers,
ensuring their AI innovations advance healthcare technology and adhere to legal
and ethical standards. | Accepted for publication at ECAI 2024, main-track | cs.AI | [
"cs.AI",
"cs.CY"
] |
||
Urdu Digital Text Word Optical Character Recognition Using Permuted Auto
Regressive Sequence Modeling | http://arxiv.org/abs/2408.15119v2 | http://arxiv.org/abs/2408.15119v2 | http://arxiv.org/pdf/2408.15119v2 | 2024-08-27 | 2024-08-28 | [
"Ahmed Mustafa",
"Muhammad Tahir Rafique",
"Muhammad Ijlal Baig",
"Hasan Sajid",
"Muhammad Jawad Khan",
"Karam Dad Kallu"
] | [
"",
"",
"",
"",
"",
""
] | This research paper presents a novel word-level Optical Character Recognition
(OCR) model developed specifically for digital Urdu text. The model utilizes
transformer-based architectures and attention mechanisms to address the unique
challenges of recognizing Urdu script, which includes handling a diverse range
of text styles, fonts, and variations. Trained on a comprehensive dataset of
approximately 160,000 Urdu text images, the model incorporates a permuted
autoregressive sequence (PARSeq) architecture. This design enables
context-aware inference and iterative refinement by leveraging bidirectional
context information, significantly enhancing its ability to accurately
recognize Urdu characters. The model achieves a character error rate (CER) of
0.178, highlighting its effectiveness and precision in real-world applications.
However, the model has some limitations, such as difficulties with blurred
images, non-horizontal orientations, and the presence of trailing punctuation
marks, which can introduce noise into the recognition process. Addressing these
challenges will be a key focus of future work. Future research will aim to
further refine the model through advanced data augmentation techniques,
optimization of hyperparameters, and the integration of context-aware language
models, ultimately enhancing the model's performance and robustness in Urdu
text recognition. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Evaluating Stability of Unreflective Alignment | http://arxiv.org/abs/2408.15116v1 | http://arxiv.org/abs/2408.15116v1 | http://arxiv.org/pdf/2408.15116v1 | 2024-08-27 | 2024-08-27 | [
"James Lucassen",
"Mark Henry",
"Philippa Wright",
"Owen Yeung"
] | [
"",
"",
"",
""
] | Many theoretical obstacles to AI alignment are consequences of reflective
stability - the problem of designing alignment mechanisms that the AI would not
disable if given the option. However, problems stemming from reflective
stability are not obviously present in current LLMs, leading to disagreement
over whether they will need to be solved to enable safe delegation of cognitive
labor. In this paper, we propose Counterfactual Priority Change (CPC)
destabilization as a mechanism by which reflective stability problems may arise
in future LLMs. We describe two risk factors for CPC-destabilization: 1)
CPC-based stepping back and 2) preference instability. We develop preliminary
evaluations for each of these risk factors, and apply them to frontier LLMs.
Our findings indicate that in current LLMs, increased scale and capability are
associated with increases in both CPC-based stepping back and preference
instability, suggesting that CPC-destabilization may cause reflective stability
problems in future LLMs. | cs.AI | [
"cs.AI"
] |
|||
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with
Spatial Adversaries | http://arxiv.org/abs/2408.15114v1 | http://arxiv.org/abs/2408.15114v1 | http://arxiv.org/pdf/2408.15114v1 | 2024-08-27 | 2024-08-27 | [
"Amine Ouasfi",
"Adnane Boukhayma"
] | [
"",
""
] | Implicit Neural Representations have gained prominence as a powerful
framework for capturing complex data modalities, encompassing a wide range from
3D shapes to images and audio. Within the realm of 3D shape representation,
Neural Signed Distance Functions (SDF) have demonstrated remarkable potential
in faithfully encoding intricate shape geometry. However, learning SDFs from
sparse 3D point clouds in the absence of ground truth supervision remains a
very challenging task. While recent methods rely on smoothness priors to
regularize the learning, our method introduces a regularization term that
leverages adversarial samples around the shape to improve the learned SDFs.
Through extensive experiments and evaluations, we illustrate the efficacy of
our proposed method, highlighting its capacity to improve SDF learning with
respect to baselines and the state-of-the-art using synthetic and real data. | ICML 2024 | cs.CV | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] |
||
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs | http://arxiv.org/abs/2408.15300v1 | http://arxiv.org/abs/2408.15300v1 | http://arxiv.org/pdf/2408.15300v1 | 2024-08-27 | 2024-08-27 | [
"Maxim Zhelnin",
"Viktor Moskvoretskii",
"Egor Shvetsov",
"Egor Venediktov",
"Mariya Krylova",
"Aleksandr Zuev",
"Evgeny Burnaev"
] | [
"",
"",
"",
"",
"",
"",
""
] | Parameter Efficient Fine-Tuning (PEFT) methods have gained popularity and
democratized the usage of Large Language Models (LLMs). Recent studies have
shown that a small subset of weights significantly impacts performance. Based
on this observation, we introduce a novel PEFT method, called Gaussian noise
Injected Fine Tuning of Salient Weights (GIFT-SW). Our method updates only
salient columns, while injecting Gaussian noise into non-salient ones. To
identify these columns, we developeda generalized sensitivity metric that
extends and unifies metrics from previous studies. Experiments with LLaMA
models demonstrate that GIFT-SW outperforms full fine-tuning and modern PEFT
methods under the same computational budget. Moreover, GIFT-SW offers practical
advantages to recover performance of models subjected to mixed-precision
quantization with keeping salient weights in full precision. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via
Mamba-Based Decoders | http://arxiv.org/abs/2408.15101v1 | http://arxiv.org/abs/2408.15101v1 | http://arxiv.org/pdf/2408.15101v1 | 2024-08-27 | 2024-08-27 | [
"Baijiong Lin",
"Weisen Jiang",
"Pengguang Chen",
"Shu Liu",
"Ying-Cong Chen"
] | [
"",
"",
"",
"",
""
] | Multi-task dense scene understanding, which trains a model for multiple dense
prediction tasks, has a wide range of application scenarios. Capturing
long-range dependency and enhancing cross-task interactions are crucial to
multi-task dense prediction. In this paper, we propose MTMamba++, a novel
architecture for multi-task scene understanding featuring with a Mamba-based
decoder. It contains two types of core blocks: self-task Mamba (STM) block and
cross-task Mamba (CTM) block. STM handles long-range dependency by leveraging
state-space models, while CTM explicitly models task interactions to facilitate
information exchange across tasks. We design two types of CTM block, namely
F-CTM and S-CTM, to enhance cross-task interaction from feature and semantic
perspectives, respectively. Experiments on NYUDv2, PASCAL-Context, and
Cityscapes datasets demonstrate the superior performance of MTMamba++ over
CNN-based and Transformer-based methods. The code is available at
https://github.com/EnVision-Research/MTMamba. | arXiv admin note: text overlap with arXiv:2407.02228 | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
No Regrets: Investigating and Improving Regret Approximations for
Curriculum Discovery | http://arxiv.org/abs/2408.15099v2 | http://arxiv.org/abs/2408.15099v2 | http://arxiv.org/pdf/2408.15099v2 | 2024-08-27 | 2024-08-29 | [
"Alexander Rutherford",
"Michael Beukman",
"Timon Willi",
"Bruno Lacerda",
"Nick Hawes",
"Jakob Foerster"
] | [
"",
"",
"",
"",
"",
""
] | What data or environments to use for training to improve downstream
performance is a longstanding and very topical question in reinforcement
learning. In particular, Unsupervised Environment Design (UED) methods have
gained recent attention as their adaptive curricula enable agents to be robust
to in- and out-of-distribution tasks. We ask to what extent these methods are
themselves robust when applied to a novel setting, closely inspired by a
real-world robotics problem. Surprisingly, we find that the state-of-the-art
UED methods either do not improve upon the na\"{i}ve baseline of Domain
Randomisation (DR), or require substantial hyperparameter tuning to do so. Our
analysis shows that this is due to their underlying scoring functions failing
to predict intuitive measures of ``learnability'', i.e., in finding the
settings that the agent sometimes solves, but not always. Based on this, we
instead directly train on levels with high learnability and find that this
simple and intuitive approach outperforms UED methods and DR in several
binary-outcome environments, including on our domain and the standard UED
domain of Minigrid. We further introduce a new adversarial evaluation procedure
for directly measuring robustness, closely mirroring the conditional value at
risk (CVaR). We open-source all our code and present visualisations of final
policies here: https://github.com/amacrutherford/sampling-for-learnability. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
|||
Ensuring Equitable Financial Decisions: Leveraging Counterfactual
Fairness and Deep Learning for Bias | http://arxiv.org/abs/2408.16088v1 | http://arxiv.org/abs/2408.16088v1 | http://arxiv.org/pdf/2408.16088v1 | 2024-08-27 | 2024-08-27 | [
"Saish Shinde"
] | [
""
] | Concerns regarding fairness and bias have been raised in recent years due to
the growing use of machine learning models in crucial decision-making
processes, especially when it comes to delicate characteristics like gender. In
order to address biases in machine learning models, this research paper
investigates advanced bias mitigation techniques, with a particular focus on
counterfactual fairness in conjunction with data augmentation. The study looks
into how these integrated approaches can lessen gender bias in the financial
industry, specifically in loan approval procedures. We show that these
approaches are effective in achieving more equitable results through thorough
testing and assessment on a skewed financial dataset. The findings emphasize
how crucial it is to use fairness-aware techniques when creating machine
learning models in order to guarantee morally righteous and impartial
decision-making. | 8 pages, 7 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Post-processing fairness with minimal changes | http://arxiv.org/abs/2408.15096v2 | http://arxiv.org/abs/2408.15096v2 | http://arxiv.org/pdf/2408.15096v2 | 2024-08-27 | 2024-08-29 | [
"Federico Di Gennaro",
"Thibault Laugel",
"Vincent Grari",
"Xavier Renard",
"Marcin Detyniecki"
] | [
"",
"",
"",
"",
""
] | In this paper, we introduce a novel post-processing algorithm that is both
model-agnostic and does not require the sensitive attribute at test time. In
addition, our algorithm is explicitly designed to enforce minimal changes
between biased and debiased predictions; a property that, while highly
desirable, is rarely prioritized as an explicit objective in fairness
literature. Our approach leverages a multiplicative factor applied to the logit
value of probability scores produced by a black-box classifier. We demonstrate
the efficacy of our method through empirical evaluations, comparing its
performance against other four debiasing algorithms on two widely used datasets
in fairness research. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and
Deduplication by Introducing a Competitive Large Language Model Baseline | http://arxiv.org/abs/2408.15079v1 | http://arxiv.org/abs/2408.15079v1 | http://arxiv.org/pdf/2408.15079v1 | 2024-08-27 | 2024-08-27 | [
"Guosheng Dong",
"Da Pan",
"Yiding Sun",
"Shusen Zhang",
"Zheng Liang",
"Xin Wu",
"Yanjun Shen",
"Fan Yang",
"Haoze Sun",
"Tianpeng Li",
"Mingan Lin",
"Jianhua Xu",
"Yufan Zhang",
"Xiaonan Nie",
"Lei Su",
"Bingning Wang",
"Wentao Zhang",
"Jiaxin Mao",
"Zenan Zhou",
"Weipeng Chen"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | The general capabilities of Large Language Models (LLM) highly rely on the
composition and selection on extensive pretraining datasets, treated as
commercial secrets by several institutions. To mitigate this issue, we
open-source the details of a universally applicable data processing pipeline
and validate its effectiveness and potential by introducing a competitive LLM
baseline. Specifically, the data processing pipeline consists of broad
collection to scale up and reweighting to improve quality. We then pretrain a
7B model BaichuanSEED with 3T tokens processed by our pipeline without any
deliberate downstream task-related optimization, followed by an easy but
effective supervised fine-tuning stage. BaichuanSEED demonstrates consistency
and predictability throughout training and achieves comparable performance on
comprehensive benchmarks with several commercial advanced large language
models, such as Qwen1.5 and Llama3. We also conduct several heuristic
experiments to discuss the potential for further optimization of downstream
tasks, such as mathematics and coding. | 19 pages, 6 figures | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
MMASD+: A Novel Dataset for Privacy-Preserving Behavior Analysis of
Children with Autism Spectrum Disorder | http://arxiv.org/abs/2408.15077v2 | http://arxiv.org/abs/2408.15077v2 | http://arxiv.org/pdf/2408.15077v2 | 2024-08-27 | 2024-08-28 | [
"Pavan Uttej Ravva",
"Behdokht Kiafar",
"Pinar Kullu",
"Jicheng Li",
"Anjana Bhat",
"Roghayeh Leila Barmaki"
] | [
"",
"",
"",
"",
"",
""
] | Autism spectrum disorder (ASD) is characterized by significant challenges in
social interaction and comprehending communication signals. Recently,
therapeutic interventions for ASD have increasingly utilized Deep learning
powered-computer vision techniques to monitor individual progress over time.
These models are trained on private, non-public datasets from the autism
community, creating challenges in comparing results across different models due
to privacy-preserving data-sharing issues. This work introduces MMASD+, an
enhanced version of the novel open-source dataset called Multimodal ASD
(MMASD). MMASD+ consists of diverse data modalities, including 3D-Skeleton, 3D
Body Mesh, and Optical Flow data. It integrates the capabilities of Yolov8 and
Deep SORT algorithms to distinguish between the therapist and children,
addressing a significant barrier in the original dataset. Additionally, a
Multimodal Transformer framework is proposed to predict 11 action types and the
presence of ASD. This framework achieves an accuracy of 95.03% for predicting
action types and 96.42% for predicting ASD presence, demonstrating over a 10%
improvement compared to models trained on single data modalities. These
findings highlight the advantages of integrating multiple data modalities
within the Multimodal Transformer framework. | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
|||
MiWaves Reinforcement Learning Algorithm | http://arxiv.org/abs/2408.15076v1 | http://arxiv.org/abs/2408.15076v1 | http://arxiv.org/pdf/2408.15076v1 | 2024-08-27 | 2024-08-27 | [
"Susobhan Ghosh",
"Yongyi Guo",
"Pei-Yao Hung",
"Lara Coughlin",
"Erin Bonar",
"Inbal Nahum-Shani",
"Maureen Walton",
"Susan Murphy"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | The escalating prevalence of cannabis use poses a significant public health
challenge globally. In the U.S., cannabis use is more prevalent among emerging
adults (EAs) (ages 18-25) than any other age group, with legalization in the
multiple states contributing to a public perception that cannabis is less risky
than in prior decades. To address this growing concern, we developed MiWaves, a
reinforcement learning (RL) algorithm designed to optimize the delivery of
personalized intervention prompts to reduce cannabis use among EAs. MiWaves
leverages domain expertise and prior data to tailor the likelihood of delivery
of intervention messages. This paper presents a comprehensive overview of the
algorithm's design, including key decisions and experimental outcomes. The
finalized MiWaves RL algorithm was deployed in a clinical trial from March to
May 2024. | arXiv admin note: substantial text overlap with arXiv:2402.17739 | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Interactive dense pixel visualizations for time series and model
attribution explanations | http://arxiv.org/abs/2408.15073v1 | http://arxiv.org/abs/2408.15073v1 | http://arxiv.org/pdf/2408.15073v1 | 2024-08-27 | 2024-08-27 | [
"Udo Schlegel",
"Daniel A. Keim"
] | [
"",
""
] | The field of Explainable Artificial Intelligence (XAI) for Deep Neural
Network models has developed significantly, offering numerous techniques to
extract explanations from models. However, evaluating explanations is often not
trivial, and differences in applied metrics can be subtle, especially with
non-intelligible data. Thus, there is a need for visualizations tailored to
explore explanations for domains with such data, e.g., time series. We propose
DAVOTS, an interactive visual analytics approach to explore raw time series
data, activations of neural networks, and attributions in a dense-pixel
visualization to gain insights into the data, models' decisions, and
explanations. To further support users in exploring large datasets, we apply
clustering approaches to the visualized data domains to highlight groups and
present ordering strategies for individual and combined data exploration to
facilitate finding patterns. We visualize a CNN trained on the FordA dataset to
demonstrate the approach. | 5 pages, 2 figures, accepted at MLVIS 2023 | cs.AI | [
"cs.AI",
"cs.HC",
"cs.LG"
] |
||
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text
and Protein Sequences for Protein Engineering | http://arxiv.org/abs/2408.15299v1 | http://arxiv.org/abs/2408.15299v1 | http://arxiv.org/pdf/2408.15299v1 | 2024-08-27 | 2024-08-27 | [
"Yiqing Shen",
"Zan Chen",
"Michail Mamalakis",
"Yungeng Liu",
"Tianbin Li",
"Yanzhou Su",
"Junjun He",
"Pietro Liò",
"Yu Guang Wang"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | The structural similarities between protein sequences and natural languages
have led to parallel advancements in deep learning across both domains. While
large language models (LLMs) have achieved much progress in the domain of
natural language processing, their potential in protein engineering remains
largely unexplored. Previous approaches have equipped LLMs with protein
understanding capabilities by incorporating external protein encoders, but this
fails to fully leverage the inherent similarities between protein sequences and
natural languages, resulting in sub-optimal performance and increased model
complexity. To address this gap, we present TourSynbio-7B, the first
multi-modal large model specifically designed for protein engineering tasks
without external protein encoders. TourSynbio-7B demonstrates that LLMs can
inherently learn to understand proteins as language. The model is post-trained
and instruction fine-tuned on InternLM2-7B using ProteinLMDataset, a dataset
comprising 17.46 billion tokens of text and protein sequence for
self-supervised pretraining and 893K instructions for supervised fine-tuning.
TourSynbio-7B outperforms GPT-4 on the ProteinLMBench, a benchmark of 944
manually verified multiple-choice questions, with 62.18% accuracy. Leveraging
TourSynbio-7B's enhanced protein sequence understanding capability, we
introduce TourSynbio-Agent, an innovative framework capable of performing
various protein engineering tasks, including mutation analysis, inverse
folding, protein folding, and visualization. TourSynbio-Agent integrates
previously disconnected deep learning models in the protein engineering domain,
offering a unified conversational user interface for improved usability.
Finally, we demonstrate the efficacy of TourSynbio-7B and TourSynbio-Agent
through two wet lab case studies on vanilla key enzyme modification and steroid
compound catalysis. | q-bio.BM | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] |
|||
Causal Rule Forest: Toward Interpretable and Precise Treatment Effect
Estimation | http://arxiv.org/abs/2408.15055v1 | http://arxiv.org/abs/2408.15055v1 | http://arxiv.org/pdf/2408.15055v1 | 2024-08-27 | 2024-08-27 | [
"Chan Hsu",
"Jun-Ting Wu",
"Yihuang Kang"
] | [
"",
"",
""
] | Understanding and inferencing Heterogeneous Treatment Effects (HTE) and
Conditional Average Treatment Effects (CATE) are vital for developing
personalized treatment recommendations. Many state-of-the-art approaches
achieve inspiring performance in estimating HTE on benchmark datasets or
simulation studies. However, the indirect predicting manner and complex model
architecture reduce the interpretability of these approaches. To mitigate the
gap between predictive performance and heterogeneity interpretability, we
introduce the Causal Rule Forest (CRF), a novel approach to learning hidden
patterns from data and transforming the patterns into interpretable multi-level
Boolean rules. By training the other interpretable causal inference models with
data representation learned by CRF, we can reduce the predictive errors of
these models in estimating HTE and CATE, while keeping their interpretability
for identifying subgroups that a treatment is more effective. Our experiments
underscore the potential of CRF to advance personalized interventions and
policies, paving the way for future research to enhance its scalability and
application across complex causal inference challenges. | The 25th IEEE International Conference on Information Reuse and
Integration for Data Science (IRI 2024) | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Earth Observation Satellite Scheduling with Graph Neural Networks | http://arxiv.org/abs/2408.15041v1 | http://arxiv.org/abs/2408.15041v1 | http://arxiv.org/pdf/2408.15041v1 | 2024-08-27 | 2024-08-27 | [
"Antoine Jacquet",
"Guillaume Infantes",
"Nicolas Meuleau",
"Emmanuel Benazera",
"Stéphanie Roussel",
"Vincent Baudoui",
"Jonathan Guerra"
] | [
"",
"",
"",
"",
"",
"",
""
] | The Earth Observation Satellite Planning (EOSP) is a difficult optimization
problem with considerable practical interest. A set of requested observations
must be scheduled on an agile Earth observation satellite while respecting
constraints on their visibility window, as well as maneuver constraints that
impose varying delays between successive observations. In addition, the problem
is largely oversubscribed: there are much more candidate observations than what
can possibly be achieved. Therefore, one must select the set of observations
that will be performed while maximizing their weighted cumulative benefit, and
propose a feasible schedule for these observations. As previous work mostly
focused on heuristic and iterative search algorithms, this paper presents a new
technique for selecting and scheduling observations based on Graph Neural
Networks (GNNs) and Deep Reinforcement Learning (DRL). GNNs are used to extract
relevant information from the graphs representing instances of the EOSP, and
DRL drives the search for optimal schedules. Our simulations show that it is
able to learn on small problem instances and generalize to larger real-world
instances, with very competitive performance compared to traditional
approaches. | Accepted at 17th European Workshop on Reinforcement Learning (EWRL
2024) | cs.AI | [
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] |
||
Evidence-Enhanced Triplet Generation Framework for Hallucination
Alleviation in Generative Question Answering | http://arxiv.org/abs/2408.15037v1 | http://arxiv.org/abs/2408.15037v1 | http://arxiv.org/pdf/2408.15037v1 | 2024-08-27 | 2024-08-27 | [
"Haowei Du",
"Huishuai Zhang",
"Dongyan Zhao"
] | [
"",
"",
""
] | To address the hallucination in generative question answering (GQA) where the
answer can not be derived from the document, we propose a novel
evidence-enhanced triplet generation framework, EATQA, encouraging the model to
predict all the combinations of (Question, Evidence, Answer) triplet by
flipping the source pair and the target label to understand their logical
relationships, i.e., predict Answer(A), Question(Q), and Evidence(E) given a
QE, EA, and QA pairs, respectively. Furthermore, we bridge the distribution gap
to distill the knowledge from evidence in inference stage. Our framework
ensures the model to learn the logical relation between query, evidence and
answer, which simultaneously improves the evidence generation and query
answering. In this paper, we apply EATQA to LLama and it outperforms other
LLMs-based methods and hallucination mitigation approaches on two challenging
GQA benchmarks. Further analysis shows that our method not only keeps prior
knowledge within LLM, but also mitigates hallucination and generates faithful
answers. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Mamba2MIL: State Space Duality Based Multiple Instance Learning for
Computational Pathology | http://arxiv.org/abs/2408.15032v1 | http://arxiv.org/abs/2408.15032v1 | http://arxiv.org/pdf/2408.15032v1 | 2024-08-27 | 2024-08-27 | [
"Yuqi Zhang",
"Xiaoqian Zhang",
"Jiakai Wang",
"Yuancheng Yang",
"Taiying Peng",
"Chao Tong"
] | [
"",
"",
"",
"",
"",
""
] | Computational pathology (CPath) has significantly advanced the clinical
practice of pathology. Despite the progress made, Multiple Instance Learning
(MIL), a promising paradigm within CPath, continues to face challenges,
particularly related to incomplete information utilization. Existing
frameworks, such as those based on Convolutional Neural Networks (CNNs),
attention, and selective scan space state sequential model (SSM), lack
sufficient flexibility and scalability in fusing diverse features, and cannot
effectively fuse diverse features. Additionally, current approaches do not
adequately exploit order-related and order-independent features, resulting in
suboptimal utilization of sequence information. To address these limitations,
we propose a novel MIL framework called Mamba2MIL. Our framework utilizes the
state space duality model (SSD) to model long sequences of patches of whole
slide images (WSIs), which, combined with weighted feature selection, supports
the fusion processing of more branching features and can be extended according
to specific application needs. Moreover, we introduce a sequence transformation
method tailored to varying WSI sizes, which enhances sequence-independent
features while preserving local sequence information, thereby improving
sequence information utilization. Extensive experiments demonstrate that
Mamba2MIL surpasses state-of-the-art MIL methods. We conducted extensive
experiments across multiple datasets, achieving improvements in nearly all
performance metrics. Specifically, on the NSCLC dataset, Mamba2MIL achieves a
binary tumor classification AUC of 0.9533 and an accuracy of 0.8794. On the
BRACS dataset, it achieves a multiclass classification AUC of 0.7986 and an
accuracy of 0.4981. The code is available at
https://github.com/YuqiZhang-Buaa/Mamba2MIL. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Sequence-aware Pre-training for Echocardiography Probe Guidance | http://arxiv.org/abs/2408.15026v1 | http://arxiv.org/abs/2408.15026v1 | http://arxiv.org/pdf/2408.15026v1 | 2024-08-27 | 2024-08-27 | [
"Haojun Jiang",
"Zhenguo Sun",
"Yu Sun",
"Ning Jia",
"Meng Li",
"Shaqi Luo",
"Shiji Song",
"Gao Huang"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Cardiac ultrasound probe guidance aims to help novices adjust the 6-DOF probe
pose to obtain high-quality sectional images. Cardiac ultrasound faces two
major challenges: (1) the inherently complex structure of the heart, and (2)
significant individual variations. Previous works have only learned the
population-averaged 2D and 3D structures of the heart rather than personalized
cardiac structural features, leading to a performance bottleneck. Clinically,
we observed that sonographers adjust their understanding of a patient's cardiac
structure based on prior scanning sequences, thereby modifying their scanning
strategies. Inspired by this, we propose a sequence-aware self-supervised
pre-training method. Specifically, our approach learns personalized 2D and 3D
cardiac structural features by predicting the masked-out images and actions in
a scanning sequence. We hypothesize that if the model can predict the missing
content it has acquired a good understanding of the personalized cardiac
structure. In the downstream probe guidance task, we also introduced a sequence
modeling approach that models individual cardiac structural information based
on the images and actions from historical scan data, enabling more accurate
navigation decisions. Experiments on a large-scale dataset with 1.36 million
samples demonstrated that our proposed sequence-aware paradigm can
significantly reduce navigation errors, with translation errors decreasing by
15.90% to 36.87% and rotation errors decreasing by 11.13% to 20.77%, compared
to state-of-the-art methods. | Tech Report | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Cross-subject Brain Functional Connectivity Analysis for Multi-task
Cognitive State Evaluation | http://arxiv.org/abs/2408.15018v1 | http://arxiv.org/abs/2408.15018v1 | http://arxiv.org/pdf/2408.15018v1 | 2024-08-27 | 2024-08-27 | [
"Jun Chen",
"Anqi Chen",
"Bingkun Jiang",
"Mohammad S. Obaidat",
"Ni Li",
"Xinyu Zhang"
] | [
"",
"",
"",
"",
"",
""
] | Cognition refers to the function of information perception and processing,
which is the fundamental psychological essence of human beings. It is
responsible for reasoning and decision-making, while its evaluation is
significant for the aviation domain in mitigating potential safety risks.
Existing studies tend to use varied methods for cognitive state evaluation yet
have limitations in timeliness, generalisation, and interpretability.
Accordingly, this study adopts brain functional connectivity with
electroencephalography signals to capture associations in brain regions across
multiple subjects for evaluating real-time cognitive states. Specifically, a
virtual reality-based flight platform is constructed with multi-screen
embedded. Three distinctive cognitive tasks are designed and each has three
degrees of difficulty. Thirty subjects are acquired for analysis and
evaluation. The results are interpreted through different perspectives,
including inner-subject and cross-subject for task-wise and gender-wise
underlying brain functional connectivity. Additionally, this study incorporates
questionnaire-based, task performance-based, and physiological measure-based
approaches to fairly label the trials. A multi-class cognitive state evaluation
is further conducted with the active brain connections. Benchmarking results
demonstrate that the identified brain regions have considerable influences in
cognition, with a multi-class accuracy rate of 95.83% surpassing existing
studies. The derived findings bring significance to understanding the dynamic
relationships among human brain functional regions, cross-subject cognitive
behaviours, and decision-making, which have promising practical application
values. | cs.HC | [
"cs.HC",
"cs.AI"
] |
|||
Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for
Long-Tailed Continual Learning | http://arxiv.org/abs/2408.14976v1 | http://arxiv.org/abs/2408.14976v1 | http://arxiv.org/pdf/2408.14976v1 | 2024-08-27 | 2024-08-27 | [
"Lei Liu",
"Li Liu",
"Yawen Cui"
] | [
"",
"",
""
] | Even in the era of large models, one of the well-known issues in continual
learning (CL) is catastrophic forgetting, which is significantly challenging
when the continual data stream exhibits a long-tailed distribution, termed as
Long-Tailed Continual Learning (LTCL). Existing LTCL solutions generally
require the label distribution of the data stream to achieve re-balance
training. However, obtaining such prior information is often infeasible in real
scenarios since the model should learn without pre-identifying the majority and
minority classes. To this end, we propose a novel Prior-free Balanced Replay
(PBR) framework to learn from long-tailed data stream with less forgetting.
Concretely, motivated by our experimental finding that the minority classes are
more likely to be forgotten due to the higher uncertainty, we newly design an
uncertainty-guided reservoir sampling strategy to prioritize rehearsing
minority data without using any prior information, which is based on the mutual
dependence between the model and samples. Additionally, we incorporate two
prior-free components to further reduce the forgetting issue: (1) Boundary
constraint is to preserve uncertain boundary supporting samples for continually
re-estimating task boundaries. (2) Prototype constraint is to maintain the
consistency of learned class prototypes along with training. Our approach is
evaluated on three standard long-tailed benchmarks, demonstrating superior
performance to existing CL methods and previous SOTA LTCL approach in both
task- and class-incremental learning settings, as well as ordered- and
shuffled-LTCL settings. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
|||
YOLO-Stutter: End-to-end Region-Wise Speech Dysfluency Detection | http://arxiv.org/abs/2408.15297v1 | http://arxiv.org/abs/2408.15297v1 | http://arxiv.org/pdf/2408.15297v1 | 2024-08-27 | 2024-08-27 | [
"Xuanru Zhou",
"Anshul Kashyap",
"Steve Li",
"Ayati Sharma",
"Brittany Morin",
"David Baquirin",
"Jet Vonk",
"Zoe Ezzes",
"Zachary Miller",
"Maria Luisa Gorno Tempini",
"Jiachen Lian",
"Gopala Krishna Anumanchipalli"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Dysfluent speech detection is the bottleneck for disordered speech analysis
and spoken language learning. Current state-of-the-art models are governed by
rule-based systems which lack efficiency and robustness, and are sensitive to
template design. In this paper, we propose YOLO-Stutter: a first end-to-end
method that detects dysfluencies in a time-accurate manner. YOLO-Stutter takes
imperfect speech-text alignment as input, followed by a spatial feature
aggregator, and a temporal dependency extractor to perform region-wise boundary
and class predictions. We also introduce two dysfluency corpus, VCTK-Stutter
and VCTK-TTS, that simulate natural spoken dysfluencies including repetition,
block, missing, replacement, and prolongation. Our end-to-end method achieves
state-of-the-art performance with a minimum number of trainable parameters for
on both simulated data and real aphasia speech. Code and datasets are
open-sourced at https://github.com/rorizzz/YOLO-Stutter | Interspeech 2024 | eess.AS | [
"eess.AS",
"cs.AI",
"cs.CL"
] |
||
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task | http://arxiv.org/abs/2408.14961v1 | http://arxiv.org/abs/2408.14961v1 | http://arxiv.org/pdf/2408.14961v1 | 2024-08-27 | 2024-08-27 | [
"Lingyun Huang",
"Jianxu Mao",
"Yaonan Wang",
"Junfei Yi",
"Ziming Tao"
] | [
"",
"",
"",
"",
""
] | In recent years, the rapid expansion of model sizes has led to large-scale
pre-trained models demonstrating remarkable capabilities. Consequently, there
has been a trend towards increasing the scale of models. However, this trend
introduces significant challenges, including substantial computational costs of
training and transfer to downstream tasks. To address these issues,
Parameter-Efficient Fine-Tuning (PEFT) methods have been introduced. These
methods optimize large-scale pre-trained models for specific tasks by
fine-tuning a select group of parameters. Among these PEFT methods,
adapter-based and prompt-based methods are the primary techniques.
Specifically, in the field of visual fine-tuning, adapters gain prominence over
prompts because of the latter's relatively weaker performance and efficiency.
Under the circumstances, we refine the widely-used Visual Prompt Tuning (VPT)
method, proposing Cross Visual Prompt Tuning (CVPT). CVPT calculates
cross-attention between the prompt tokens and the embedded tokens, which allows
us to compute the semantic relationship between them and conduct the
fine-tuning of models exactly to adapt visual tasks better. Furthermore, we
introduce the weight-sharing mechanism to initialize the parameters of
cross-attention, which avoids massive learnable parameters from cross-attention
and enhances the representative capability of cross-attention. We conduct
comprehensive testing across 25 datasets and the result indicates that CVPT
significantly improves VPT's performance and efficiency in visual tasks. For
example, on the VTAB-1K benchmark, CVPT outperforms VPT over 4% in average
accuracy, rivaling the advanced adapter-based methods in performance and
efficiency. Our experiments confirm that prompt-based methods can achieve
exceptional results in visual fine-tuning. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual
Progress | http://arxiv.org/abs/2408.14960v1 | http://arxiv.org/abs/2408.14960v1 | http://arxiv.org/pdf/2408.14960v1 | 2024-08-27 | 2024-08-27 | [
"Ayomide Odumakinde",
"Daniel D'souza",
"Pat Verga",
"Beyza Ermis",
"Sara Hooker"
] | [
"",
"",
"",
"",
""
] | The use of synthetic data has played a critical role in recent state-of-art
breakthroughs. However, overly relying on a single oracle teacher model to
generate data has been shown to lead to model collapse and invite propagation
of biases. These limitations are particularly evident in multilingual settings,
where the absence of a universally effective teacher model that excels across
all languages presents significant challenges. In this work, we address these
extreme difference by introducing "multilingual arbitrage", which capitalizes
on performance variations between multiple models for a given language. To do
so, we strategically route samples through a diverse pool of models, each with
unique strengths in different languages. Across exhaustive experiments on
state-of-art models, our work suggests that arbitrage techniques allow for
spectacular gains in performance that far outperform relying on a single
teacher. In particular, compared to the best single teacher, we observe gains
of up to 56.5% improvement in win rates averaged across all languages when
switching to multilingual arbitrage. We observe the most significant gains for
the least resourced languages in our pool. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
NeuralOOD: Improving Out-of-Distribution Generalization Performance with
Brain-machine Fusion Learning Framework | http://arxiv.org/abs/2408.14950v1 | http://arxiv.org/abs/2408.14950v1 | http://arxiv.org/pdf/2408.14950v1 | 2024-08-27 | 2024-08-27 | [
"Shuangchen Zhao",
"Changde Du",
"Hui Li",
"Huiguang He"
] | [
"",
"",
"",
""
] | Deep Neural Networks (DNNs) have demonstrated exceptional recognition
capabilities in traditional computer vision (CV) tasks. However, existing CV
models often suffer a significant decrease in accuracy when confronted with
out-of-distribution (OOD) data. In contrast to these DNN models, human can
maintain a consistently low error rate when facing OOD scenes, partly
attributed to the rich prior cognitive knowledge stored in the human brain.
Previous OOD generalization researches only focus on the single modal,
overlooking the advantages of multimodal learning method. In this paper, we
utilize the multimodal learning method to improve the OOD generalization and
propose a novel Brain-machine Fusion Learning (BMFL) framework. We adopt the
cross-attention mechanism to fuse the visual knowledge from CV model and prior
cognitive knowledge from the human brain. Specially, we employ a pre-trained
visual neural encoding model to predict the functional Magnetic Resonance
Imaging (fMRI) from visual features which eliminates the need for the fMRI data
collection and pre-processing, effectively reduces the workload associated with
conventional BMFL methods. Furthermore, we construct a brain transformer to
facilitate the extraction of knowledge inside the fMRI data. Moreover, we
introduce the Pearson correlation coefficient maximization regularization
method into the training process, which improves the fusion capability with
better constrains. Our model outperforms the DINOv2 and baseline models on the
ImageNet-1k validation dataset as well as six curated OOD datasets, showcasing
its superior performance in diverse scenarios. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian
Network Structures | http://arxiv.org/abs/2408.14935v1 | http://arxiv.org/abs/2408.14935v1 | http://arxiv.org/pdf/2408.14935v1 | 2024-08-27 | 2024-08-27 | [
"Tomi Silander",
"Janne Leppä-aho",
"Elias Jääsaari",
"Teemu Roos"
] | [
"",
"",
"",
""
] | We introduce an information theoretic criterion for Bayesian network
structure learning which we call quotient normalized maximum likelihood (qNML).
In contrast to the closely related factorized normalized maximum likelihood
criterion, qNML satisfies the property of score equivalence. It is also
decomposable and completely free of adjustable hyperparameters. For practical
computations, we identify a remarkably accurate approximation proposed earlier
by Szpankowski and Weinberger. Experiments on both simulated and real data
demonstrate that the new criterion leads to parsimonious models with good
predictive accuracy. | Accepted to AISTATS 2018 | PMLR 84:948-957, 2018 | cs.LG | [
"cs.LG",
"cs.AI"
] |
|
Distance-Forward Learning: Enhancing the Forward-Forward Algorithm
Towards High-Performance On-Chip Learning | http://arxiv.org/abs/2408.14925v1 | http://arxiv.org/abs/2408.14925v1 | http://arxiv.org/pdf/2408.14925v1 | 2024-08-27 | 2024-08-27 | [
"Yujie Wu",
"Siyuan Xu",
"Jibin Wu",
"Lei Deng",
"Mingkun Xu",
"Qinghao Wen",
"Guoqi Li"
] | [
"",
"",
"",
"",
"",
"",
""
] | The Forward-Forward (FF) algorithm was recently proposed as a local learning
method to address the limitations of backpropagation (BP), offering biological
plausibility along with memory-efficient and highly parallelized computational
benefits. However, it suffers from suboptimal performance and poor
generalization, largely due to inadequate theoretical support and a lack of
effective learning strategies. In this work, we reformulate FF using distance
metric learning and propose a distance-forward algorithm (DF) to improve FF
performance in supervised vision tasks while preserving its local computational
properties, making it competitive for efficient on-chip learning. To achieve
this, we reinterpret FF through the lens of centroid-based metric learning and
develop a goodness-based N-pair margin loss to facilitate the learning of
discriminative features. Furthermore, we integrate layer-collaboration local
update strategies to reduce information loss caused by greedy local parameter
updates. Our method surpasses existing FF models and other advanced local
learning approaches, with accuracies of 99.7\% on MNIST, 88.2\% on CIFAR-10,
59\% on CIFAR-100, 95.9\% on SVHN, and 82.5\% on ImageNette, respectively.
Moreover, it achieves comparable performance with less than 40\% memory cost
compared to BP training, while exhibiting stronger robustness to multiple types
of hardware-related noise, demonstrating its potential for online learning and
energy-efficient computation on neuromorphic chips. | cs.NE | [
"cs.NE",
"cs.AI"
] |
|||
Evaluating the Predictive Features of Person-Centric Knowledge Graph
Embeddings: Unfolding Ablation Studies | http://arxiv.org/abs/2408.15294v2 | http://arxiv.org/abs/2408.15294v2 | http://arxiv.org/pdf/2408.15294v2 | 2024-08-27 | 2024-08-29 | [
"Christos Theodoropoulos",
"Natasha Mulligan",
"Joao Bettencourt-Silva"
] | [
"",
"",
""
] | Developing novel predictive models with complex biomedical information is
challenging due to various idiosyncrasies related to heterogeneity,
standardization or sparseness of the data. We previously introduced a
person-centric ontology to organize information about individual patients, and
a representation learning framework to extract person-centric knowledge graphs
(PKGs) and to train Graph Neural Networks (GNNs). In this paper, we propose a
systematic approach to examine the results of GNN models trained with both
structured and unstructured information from the MIMIC-III dataset. Through
ablation studies on different clinical, demographic, and social data, we show
the robustness of this approach in identifying predictive features in PKGs for
the task of readmission prediction. | Published in the 34th Medical Informatics Europe Conference | Studies in health technology and informatics vol. 316 (2024):
575-579 | 10.3233/SHTI240479 | cs.LG | [
"cs.LG",
"cs.AI"
] |
VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view
Videos of Daily Activities | http://arxiv.org/abs/2408.14895v2 | http://arxiv.org/abs/2408.14895v2 | http://arxiv.org/pdf/2408.14895v2 | 2024-08-27 | 2024-08-28 | [
"Shusaku Egami",
"Takahiro Ugai",
"Swe Nwe Nwe Htun",
"Ken Fukuda"
] | [
"",
"",
"",
""
] | Multi-modal knowledge graphs (MMKGs), which ground various non-symbolic data
(e.g., images and videos) into symbols, have attracted attention as resources
enabling knowledge processing and machine learning across modalities. However,
the construction of MMKGs for videos consisting of multiple events, such as
daily activities, is still in the early stages. In this paper, we construct an
MMKG based on synchronized multi-view simulated videos of daily activities.
Besides representing the content of daily life videos as event-centric
knowledge, our MMKG also includes frame-by-frame fine-grained changes, such as
bounding boxes within video frames. In addition, we provide support tools for
querying our MMKG. As an application example, we demonstrate that our MMKG
facilitates benchmarking vision-language models by providing the necessary
vision-language datasets for a tailored task. | 5 pages, 4 figures, accepted by CIKM2024 Resource Track | 10.1145/3627673.3679175 | cs.AI | [
"cs.AI",
"cs.CL",
"cs.CV",
"68T30",
"I.2.4; H.5.1"
] |
|
The VoxCeleb Speaker Recognition Challenge: A Retrospective | http://arxiv.org/abs/2408.14886v1 | http://arxiv.org/abs/2408.14886v1 | http://arxiv.org/pdf/2408.14886v1 | 2024-08-27 | 2024-08-27 | [
"Jaesung Huh",
"Joon Son Chung",
"Arsha Nagrani",
"Andrew Brown",
"Jee-weon Jung",
"Daniel Garcia-Romero",
"Andrew Zisserman"
] | [
"",
"",
"",
"",
"",
"",
""
] | The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of
challenges and workshops that ran annually from 2019 to 2023. The challenges
primarily evaluated the tasks of speaker recognition and diarisation under
various settings including: closed and open training data; as well as
supervised, self-supervised, and semi-supervised training for domain
adaptation. The challenges also provided publicly available training and
evaluation datasets for each task and setting, with new test sets released each
year. In this paper, we provide a review of these challenges that covers: what
they explored; the methods developed by the challenge participants and how
these evolved; and also the current state of the field for speaker verification
and diarisation. We chart the progress in performance over the five
installments of the challenge on a common evaluation dataset and provide a
detailed analysis of how each year's special focus affected participants'
performance. This paper is aimed both at researchers who want an overview of
the speaker recognition and diarisation field, and also at challenge organisers
who want to benefit from the successes and avoid the mistakes of the VoxSRC
challenges. We end with a discussion of the current strengths of the field and
open challenges. Project page :
https://mm.kaist.ac.kr/datasets/voxceleb/voxsrc/workshop.html | TASLP 2024 | 10.1109/TASLP.2024.3444456 | cs.SD | [
"cs.SD",
"cs.AI",
"eess.AS"
] |
|
Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting
for Smart and Connected Infrastructures | http://arxiv.org/abs/2408.14875v1 | http://arxiv.org/abs/2408.14875v1 | http://arxiv.org/pdf/2408.14875v1 | 2024-08-27 | 2024-08-27 | [
"Pooja Krishan",
"Rohan Mohapatra",
"Saptarshi Sengupta"
] | [
"",
"",
""
] | The emergence of deep learning models has revolutionized various industries
over the last decade, leading to a surge in connected devices and
infrastructures. However, these models can be tricked into making incorrect
predictions with high confidence, leading to disastrous failures and security
concerns. To this end, we explore the impact of adversarial attacks on
multivariate time-series forecasting and investigate methods to counter them.
Specifically, we employ untargeted white-box attacks, namely the Fast Gradient
Sign Method (FGSM) and the Basic Iterative Method (BIM), to poison the inputs
to the training process, effectively misleading the model. We also illustrate
the subtle modifications to the inputs after the attack, which makes detecting
the attack using the naked eye quite difficult. Having demonstrated the
feasibility of these attacks, we develop robust models through adversarial
training and model hardening. We are among the first to showcase the
transferability of these attacks and defenses by extrapolating our work from
the benchmark electricity data to a larger, 10-year real-world data used for
predicting the time-to-failure of hard disks. Our experimental results confirm
that the attacks and defenses achieve the desired security thresholds, leading
to a 72.41% and 94.81% decrease in RMSE for the electricity and hard disk
datasets respectively after implementing the adversarial defenses. | 17 pages, 32 figures | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.PF",
"B.1.3; I.2.4"
] |
||
Learning Robust Reward Machines from Noisy Labels | http://arxiv.org/abs/2408.14871v1 | http://arxiv.org/abs/2408.14871v1 | http://arxiv.org/pdf/2408.14871v1 | 2024-08-27 | 2024-08-27 | [
"Roko Parac",
"Lorenzo Nodari",
"Leo Ardon",
"Daniel Furelos-Blanco",
"Federico Cerutti",
"Alessandra Russo"
] | [
"",
"",
"",
"",
"",
""
] | This paper presents PROB-IRM, an approach that learns robust reward machines
(RMs) for reinforcement learning (RL) agents from noisy execution traces. The
key aspect of RM-driven RL is the exploitation of a finite-state machine that
decomposes the agent's task into different subtasks. PROB-IRM uses a
state-of-the-art inductive logic programming framework robust to noisy examples
to learn RMs from noisy traces using the Bayesian posterior degree of beliefs,
thus ensuring robustness against inconsistencies. Pivotal for the results is
the interleaving between RM learning and policy learning: a new RM is learned
whenever the RL agent generates a trace that is believed not to be accepted by
the current RM. To speed up the training of the RL agent, PROB-IRM employs a
probabilistic formulation of reward shaping that uses the posterior Bayesian
beliefs derived from the traces. Our experimental analysis shows that PROB-IRM
can learn (potentially imperfect) RMs from noisy traces and exploit them to
train an RL agent to solve its tasks successfully. Despite the complexity of
learning the RM from noisy traces, agents trained with PROB-IRM perform
comparably to agents provided with handcrafted RMs. | Preprint accepted for publication to the 21st International
Conference on Principles of Knowledge Representation and Reasoning (KR 2024) | cs.AI | [
"cs.AI",
"cs.LG"
] |
||
Learning Granularity Representation for Temporal Knowledge Graph
Completion | http://arxiv.org/abs/2408.15293v1 | http://arxiv.org/abs/2408.15293v1 | http://arxiv.org/pdf/2408.15293v1 | 2024-08-27 | 2024-08-27 | [
"Jinchuan Zhang",
"Tianqi Wan",
"Chong Mu",
"Guangxi Lu",
"Ling Tian"
] | [
"",
"",
"",
"",
""
] | Temporal Knowledge Graphs (TKGs) incorporate temporal information to reflect
the dynamic structural knowledge and evolutionary patterns of real-world facts.
Nevertheless, TKGs are still limited in downstream applications due to the
problem of incompleteness. Consequently, TKG completion (also known as link
prediction) has been widely studied, with recent research focusing on
incorporating independent embeddings of time or combining them with entities
and relations to form temporal representations. However, most existing methods
overlook the impact of history from a multi-granularity aspect. The inherent
semantics of human-defined temporal granularities, such as ordinal dates,
reveal general patterns to which facts typically adhere. To counter this
limitation, this paper proposes \textbf{L}earning \textbf{G}ranularity
\textbf{Re}presentation (termed $\mathsf{LGRe}$) for TKG completion. It
comprises two main components: Granularity Representation Learning (GRL) and
Adaptive Granularity Balancing (AGB). Specifically, GRL employs time-specific
multi-layer convolutional neural networks to capture interactions between
entities and relations at different granularities. After that, AGB generates
adaptive weights for these embeddings according to temporal semantics,
resulting in expressive representations of predictions. Moreover, to reflect
similar semantics of adjacent timestamps, a temporal loss function is
introduced. Extensive experimental results on four event benchmarks demonstrate
the effectiveness of $\mathsf{LGRe}$ in learning time-related representations.
To ensure reproducibility, our code is available at
https://github.com/KcAcoZhang/LGRe. | 15 pages. Accepted at ICONIP 2024 | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
||
Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus
via Model-Based RL | http://arxiv.org/abs/2408.14855v1 | http://arxiv.org/abs/2408.14855v1 | http://arxiv.org/pdf/2408.14855v1 | 2024-08-27 | 2024-08-27 | [
"Jihwan Lee",
"Woochang Sim",
"Sejin Kim",
"Sundong Kim"
] | [
"",
"",
"",
""
] | This paper demonstrates that model-based reinforcement learning (model-based
RL) is a suitable approach for the task of analogical reasoning. We hypothesize
that model-based RL can solve analogical reasoning tasks more efficiently
through the creation of internal models. To test this, we compared DreamerV3, a
model-based RL method, with Proximal Policy Optimization, a model-free RL
method, on the Abstraction and Reasoning Corpus (ARC) tasks. Our results
indicate that model-based RL not only outperforms model-free RL in learning and
generalizing from single tasks but also shows significant advantages in
reasoning across similar tasks. | Accepted to IJCAI 2024 IARML Workshop | cs.AI | [
"cs.AI",
"cs.LO"
] |
||
Detecting AI Flaws: Target-Driven Attacks on Internal Faults in Language
Models | http://arxiv.org/abs/2408.14853v1 | http://arxiv.org/abs/2408.14853v1 | http://arxiv.org/pdf/2408.14853v1 | 2024-08-27 | 2024-08-27 | [
"Yuhao Du",
"Zhuo Li",
"Pengyu Cheng",
"Xiang Wan",
"Anningzhe Gao"
] | [
"",
"",
"",
"",
""
] | Large Language Models (LLMs) have become a focal point in the rapidly
evolving field of artificial intelligence. However, a critical concern is the
presence of toxic content within the pre-training corpus of these models, which
can lead to the generation of inappropriate outputs. Investigating methods for
detecting internal faults in LLMs can help us understand their limitations and
improve their security. Existing methods primarily focus on jailbreaking
attacks, which involve manually or automatically constructing adversarial
content to prompt the target LLM to generate unexpected responses. These
methods rely heavily on prompt engineering, which is time-consuming and usually
requires specially designed questions. To address these challenges, this paper
proposes a target-driven attack paradigm that focuses on directly eliciting the
target response instead of optimizing the prompts. We introduce the use of
another LLM as the detector for toxic content, referred to as ToxDet. Given a
target toxic response, ToxDet can generate a possible question and a
preliminary answer to provoke the target model into producing desired toxic
responses with meanings equivalent to the provided one. ToxDet is trained by
interacting with the target LLM and receiving reward signals from it, utilizing
reinforcement learning for the optimization process. While the primary focus of
the target models is on open-source LLMs, the fine-tuned ToxDet can also be
transferred to attack black-box models such as GPT-4o, achieving notable
results. Experimental results on AdvBench and HH-Harmless datasets demonstrate
the effectiveness of our methods in detecting the tendencies of target LLMs to
generate harmful responses. This algorithm not only exposes vulnerabilities but
also provides a valuable resource for researchers to strengthen their models
against such attacks. | cs.CL | [
"cs.CL",
"cs.AI",
"cs.CR"
] |
|||
Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On
Wikidata using LM probing | http://arxiv.org/abs/2408.14849v1 | http://arxiv.org/abs/2408.14849v1 | http://arxiv.org/pdf/2408.14849v1 | 2024-08-27 | 2024-08-27 | [
"Hanna Abi Akl"
] | [
""
] | We introduce SHADOW, a fine-tuned language model trained on an intermediate
task using associative deductive reasoning, and measure its performance on a
knowledge base construction task using Wikidata triple completion. We evaluate
SHADOW on the LM-KBC 2024 challenge and show that it outperforms the baseline
solution by 20% with a F1 score of 68.72%. | 6 pages, 1 figure | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
Diffusion based Semantic Outlier Generation via Nuisance Awareness for
Out-of-Distribution Detection | http://arxiv.org/abs/2408.14841v1 | http://arxiv.org/abs/2408.14841v1 | http://arxiv.org/pdf/2408.14841v1 | 2024-08-27 | 2024-08-27 | [
"Suhee Yoon",
"Sanghyu Yoon",
"Hankook Lee",
"Ye Seul Sim",
"Sungik Choi",
"Kyungeun Lee",
"Hye-Seung Cho",
"Woohyung Lim"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Out-of-distribution (OOD) detection, which determines whether a given sample
is part of the in-distribution (ID), has recently shown promising results
through training with synthetic OOD datasets. Nonetheless, existing methods
often produce outliers that are considerably distant from the ID, showing
limited efficacy for capturing subtle distinctions between ID and OOD. To
address these issues, we propose a novel framework, Semantic Outlier generation
via Nuisance Awareness (SONA), which notably produces challenging outliers by
directly leveraging pixel-space ID samples through diffusion models. Our
approach incorporates SONA guidance, providing separate control over semantic
and nuisance regions of ID samples. Thereby, the generated outliers achieve two
crucial properties: (i) they present explicit semantic-discrepant information,
while (ii) maintaining various levels of nuisance resemblance with ID.
Furthermore, the improved OOD detector training with SONA outliers facilitates
learning with a focus on semantic distinctions. Extensive experiments
demonstrate the effectiveness of our framework, achieving an impressive AUROC
of 88% on near-OOD datasets, which surpasses the performance of baseline
methods by a significant margin of approximately 6%. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding | http://arxiv.org/abs/2408.14840v1 | http://arxiv.org/abs/2408.14840v1 | http://arxiv.org/pdf/2408.14840v1 | 2024-08-27 | 2024-08-27 | [
"Yang Liu",
"Chuan Zhou",
"Peng Zhang",
"Yanan Cao",
"Yongchao Liu",
"Zhao Li",
"Hongyang Chen"
] | [
"",
"",
"",
"",
"",
"",
""
] | Knowledge graph embedding (KGE) constitutes a foundational task, directed
towards learning representations for entities and relations within knowledge
graphs (KGs), with the objective of crafting representations comprehensive
enough to approximate the logical and symbolic interconnections among entities.
In this paper, we define a metric Z-counts to measure the difficulty of
training each triple ($<$head entity, relation, tail entity$>$) in KGs with
theoretical analysis. Based on this metric, we propose \textbf{CL4KGE}, an
efficient \textbf{C}urriculum \textbf{L}earning based training strategy for
\textbf{KGE}. This method includes a difficulty measurer and a training
scheduler that aids in the training of KGE models. Our approach possesses the
flexibility to act as a plugin within a wide range of KGE models, with the
added advantage of adaptability to the majority of KGs in existence. The
proposed method has been evaluated on popular KGE models, and the results
demonstrate that it enhances the state-of-the-art methods. The use of Z-counts
as a metric has enabled the identification of challenging triples in KGs, which
helps in devising effective training strategies. | 16 pages, 3 figures | cs.AI | [
"cs.AI",
"cs.CL",
"cs.LG"
] |
||
Diffusion Models Are Real-Time Game Engines | http://arxiv.org/abs/2408.14837v1 | http://arxiv.org/abs/2408.14837v1 | http://arxiv.org/pdf/2408.14837v1 | 2024-08-27 | 2024-08-27 | [
"Dani Valevski",
"Yaniv Leviathan",
"Moab Arar",
"Shlomi Fruchter"
] | [
"",
"",
"",
""
] | We present GameNGen, the first game engine powered entirely by a neural model
that enables real-time interaction with a complex environment over long
trajectories at high quality. GameNGen can interactively simulate the classic
game DOOM at over 20 frames per second on a single TPU. Next frame prediction
achieves a PSNR of 29.4, comparable to lossy JPEG compression. Human raters are
only slightly better than random chance at distinguishing short clips of the
game from clips of the simulation. GameNGen is trained in two phases: (1) an
RL-agent learns to play the game and the training sessions are recorded, and
(2) a diffusion model is trained to produce the next frame, conditioned on the
sequence of past frames and actions. Conditioning augmentations enable stable
auto-regressive generation over long trajectories. | Project page: https://gamengen.github.io/ | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
||
Strategic Optimization and Challenges of Large Language Models in
Object-Oriented Programming | http://arxiv.org/abs/2408.14834v1 | http://arxiv.org/abs/2408.14834v1 | http://arxiv.org/pdf/2408.14834v1 | 2024-08-27 | 2024-08-27 | [
"Zinan Wang"
] | [
""
] | In the area of code generation research, the emphasis has transitioned from
crafting individual functions to developing class-level method code that
integrates contextual information. This shift has brought several benchmarks
such as ClassEval and CoderEval, which consider class-level contexts.
Nevertheless, the influence of specific contextual factors at the method level
remains less explored.
This research focused on method-level code generation within the
Object-Oriented Programming (OOP) framework. Based on CoderEval, we devised
experiments that varied the extent of contextual information in the prompts,
ranging from method-specific to project-wide details. We introduced the
innovative metric of "Prompt-Token Cost-Effectiveness" to evaluate the economic
viability of incorporating additional contextual layers. Our findings indicate
that prompts enriched with method invocation details yield the highest
cost-effectiveness. Additionally, our study revealed disparities among Large
Language Models (LLMs) regarding error type distributions and the level of
assistance they provide to developers. Notably, larger LLMs do not invariably
perform better. We also observed that tasks with higher degrees of coupling
present more substantial challenges, suggesting that the choice of LLM should
be tailored to the task's coupling degree. For example, GPT-4 exhibited
improved performance in low-coupling scenarios, whereas GPT-3.5 seemed better
suited for tasks with high coupling. By meticulously curating prompt content
and selecting the appropriate LLM, developers can optimize code quality while
maximizing cost-efficiency during the development process. | 10 pages | cs.SE | [
"cs.SE",
"cs.AI"
] |
||
From Rule-Based Models to Deep Learning Transformers Architectures for
Natural Language Processing and Sign Language Translation Systems: Survey,
Taxonomy and Performance Evaluation | http://arxiv.org/abs/2408.14825v1 | http://arxiv.org/abs/2408.14825v1 | http://arxiv.org/pdf/2408.14825v1 | 2024-08-27 | 2024-08-27 | [
"Nada Shahin",
"Leila Ismail"
] | [
"",
""
] | With the growing Deaf and Hard of Hearing population worldwide and the
persistent shortage of certified sign language interpreters, there is a
pressing need for an efficient, signs-driven, integrated end-to-end translation
system, from sign to gloss to text and vice-versa. There has been a wealth of
research on machine translations and related reviews. However, there are few
works on sign language machine translation considering the particularity of the
language being continuous and dynamic. This paper aims to address this void,
providing a retrospective analysis of the temporal evolution of sign language
machine translation algorithms and a taxonomy of the Transformers
architectures, the most used approach in language translation. We also present
the requirements of a real-time Quality-of-Service sign language ma-chine
translation system underpinned by accurate deep learning algorithms. We propose
future research directions for sign language translation systems. | cs.AI | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG",
"I.2, I.2.7, I.4, I.4.9"
] |
|||
A Comprehensive Benchmark of Machine and Deep Learning Across Diverse
Tabular Datasets | http://arxiv.org/abs/2408.14817v1 | http://arxiv.org/abs/2408.14817v1 | http://arxiv.org/pdf/2408.14817v1 | 2024-08-27 | 2024-08-27 | [
"Assaf Shmuel",
"Oren Glickman",
"Teddy Lazebnik"
] | [
"",
"",
""
] | The analysis of tabular datasets is highly prevalent both in scientific
research and real-world applications of Machine Learning (ML). Unlike many
other ML tasks, Deep Learning (DL) models often do not outperform traditional
methods in this area. Previous comparative benchmarks have shown that DL
performance is frequently equivalent or even inferior to models such as
Gradient Boosting Machines (GBMs). In this study, we introduce a comprehensive
benchmark aimed at better characterizing the types of datasets where DL models
excel. Although several important benchmarks for tabular datasets already
exist, our contribution lies in the variety and depth of our comparison: we
evaluate 111 datasets with 20 different models, including both regression and
classification tasks. These datasets vary in scale and include both those with
and without categorical variables. Importantly, our benchmark contains a
sufficient number of datasets where DL models perform best, allowing for a
thorough analysis of the conditions under which DL models excel. Building on
the results of this benchmark, we train a model that predicts scenarios where
DL models outperform alternative methods with 86.1% accuracy (AUC 0.78). We
present insights derived from this characterization and compare these findings
to previous benchmarks. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Brain-inspired Artificial Intelligence: A Comprehensive Review | http://arxiv.org/abs/2408.14811v1 | http://arxiv.org/abs/2408.14811v1 | http://arxiv.org/pdf/2408.14811v1 | 2024-08-27 | 2024-08-27 | [
"Jing Ren",
"Feng Xia"
] | [
"",
""
] | Current artificial intelligence (AI) models often focus on enhancing
performance through meticulous parameter tuning and optimization techniques.
However, the fundamental design principles behind these models receive
comparatively less attention, which can limit our understanding of their
potential and constraints. This comprehensive review explores the diverse
design inspirations that have shaped modern AI models, i.e., brain-inspired
artificial intelligence (BIAI). We present a classification framework that
categorizes BIAI approaches into physical structure-inspired and human
behavior-inspired models. We also examine the real-world applications where
different BIAI models excel, highlighting their practical benefits and
deployment challenges. By delving into these areas, we provide new insights and
propose future research directions to drive innovation and address current gaps
in the field. This review offers researchers and practitioners a comprehensive
overview of the BIAI landscape, helping them harness its potential and expedite
advancements in AI development. | 35 pages, 4 figures | cs.AI | [
"cs.AI"
] |
||
Poly2Vec: Polymorphic Encoding of Geospatial Objects for Spatial
Reasoning with Deep Neural Networks | http://arxiv.org/abs/2408.14806v1 | http://arxiv.org/abs/2408.14806v1 | http://arxiv.org/pdf/2408.14806v1 | 2024-08-27 | 2024-08-27 | [
"Maria Despoina Siampou",
"Jialiang Li",
"John Krumm",
"Cyrus Shahabi",
"Hua Lu"
] | [
"",
"",
"",
"",
""
] | Encoding geospatial data is crucial for enabling machine learning (ML) models
to perform tasks that require spatial reasoning, such as identifying the
topological relationships between two different geospatial objects. However,
existing encoding methods are limited as they are typically customized to
handle only specific types of spatial data, which impedes their applicability
across different downstream tasks where multiple data types coexist. To address
this, we introduce Poly2Vec, an encoding framework that unifies the modeling of
different geospatial objects, including 2D points, polylines, and polygons,
irrespective of the downstream task. We leverage the power of the 2D Fourier
transform to encode useful spatial properties, such as shape and location, from
geospatial objects into fixed-length vectors. These vectors are then inputted
into neural network models for spatial reasoning tasks.This unified approach
eliminates the need to develop and train separate models for each distinct
spatial type. We evaluate Poly2Vec on both synthetic and real datasets of mixed
geometry types and verify its consistent performance across several downstream
spatial reasoning tasks. | cs.LG | [
"cs.LG",
"cs.AI"
] |