query: The query consists of users’ needs, leading to several research questions that span a paragraph. Each candidate passage is an abstract from a scientific paper. The objective of this information retrieval task is to identify the abstract that most effectively meets the user’s needs in the query : I am in the process of developing a deep learning model designed to solve a range of tasks, each with its unique structure. I am operating under the assumption that the structures of these various tasks can be represented by graphs. Additionally, I am working with the constraint of having limited labeled data for each task. My objective is twofold. Firstly, I aim to create a model capable of processing graphical data, learning from diverse tasks, and then adapting to new tasks using a few-shot learning approach. My current strategy involves the use of a graph meta-learning algorithm. Secondly, I have observed that when a sub-task is adversarially designed, it can severely disrupt the meta-learning algorithm. As a result, I need to ensure my model is robust against adversarial attacks. My current strategy to achieve this involves introducing randomized variations in the model architecture, input data, and intermediate latent results to fortify my model. I am also open to other robust training strategies. By integrating these techniques, my goal is to develop a scalable and robust deep learning model that can efficiently adapt to new graph-based tasks and resist adversarial attacks.

positive:
Learning tasks on source code (i.e., formal languages) have been considered
recently, but most work has tried to transfer natural language methods and does
not capitalize on the unique opportunities offered by code's known syntax. For
example, long-range dependencies induced by using the same variable or function
in distant locations are often not considered. We propose to use graphs to
represent both the syntactic and semantic structure of code and use graph-based
deep learning methods to learn to reason over program structures.
  In this work, we present how to construct graphs from source code and how to
scale Gated Graph Neural Networks training to such large graphs. We evaluate
our method on two tasks: VarNaming, in which a network attempts to predict the
name of a variable given its usage, and VarMisuse, in which the network learns
to reason about selecting the correct variable that should be used at a given
program location. Our comparison to methods that use less structured program
representations shows the advantages of modeling known structure, and suggests
that our models learn to infer meaningful names and to solve the VarMisuse task
in many cases. Additionally, our testing showed that VarMisuse identifies a
number of bugs in mature open-source projects.

positive:
Most previous few-shot learning algorithms are based on meta-training with
fake few-shot tasks as training samples, where large labeled base classes are
required. The trained model is also limited by the type of tasks. In this paper
we propose a new paradigm of unsupervised few-shot learning to repair the
deficiencies. We solve the few-shot tasks in two phases: meta-training a
transferable feature extractor via contrastive self-supervised learning and
training a classifier using graph aggregation, self-distillation and manifold
augmentation. Once meta-trained, the model can be used in any type of tasks
with a task-dependent classifier training. Our method achieves state of-the-art
performance in a variety of established few-shot tasks on the standard few-shot
visual classification datasets, with an 8- 28% increase compared to the
available unsupervised few-shot learning methods.

positive:
Label propagation is a powerful and flexible semi-supervised learning
technique on graphs. Neural networks, on the other hand, have proven track
records in many supervised learning tasks. In this work, we propose a training
framework with a graph-regularised objective, namely "Neural Graph Machines",
that can combine the power of neural networks and label propagation. This work
generalises previous literature on graph-augmented training of neural networks,
enabling it to be applied to multiple neural architectures (Feed-forward NNs,
CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the
neural networks to harness both labeled and unlabeled data by: (a) allowing the
network to train using labeled data as in the supervised setting, (b) biasing
the network to learn similar hidden representations for neighboring nodes on a
graph, in the same vein as label propagation. Such architectures with the
proposed objective can be trained efficiently using stochastic gradient descent
and scaled to large graphs, with a runtime that is linear in the number of
edges. The proposed joint training approach convincingly outperforms many
existing methods on a wide range of tasks (multi-label classification on social
graphs, news categorization, document classification and semantic intent
classification), with multiple forms of graph inputs (including graphs with and
without node-level features) and using different types of neural networks.

positive:
In this paper, we focus on the problem of unsupervised image-sentence
matching. Existing research explores to utilize document-level structural
information to sample positive and negative instances for model training.
Although the approach achieves positive results, it introduces a sampling bias
and fails to distinguish instances with high semantic similarity. To alleviate
the bias, we propose a new sampling strategy to select additional
intra-document image-sentence pairs as positive or negative samples.
Furthermore, to recognize the complex pattern in intra-document samples, we
propose a Transformer based model to capture fine-grained features and
implicitly construct a graph for each document, where concepts in a document
are introduced to bridge the representation learning of images and sentences in
the context of a document. Experimental results show the effectiveness of our
approach to alleviate the bias and learn well-aligned multimodal
representations.

positive:
Meta-learning extracts common knowledge from learning different tasks and
uses it for unseen tasks. It can significantly improve tasks that suffer from
insufficient training data, e.g., few shot learning. In most meta-learning
methods, tasks are implicitly related by sharing parameters or optimizer. In
this paper, we show that a meta-learner that explicitly relates tasks on a
graph describing the relations of their output dimensions (e.g., classes) can
significantly improve few shot learning. The graph's structure is usually free
or cheap to obtain but has rarely been explored in previous works. We develop a
novel meta-learner of this type for prototype-based classification, in which a
prototype is generated for each class, such that the nearest neighbor search
among the prototypes produces an accurate classification. The meta-learner,
called "Gated Propagation Network (GPN)", learns to propagate messages between
prototypes of different classes on the graph, so that learning the prototype of
each class benefits from the data of other related classes. In GPN, an
attention mechanism aggregates messages from neighboring classes of each class,
with a gate choosing between the aggregated message and the message from the
class itself. We train GPN on a sequence of tasks from many-shot to few shot
generated by subgraph sampling. During training, it is able to reuse and update
previously achieved prototypes from the memory in a life-long learning cycle.
In experiments, under different training-test discrepancy and test task
generation settings, GPN outperforms recent meta-learning methods on two
benchmark datasets. The code of GPN and dataset generation is available at
https://github.com/liulu112601/Gated-Propagation-Net.

positive:
Deep neural networks excel in regimes with large amounts of data, but tend to
struggle when data is scarce or when they need to adapt quickly to changes in
the task. In response, recent work in meta-learning proposes training a
meta-learner on a distribution of similar tasks, in the hopes of generalization
to novel but related tasks by learning a high-level strategy that captures the
essence of the problem it is asked to solve. However, many recent meta-learning
approaches are extensively hand-designed, either using architectures
specialized to a particular application, or hard-coding algorithmic components
that constrain how the meta-learner solves the task. We propose a class of
simple and generic meta-learner architectures that use a novel combination of
temporal convolutions and soft attention; the former to aggregate information
from past experience and the latter to pinpoint specific pieces of information.
In the most extensive set of meta-learning experiments to date, we evaluate the
resulting Simple Neural AttentIve Learner (or SNAIL) on several
heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement
learning, SNAIL attains state-of-the-art performance by significant margins.

positive:
Meta-reinforcement learning (meta-RL) aims to learn from multiple training
tasks the ability to adapt efficiently to unseen test tasks. Despite the
success, existing meta-RL algorithms are known to be sensitive to the task
distribution shift. When the test task distribution is different from the
training task distribution, the performance may degrade significantly. To
address this issue, this paper proposes Model-based Adversarial
Meta-Reinforcement Learning (AdMRL), where we aim to minimize the worst-case
sub-optimality gap -- the difference between the optimal return and the return
that the algorithm achieves after adaptation -- across all tasks in a family of
tasks, with a model-based approach. We propose a minimax objective and optimize
it by alternating between learning the dynamics model on a fixed task and
finding the adversarial task for the current model -- the task for which the
policy induced by the model is maximally suboptimal. Assuming the family of
tasks is parameterized, we derive a formula for the gradient of the
suboptimality with respect to the task parameters via the implicit function
theorem, and show how the gradient estimator can be efficiently implemented by
the conjugate gradient method and a novel use of the REINFORCE estimator. We
evaluate our approach on several continuous control benchmarks and demonstrate
its efficacy in the worst-case performance over all tasks, the generalization
power to out-of-distribution tasks, and in training and test time sample
efficiency, over existing state-of-the-art meta-RL algorithms.

positive:
This article reviews meta-learning also known as learning-to-learn which
seeks rapid and accurate model adaptation to unseen tasks with applications in
highly automated AI, few-shot learning, natural language processing and
robotics. Unlike deep learning, meta-learning can be applied to few-shot
high-dimensional datasets and considers further improving model generalization
to unseen tasks. Deep learning is focused upon in-sample prediction and
meta-learning concerns model adaptation for out-of-sample prediction.
Meta-learning can continually perform self-improvement to achieve highly
autonomous AI. Meta-learning may serve as an additional generalization block
complementary for original deep learning model. Meta-learning seeks adaptation
of machine learning models to unseen tasks which are vastly different from
trained tasks. Meta-learning with coevolution between agent and environment
provides solutions for complex tasks unsolvable by training from scratch.
Meta-learning methodology covers a wide range of great minds and thoughts. We
briefly introduce meta-learning methodologies in the following categories:
black-box meta-learning, metric-based meta-learning, layered meta-learning and
Bayesian meta-learning framework. Recent applications concentrate upon the
integration of meta-learning with other machine learning framework to provide
feasible integrated problem solutions. We briefly present recent meta-learning
advances and discuss potential future research directions.

positive:
Many few-shot learning approaches have been designed under the meta-learning
framework, which learns from a variety of learning tasks and generalizes to new
tasks. These meta-learning approaches achieve the expected performance in the
scenario where all samples are drawn from the same distributions (i.i.d.
observations). However, in real-world applications, few-shot learning paradigm
often suffers from data shift, i.e., samples in different tasks, even in the
same task, could be drawn from various data distributions. Most existing
few-shot learning approaches are not designed with the consideration of data
shift, and thus show downgraded performance when data distribution shifts.
However, it is non-trivial to address the data shift problem in few-shot
learning, due to the limited number of labeled samples in each task. Targeting
at addressing this problem, we propose a novel metric-based meta-learning
framework to extract task-specific representations and task-shared
representations with the help of knowledge graph. The data shift within/between
tasks can thus be combated by the combination of task-shared and task-specific
representations. The proposed model is evaluated on popular benchmarks and two
constructed new challenging datasets. The evaluation results demonstrate its
remarkable performance.

positive:
Meta-learning has received a tremendous recent attention as a possible
approach for mimicking human intelligence, i.e., acquiring new knowledge and
skills with little or even no demonstration. Most of the existing meta-learning
methods are proposed to tackle few-shot learning problems such as image and
text, in rather Euclidean domain. However, there are very few works applying
meta-learning to non-Euclidean domains, and the recently proposed graph neural
networks (GNNs) models do not perform effectively on graph few-shot learning
problems. Towards this, we propose a novel graph meta-learning framework --
Meta-GNN -- to tackle the few-shot node classification problem in graph
meta-learning settings. It obtains the prior knowledge of classifiers by
training on many similar few-shot learning tasks and then classifies the nodes
from new classes with only few labeled samples. Additionally, Meta-GNN is a
general model that can be straightforwardly incorporated into any existing
state-of-the-art GNN. Our experiments conducted on three benchmark datasets
demonstrate that our proposed approach not only improves the node
classification performance by a large margin on few-shot learning problems in
meta-learning paradigm, but also learns a more general and flexible model for
task adaption.

positive:
Learning from demonstrations has made great progress over the past few years.
However, it is generally data hungry and task specific. In other words, it
requires a large amount of data to train a decent model on a particular task,
and the model often fails to generalize to new tasks that have a different
distribution. In practice, demonstrations from new tasks will be continuously
observed and the data might be unlabeled or only partially labeled. Therefore,
it is desirable for the trained model to adapt to new tasks that have limited
data samples available. In this work, we build an adaptable imitation learning
model based on the integration of Meta-learning and Adversarial Inverse
Reinforcement Learning (Meta-AIRL). We exploit the adversarial learning and
inverse reinforcement learning mechanisms to learn policies and reward
functions simultaneously from available training tasks and then adapt them to
new tasks with the meta-learning framework. Simulation results show that the
adapted policy trained with Meta-AIRL can effectively learn from limited number
of demonstrations, and quickly reach the performance comparable to that of the
experts on unseen tasks.

positive:
Graph Neural Networks (GNNs), a generalization of deep neural networks on
graph data have been widely used in various domains, ranging from drug
discovery to recommender systems. However, GNNs on such applications are
limited when there are few available samples. Meta-learning has been an
important framework to address the lack of samples in machine learning, and in
recent years, researchers have started to apply meta-learning to GNNs. In this
work, we provide a comprehensive survey of different meta-learning approaches
involving GNNs on various graph problems showing the power of using these two
approaches together. We categorize the literature based on proposed
architectures, shared representations, and applications. Finally, we discuss
several exciting future research directions and open problems.

positive:
Graphs are widely used to model the relational structure of data, and the
research of graph machine learning (ML) has a wide spectrum of applications
ranging from drug design in molecular graphs to friendship recommendation in
social networks. Prevailing approaches for graph ML typically require abundant
labeled instances in achieving satisfactory results, which is commonly
infeasible in real-world scenarios since labeled data for newly emerged
concepts (e.g., new categorizations of nodes) on graphs is limited. Though
meta-learning has been applied to different few-shot graph learning problems,
most existing efforts predominately assume that all the data from those seen
classes is gold-labeled, while those methods may lose their efficacy when the
seen data is weakly-labeled with severe label noise. As such, we aim to
investigate a novel problem of weakly-supervised graph meta-learning for
improving the model robustness in terms of knowledge transfer. To achieve this
goal, we propose a new graph meta-learning framework -- Graph Hallucination
Networks (Meta-GHN) in this paper. Based on a new robustness-enhanced episodic
training, Meta-GHN is meta-learned to hallucinate clean node representations
from weakly-labeled data and extracts highly transferable meta-knowledge, which
enables the model to quickly adapt to unseen tasks with few labeled instances.
Extensive experiments demonstrate the superiority of Meta-GHN over existing
graph meta-learning studies on the task of weakly-supervised few-shot node
classification.

positive:
Deep neural networks are highly effective when a large number of labeled
samples are available but fail with few-shot classification tasks. Recently,
meta-learning methods have received much attention, which train a meta-learner
on massive additional tasks to gain the knowledge to instruct the few-shot
classification. Usually, the training tasks are randomly sampled and performed
indiscriminately, often making the meta-learner stuck into a bad local optimum.
Some works in the optimization of deep neural networks have shown that a better
arrangement of training data can make the classifier converge faster and
perform better. Inspired by this idea, we propose an easy-to-hard expert
meta-training strategy to arrange the training tasks properly, where easy tasks
are preferred in the first phase, then, hard tasks are emphasized in the second
phase. A task hardness aware module is designed and integrated into the
training procedure to estimate the hardness of a task based on the
distinguishability of its categories. In addition, we explore multiple hardness
measurements including the semantic relation, the pairwise Euclidean distance,
the Hausdorff distance, and the Hilbert-Schmidt independence criterion.
Experimental results on the miniImageNet and tieredImageNetSketch datasets show
that the meta-learners can obtain better results with our expert training
strategy.

positive:
Modern machine learning systems need to be able to cope with constantly
arriving and changing data. Two main areas of research dealing with such
scenarios are continual learning and data stream mining. Continual learning
focuses on accumulating knowledge and avoiding forgetting, assuming information
once learned should be stored. Data stream mining focuses on adaptation to
concept drift and discarding outdated information, assuming that only the most
recent data is relevant. While these two areas are mainly being developed in
separation, they offer complementary views on the problem of learning from
dynamic data. There is a need for unifying them, by offering architectures
capable of both learning and storing new information, as well as revisiting and
adapting to changes in previously seen concepts. We propose a novel continual
learning approach that can handle both tasks. Our experience replay method is
fueled by a centroid-driven memory storing diverse instances of incrementally
arriving classes. This is enhanced with a reactive subspace buffer that tracks
concept drift occurrences in previously seen classes and adapts clusters
accordingly. The proposed architecture is thus capable of both remembering
valid and forgetting outdated information, offering a holistic framework for
continual learning under concept drift.

positive:
The ability to learn new concepts with small amounts of data is a critical
aspect of intelligence that has proven challenging for deep learning methods.
Meta-learning has emerged as a promising technique for leveraging data from
previous tasks to enable efficient learning of new tasks. However, most
meta-learning algorithms implicitly require that the meta-training tasks be
mutually-exclusive, such that no single model can solve all of the tasks at
once. For example, when creating tasks for few-shot image classification, prior
work uses a per-task random assignment of image classes to N-way classification
labels. If this is not done, the meta-learner can ignore the task training data
and learn a single model that performs all of the meta-training tasks
zero-shot, but does not adapt effectively to new image classes. This
requirement means that the user must take great care in designing the tasks,
for example by shuffling labels or removing task identifying information from
the inputs. In some domains, this makes meta-learning entirely inapplicable. In
this paper, we address this challenge by designing a meta-regularization
objective using information theory that places precedence on data-driven
adaptation. This causes the meta-learner to decide what must be learned from
the task training data and what should be inferred from the task testing input.
By doing so, our algorithm can successfully use data from
non-mutually-exclusive tasks to efficiently adapt to novel tasks. We
demonstrate its applicability to both contextual and gradient-based
meta-learning algorithms, and apply it in practical settings where applying
standard meta-learning has been difficult. Our approach substantially
outperforms standard meta-learning algorithms in these settings.

positive:
Recent approaches in literature have exploited the multi-modal information in
documents (text, layout, image) to serve specific downstream document tasks.
However, they are limited by their - (i) inability to learn cross-modal
representations across text, layout and image dimensions for documents and (ii)
inability to process multi-page documents. Pre-training techniques have been
shown in Natural Language Processing (NLP) domain to learn generic textual
representations from large unlabelled datasets, applicable to various
downstream NLP tasks. In this paper, we propose a multi-task learning-based
framework that utilizes a combination of self-supervised and supervised
pre-training tasks to learn a generic document representation applicable to
various downstream document tasks. Specifically, we introduce Document Topic
Modelling and Document Shuffle Prediction as novel pre-training tasks to learn
rich image representations along with the text and layout representations for
documents. We utilize the Longformer network architecture as the backbone to
encode the multi-modal information from multi-page documents in an end-to-end
fashion. We showcase the applicability of our pre-training framework on a
variety of different real-world document tasks such as document classification,
document information extraction, and document retrieval. We evaluate our
framework on different standard document datasets and conduct exhaustive
experiments to compare performance against various ablations of our framework
and state-of-the-art baselines.

positive:
Few-shot learning aims to adapt knowledge learned from previous tasks to
novel tasks with only a limited amount of labeled data. Research literature on
few-shot learning exhibits great diversity, while different algorithms often
excel at different few-shot learning scenarios. It is therefore tricky to
decide which learning strategies to use under different task conditions.
Inspired by the recent success in Automated Machine Learning literature
(AutoML), in this paper, we present Meta Navigator, a framework that attempts
to solve the aforementioned limitation in few-shot learning by seeking a
higher-level strategy and proffer to automate the selection from various
few-shot learning designs. The goal of our work is to search for good parameter
adaptation policies that are applied to different stages in the network for
few-shot classification. We present a search space that covers many popular
few-shot learning algorithms in the literature and develop a differentiable
searching and decoding algorithm based on meta-learning that supports
gradient-based optimization. We demonstrate the effectiveness of our
searching-based method on multiple benchmark datasets. Extensive experiments
show that our approach significantly outperforms baselines and demonstrates
performance advantages over many state-of-the-art methods. Code and models will
be made publicly available.

positive:
Large-scale pretrained language models have led to dramatic improvements in
text generation. Impressive performance can be achieved by finetuning only on a
small number of instances (few-shot setting). Nonetheless, almost all previous
work simply applies random sampling to select the few-shot training instances.
Little to no attention has been paid to the selection strategies and how they
would affect model performance. In this work, we present a study on training
instance selection in few-shot neural text generation. The selection decision
is made based only on the unlabeled data so as to identify the most worthwhile
data points that should be annotated under some budget of labeling cost. Based
on the intuition that the few-shot training instances should be diverse and
representative of the entire data distribution, we propose a simple selection
strategy with K-means clustering. We show that even with the naive
clustering-based approach, the generation models consistently outperform random
sampling on three text generation tasks: data-to-text generation, document
summarization and question generation. We hope that this work will call for
more attention on this largely unexplored area.

positive:
One of the major prerequisites for any deep learning approach is the
availability of large-scale training data. When dealing with scanned document
images in real world scenarios, the principal information of its content is
stored in the layout itself. In this work, we have proposed an automated deep
generative model using Graph Neural Networks (GNNs) to generate synthetic data
with highly variable and plausible document layouts that can be used to train
document interpretation systems, in this case, specially in digital mailroom
applications. It is also the first graph-based approach for document layout
generation task experimented on administrative document images, in this case,
invoices.

positive:
Document screening is a central task within Evidenced Based Medicine, which
is a clinical discipline that supplements scientific proof to back medical
decisions. Given the recent advances in DL (Deep Learning) methods applied to
Information Retrieval tasks, I propose a DL document classification approach
with BERT or PubMedBERT embeddings and a DL similarity search path using SBERT
embeddings to reduce physicians' tasks of screening and classifying immense
amounts of documents to answer clinical queries. I test and evaluate the
retrieval effectiveness of my DL strategy on the 2017 and 2018 CLEF eHealth
collections. I find that the proposed DL strategy works, I compare it to the
recently successful BM25 plus RM3 model, and conclude that the suggested method
accomplishes advanced retrieval performance in the initial ranking of the
articles with the aforementioned datasets, for the CLEF eHealth Technologically
Assisted Reviews in Empirical Medicine Task.

positive:
Document classification is a challenging task with important applications.
The deep learning approaches to the problem have gained much attention
recently. Despite the progress, the proposed models do not incorporate the
knowledge of the document structure in the architecture efficiently and not
take into account the contexting importance of words and sentences. In this
paper, we propose a new approach based on a combination of convolutional neural
networks, gated recurrent units, and attention mechanisms for document
classification tasks. The main contribution of this work is the use of
convolution layers to extract more meaningful, generalizable and abstract
features by the hierarchical representation. The proposed method in this paper
improves the results of the current attention-based approaches for document
classification.

positive:
This doctoral thesis covers some of my advances in electron microscopy with
deep learning. Highlights include a comprehensive review of deep learning in
electron microscopy; large new electron microscopy datasets for machine
learning, dataset search engines based on variational autoencoders, and
automatic data clustering by t-distributed stochastic neighbour embedding;
adaptive learning rate clipping to stabilize learning; generative adversarial
networks for compressed sensing with spiral, uniformly spaced and other fixed
sparse scan paths; recurrent neural networks trained to piecewise adapt sparse
scan paths to specimens by reinforcement learning; improving signal-to-noise;
and conditional generative adversarial networks for exit wavefunction
reconstruction from single transmission electron micrographs. This thesis adds
to my publications by presenting their relationships, reflections, and holistic
conclusions. This version of my thesis is typeset for online dissemination to
improve readability, whereas the thesis submitted to the University of Warwick
in support of my application for the degree of Doctor of Philosophy in Physics
is typeset for physical printing and binding.

positive:
This paper and accompanying Python and C++ Framework is the product of the
authors perceived problems with narrow (Discrimination based) AI. (Artificial
Intelligence) The Framework attempts to develop a genetic transfer of
experience through potential structural expressions using a common
regulation/exchange value (energy) to create a model whereby neural
architecture and all unit processes are co-dependently developed by genetic and
real time signal processing influences; successful routes are defined by
stability of the spike distribution per epoch which is influenced by
genetically encoded morphological development biases.These principles are aimed
towards creating a diverse and robust network that is capable of adapting to
general tasks by training within a simulation designed for transfer learning to
other mediums at scale.

positive:
Few-Shot Learning is the challenge of training a model with only a small
amount of data. Many solutions to this problem use meta-learning algorithms,
i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger
dataset, we can teach these algorithms to solve new, unseen tasks. This
document reports my work on meta-learning algorithms for Few-Shot Computer
Vision. This work was done during my internship at Sicara, a French company
building image recognition solutions for businesses. It contains: 1. an
extensive review of the state-of-the-art in few-shot computer vision; 2. a
benchmark of meta-learning algorithms for few-shot image classification; 3. the
introduction to a novel meta-learning algorithm for few-shot object detection,
which is still in development.

positive:
We consider the task of few shot link prediction on graphs. The goal is to
learn from a distribution over graphs so that a model is able to quickly infer
missing edges in a new graph after a small amount of training. We show that
current link prediction methods are generally ill-equipped to handle this task.
They cannot effectively transfer learned knowledge from one graph to another
and are unable to effectively learn from sparse samples of edges. To address
this challenge, we introduce a new gradient-based meta learning framework,
Meta-Graph. Our framework leverages higher-order gradients along with a learned
graph signature function that conditionally generates a graph neural network
initialization. Using a novel set of few shot link prediction benchmarks, we
show that Meta-Graph can learn to quickly adapt to a new graph using only a
small sample of true edges, enabling not only fast adaptation but also improved
results at convergence.

positive:
Graph Neural Networks (GNNs) are a powerful tool for machine learning on
graphs.GNNs combine node feature information with the graph structure by
recursively passing neural messages along edges of the input graph. However,
incorporating both graph structure and feature information leads to complex
models, and explaining predictions made by GNNs remains unsolved. Here we
propose GNNExplainer, the first general, model-agnostic approach for providing
interpretable explanations for predictions of any GNN-based model on any
graph-based machine learning task. Given an instance, GNNExplainer identifies a
compact subgraph structure and a small subset of node features that have a
crucial role in GNN's prediction. Further, GNNExplainer can generate consistent
and concise explanations for an entire class of instances. We formulate
GNNExplainer as an optimization task that maximizes the mutual information
between a GNN's prediction and distribution of possible subgraph structures.
Experiments on synthetic and real-world graphs show that our approach can
identify important graph structures as well as node features, and outperforms
baselines by 17.1% on average. GNNExplainer provides a variety of benefits,
from the ability to visualize semantically relevant structures to
interpretability, to giving insights into errors of faulty GNNs.

positive:
Many existing deep learning models are vulnerable to adversarial examples
that are imperceptible to humans. To address this issue, various methods have
been proposed to design network architectures that are robust to one particular
type of adversarial attacks. It is practically impossible, however, to predict
beforehand which type of attacks a machine learn model may suffer from. To
address this challenge, we propose to search for deep neural architectures that
are robust to five types of well-known adversarial attacks using a
multi-objective evolutionary algorithm. To reduce the computational cost, a
normalized error rate of a randomly chosen attack is calculated as the
robustness for each newly generated neural architecture at each generation. All
non-dominated network architectures obtained by the proposed method are then
fully trained against randomly chosen adversarial attacks and tested on two
widely used datasets. Our experimental results demonstrate the superiority of
optimized neural architectures found by the proposed approach over
state-of-the-art networks that are widely used in the literature in terms of
the classification accuracy under different adversarial attacks.

positive:
The ability to learn and adapt in real time is a central feature of
biological systems. Neuromorphic architectures demonstrating such versatility
can greatly enhance our ability to efficiently process information at the edge.
A key challenge, however, is to understand which learning rules are best suited
for specific tasks and how the relevant hyperparameters can be fine-tuned. In
this work, we introduce a conceptual framework in which the learning process is
integrated into the network itself. This allows us to cast meta-learning as a
mathematical optimization problem. We employ DeepHyper, a scalable,
asynchronous model-based search, to simultaneously optimize the choice of
meta-learning rules and their hyperparameters. We demonstrate our approach with
two different datasets, MNIST and FashionMNIST, using a network architecture
inspired by the learning center of the insect brain. Our results show that
optimal learning rules can be dataset-dependent even within similar tasks. This
dependency demonstrates the importance of introducing versatility and
flexibility in the learning algorithms. It also illuminates experimental
findings in insect neuroscience that have shown a heterogeneity of learning
rules within the insect mushroom body.

positive:
Adversarial attacks can affect the performance of existing deep learning
models. With the increased interest in graph based machine learning techniques,
there have been investigations which suggest that these models are also
vulnerable to attacks. In particular, corruptions of the graph topology can
degrade the performance of graph based learning algorithms severely. This is
due to the fact that the prediction capability of these algorithms relies
mostly on the similarity structure imposed by the graph connectivity.
Therefore, detecting the location of the corruption and correcting the induced
errors becomes crucial. There has been some recent work which tackles the
detection problem, however these methods do not address the effect of the
attack on the downstream learning task. In this work, we propose an algorithm
that uses node copying to mitigate the degradation in classification that is
caused by adversarial attacks. The proposed methodology is applied only after
the model for the downstream task is trained and the added computation cost
scales well for large graphs. Experimental results show the effectiveness of
our approach for several real world datasets.

negative:
How to leverage cross-document interactions to improve ranking performance is
an important topic in information retrieval (IR) research. However, this topic
has not been well-studied in the learning-to-rank setting and most of the
existing work still treats each document independently while scoring. The
recent development of deep learning shows strength in modeling complex
relationships across sequences and sets. It thus motivates us to study how to
leverage cross-document interactions for learning-to-rank in the deep learning
framework. In this paper, we formally define the permutation-equivariance
requirement for a scoring function that captures cross-document interactions.
We then propose a self-attention based document interaction network and show
that it satisfies the permutation-equivariant requirement, and can generate
scores for document sets of varying sizes. Our proposed methods can
automatically learn to capture document interactions without any auxiliary
information, and can scale across large document sets. We conduct experiments
on three ranking datasets: the benchmark Web30k, a Gmail search, and a Google
Drive Quick Access dataset. Experimental results show that our proposed methods
are both more effective and efficient than baselines.

negative:
Recent grid-based document representations like BERTgrid allow the
simultaneous encoding of the textual and layout information of a document in a
2D feature map so that state-of-the-art image segmentation and/or object
detection models can be straightforwardly leveraged to extract key information
from documents. However, such methods have not achieved comparable performance
to state-of-the-art sequence- and graph-based methods such as LayoutLM and PICK
yet. In this paper, we propose a new multi-modal backbone network by
concatenating a BERTgrid to an intermediate layer of a CNN model, where the
input of CNN is a document image and the BERTgrid is a grid of word embeddings,
to generate a more powerful grid-based document representation, named
ViBERTgrid. Unlike BERTgrid, the parameters of BERT and CNN in our multimodal
backbone network are trained jointly. Our experimental results demonstrate that
this joint training strategy improves significantly the representation ability
of ViBERTgrid. Consequently, our ViBERTgrid-based key information extraction
approach has achieved state-of-the-art performance on real-world datasets.

negative:
Automatic image cropping techniques are commonly used to enhance the
aesthetic quality of an image; they do it by detecting the most beautiful or
the most salient parts of the image and removing the unwanted content to have a
smaller image that is more visually pleasing. In this thesis, I introduce an
additional dimension to the problem of cropping, semantics. I argue that image
cropping can also enhance the image's relevancy for a given entity by using the
semantic information contained in the image. I call this problem, Semantic
Image Cropping. To support my argument, I provide a new dataset containing 100
images with at least two different entities per image and four ground truth
croppings collected using Amazon Mechanical Turk. I use this dataset to show
that state-of-the-art cropping algorithms that only take into account
aesthetics do not perform well in the problem of semantic image cropping.
Additionally, I provide a new deep learning system that takes not just
aesthetics but also semantics into account to generate image croppings, and I
evaluate its performance using my new semantic cropping dataset, showing that
using the semantic information of an image can help to produce better
croppings.

negative:
Deep learning models on graphs have achieved remarkable performance in
various graph analysis tasks, e.g., node classification, link prediction, and
graph clustering. However, they expose uncertainty and unreliability against
the well-designed inputs, i.e., adversarial examples. Accordingly, a line of
studies has emerged for both attack and defense addressed in different graph
analysis tasks, leading to the arms race in graph adversarial learning. Despite
the booming works, there still lacks a unified problem definition and a
comprehensive review. To bridge this gap, we investigate and summarize the
existing works on graph adversarial learning tasks systemically. Specifically,
we survey and unify the existing works w.r.t. attack and defense in graph
analysis tasks, and give appropriate definitions and taxonomies at the same
time. Besides, we emphasize the importance of related evaluation metrics,
investigate and summarize them comprehensively. Hopefully, our works can
provide a comprehensive overview and offer insights for the relevant
researchers. Latest advances in graph adversarial learning are summarized in
our GitHub repository
https://github.com/EdisonLeeeee/Graph-Adversarial-Learning.

negative:
Alongside the well-publicized accomplishments of deep neural networks there
has emerged an apparent bug in their success on tasks such as object
recognition: with deep models trained using vanilla methods, input images can
be slightly corrupted in order to modify output predictions, even when these
corruptions are practically invisible. This apparent lack of robustness has led
researchers to propose methods that can help to prevent an adversary from
having such capabilities. The state-of-the-art approaches have incorporated the
robustness requirement into the loss function, and the training process
involves taking stochastic gradient descent steps not using original inputs but
on adversarially-corrupted ones. In this paper we propose a multiclass boosting
framework to ensure adversarial robustness. Boosting algorithms are generally
well-suited for adversarial scenarios, as they were classically designed to
satisfy a minimax guarantee. We provide a theoretical foundation for this
methodology and describe conditions under which robustness can be achieved
given a weak training oracle. We show empirically that adversarially-robust
multiclass boosting not only outperforms the state-of-the-art methods, it does
so at a fraction of the training time.

negative:
Graph deep learning models, such as graph convolutional networks (GCN)
achieve remarkable performance for tasks on graph data. Similar to other types
of deep models, graph deep learning models often suffer from adversarial
attacks. However, compared with non-graph data, the discrete features, graph
connections and different definitions of imperceptible perturbations bring
unique challenges and opportunities for the adversarial attacks and defenses
for graph data. In this paper, we propose both attack and defense techniques.
For attack, we show that the discreteness problem could easily be resolved by
introducing integrated gradients which could accurately reflect the effect of
perturbing certain features or edges while still benefiting from the parallel
computations. For defense, we observe that the adversarially manipulated graph
for the targeted attack differs from normal graphs statistically. Based on this
observation, we propose a defense approach which inspects the graph and
recovers the potential adversarial perturbations. Our experiments on a number
of datasets show the effectiveness of the proposed methods.

negative:
The encoder-decoder framework achieves state-of-the-art results in keyphrase
generation (KG) tasks by predicting both present keyphrases that appear in the
source document and absent keyphrases that do not. However, relying solely on
the source document can result in generating uncontrollable and inaccurate
absent keyphrases. To address these problems, we propose a novel graph-based
method that can capture explicit knowledge from related references. Our model
first retrieves some document-keyphrases pairs similar to the source document
from a pre-defined index as references. Then a heterogeneous graph is
constructed to capture relationships of different granularities between the
source document and its references. To guide the decoding process, a
hierarchical attention and copy mechanism is introduced, which directly copies
appropriate words from both the source document and its references based on
their relevance and significance. The experimental results on multiple KG
benchmarks show that the proposed model achieves significant improvements
against other baseline models, especially with regard to the absent keyphrase
prediction.

negative:
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis.

negative:
Document-level relation extraction aims to discover relations between
entities across a whole document. How to build the dependency of entities from
different sentences in a document remains to be a great challenge. Current
approaches either leverage syntactic trees to construct document-level graphs
or aggregate inference information from different sentences. In this paper, we
build cross-sentence dependencies by inferring compositional relations between
inter-sentence mentions. Adopting aggressive linking strategy, intermediate
relations are reasoned on the document-level graphs by mention convolution. We
further notice the generalization problem of NA instances, which is caused by
incomplete annotation and worsened by fully-connected mention pairs. An
improved ranking loss is proposed to attend this problem. Experiments show the
connections between different mentions are crucial to document-level relation
extraction, which enables the model to extract more meaningful higher-level
compositional relations.

negative:
In this paper, we focus on learning structure-aware document representations
from data without recourse to a discourse parser or additional annotations.
Drawing inspiration from recent efforts to empower neural networks with a
structural bias, we propose a model that can encode a document while
automatically inducing rich structural dependencies. Specifically, we embed a
differentiable non-projective parsing algorithm into a neural model and use
attention mechanisms to incorporate the structural biases. Experimental
evaluation across different tasks and datasets shows that the proposed model
achieves state-of-the-art results on document modeling tasks while inducing
intermediate structures which are both interpretable and meaningful.

negative:
An accurate abstractive summary of a document should contain all its salient
information and should be logically entailed by the input document. We improve
these important aspects of abstractive summarization via multi-task learning
with the auxiliary tasks of question generation and entailment generation,
where the former teaches the summarization model how to look for salient
questioning-worthy details, and the latter teaches the model how to rewrite a
summary which is a directed-logical subset of the input document. We also
propose novel multi-task architectures with high-level (semantic)
layer-specific sharing across multiple encoder and decoder layers of the three
tasks, as well as soft-sharing mechanisms (and show performance ablations and
analysis examples of each contribution). Overall, we achieve statistically
significant improvements over the state-of-the-art on both the CNN/DailyMail
and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also
present several quantitative and qualitative analysis studies of our model's
learned saliency and entailment skills.

negative:
Marrying topic models and language models exposes language understanding to a
broader source of document-level context beyond sentences via topics. While
introducing topical semantics in language models, existing approaches
incorporate latent document topic proportions and ignore topical discourse in
sentences of the document. This work extends the line of research by
additionally introducing an explainable topic representation in language
understanding, obtained from a set of key terms correspondingly for each latent
topic of the proportion. Moreover, we retain sentence-topic associations along
with document-topic association by modeling topical discourse for every
sentence in the document. We present a novel neural composite language model
that exploits both the latent and explainable topics along with topical
discourse at sentence-level in a joint learning framework of topic and language
models. Experiments over a range of tasks such as language modeling, word sense
disambiguation, document classification, retrieval and text generation
demonstrate ability of the proposed model in improving language understanding.

negative:
Forum discussion data differ in both structure and properties from generic
form of textual data such as news. Henceforth, summarization techniques should,
in turn, make use of such differences, and craft models that can benefit from
the structural nature of discussion data. In this work, we propose attending to
the beginning of a document, to improve the performance of extractive
summarization models when applied to forum discussion data. Evaluations
demonstrated that with the help of bidirectional attention mechanism, attending
to the beginning of a document (initial comment/post) in a discussion thread,
can introduce a consistent boost in ROUGE scores, as well as introducing a new
State Of The Art (SOTA) ROUGE scores on the forum discussions dataset.
Additionally, we explored whether this hypothesis is extendable to other
generic forms of textual data. We make use of the tendency of introducing
important information early in the text, by attending to the first few
sentences in generic textual data. Evaluations demonstrated that attending to
introductory sentences using bidirectional attention, improves the performance
of extractive summarization models when even applied to more generic form of
textual data.

negative:
Image-to-image translation, which translates input images to a different
domain with a learned one-to-one mapping, has achieved impressive success in
recent years. The success of translation mainly relies on the network
architecture to reserve the structural information while modify the appearance
slightly at the pixel level through adversarial training. Although these
networks are able to learn the mapping, the translated images are predictable
without exclusion. It is more desirable to diversify them using image-to-image
translation by introducing uncertainties, i.e., the generated images hold
potential for variations in colors and textures in addition to the general
similarity to the input images, and this happens in both the target and source
domains. To this end, we propose a novel generative adversarial network (GAN)
based model, InjectionGAN, to learn a many-to-many mapping. In this model, the
input image is combined with latent variables, which comprise of
domain-specific attribute and unspecific random variations. The domain-specific
attribute indicates the target domain of the translation, while the unspecific
random variations introduce uncertainty into the model. A unified framework is
proposed to regroup these two parts and obtain diverse generations in each
domain. Extensive experiments demonstrate that the diverse generations have
high quality for the challenging image-to-image translation tasks where no
pairing information of the training dataset exits. Both quantitative and
qualitative results prove the superior performance of InjectionGAN over the
state-of-the-art approaches.

negative:
The field of meta-learning has seen a dramatic rise in interest in recent
years. In existing meta-learning approaches, learning tasks for training
meta-models are usually collected from public datasets, which brings the
difficulty of obtaining a sufficient number of meta-learning tasks with a large
amount of training data. In this paper, we propose a meta-learning approach
based on randomly generated meta-learning tasks to obtain a parametric loss for
classification learning based on big data. The loss is represented by a deep
neural network, called meta-loss network (MLN). To train the MLN, we construct
a large number of classification learning tasks through randomly generating
training data, validation data, and corresponding ground-truth linear
classifier. Our approach has two advantages. First, sufficient meta-learning
tasks with large number of training data can be obtained easily. Second, the
ground-truth classifier is given, so that the difference between the learned
classifier and the ground-truth model can be measured to reflect the
performance of MLN more precisely than validation accuracy. Based on this
difference, we apply the evolutionary strategy algorithm to find out the
optimal MLN. The resultant MLN not only leads to satisfactory learning effects
on generated linear classifier learning tasks for testing, but also behaves
very well on generated nonlinear classifier learning tasks and various public
classification tasks. Our MLN stably surpass cross-entropy (CE) and mean square
error (MSE) in testing accuracy and generalization ability. These results
illustrate the possibility of achieving satisfactory meta-learning effects
using generated learning tasks.

negative:
The recent explosion of machine learning (ML) and artificial intelligence
(AI) shows great potential in the breakthrough of metal additive manufacturing
(AM) process modeling. However, the success of conventional machine learning
tools in data science is primarily attributed to the unprecedented large amount
of labeled data-sets (big data), which can be either obtained by experiments or
first-principle simulations. Unfortunately, these labeled data-sets are
expensive to obtain in AM due to the high expense of the AM experiments and
prohibitive computational cost of high-fidelity simulations.
  We propose a physics-informed neural network (PINN) framework that fuses both
data and first physical principles, including conservation laws of momentum,
mass, and energy, into the neural network to inform the learning processes. To
the best knowledge of the authors, this is the first application of PINN to
three dimensional AM processes modeling. Besides, we propose a hard-type
approach for Dirichlet boundary conditions (BCs) based on a Heaviside function,
which can not only enforce the BCs but also accelerate the learning process.
The PINN framework is applied to two representative metal manufacturing
problems, including the 2018 NIST AM-Benchmark test series. We carefully assess
the performance of the PINN model by comparing the predictions with available
experimental data and high-fidelity simulation results. The investigations show
that the PINN, owed to the additional physical knowledge, can accurately
predict the temperature and melt pool dynamics during metal AM processes with
only a moderate amount of labeled data-sets. The foray of PINN to metal AM
shows the great potential of physics-informed deep learning for broader
applications to advanced manufacturing.

negative:
We introduce MARGE, a pre-trained sequence-to-sequence model learned with an
unsupervised multi-lingual multi-document paraphrasing objective. MARGE
provides an alternative to the dominant masked language modeling paradigm,
where we self-supervise the reconstruction of target text by retrieving a set
of related texts (in many languages) and conditioning on them to maximize the
likelihood of generating the original. We show it is possible to jointly learn
to do retrieval and reconstruction, given only a random initialization. The
objective noisily captures aspects of paraphrase, translation, multi-document
summarization, and information retrieval, allowing for strong zero-shot
performance on several tasks. For example, with no additional task-specific
training we achieve BLEU scores of up to 35.8 for document translation. We
further show that fine-tuning gives strong performance on a range of
discriminative and generative tasks in many languages, making MARGE the most
generally applicable pre-training method to date.

negative:
Although machine learning models typically experience a drop in performance
on out-of-distribution data, accuracies on in- versus out-of-distribution data
are widely observed to follow a single linear trend when evaluated across a
testbed of models. Models that are more accurate on the out-of-distribution
data relative to this baseline exhibit "effective robustness" and are
exceedingly rare. Identifying such models, and understanding their properties,
is key to improving out-of-distribution performance. We conduct a thorough
empirical investigation of effective robustness during fine-tuning and
surprisingly find that models pre-trained on larger datasets exhibit effective
robustness during training that vanishes at convergence. We study how
properties of the data influence effective robustness, and we show that it
increases with the larger size, more diversity, and higher example difficulty
of the dataset. We also find that models that display effective robustness are
able to correctly classify 10% of the examples that no other current testbed
model gets correct. Finally, we discuss several strategies for scaling
effective robustness to the high-accuracy regime to improve the
out-of-distribution accuracy of state-of-the-art models.

negative:
Many important NLP problems can be posed as dual-sequence or
sequence-to-sequence modeling tasks. Recent advances in building end-to-end
neural architectures have been highly successful in solving such tasks. In this
work we propose a new architecture for dual-sequence modeling that is based on
associative memory. We derive AM-RNNs, a recurrent associative memory (AM)
which augments generic recurrent neural networks (RNN). This architecture is
extended to the Dual AM-RNN which operates on two AMs at once. Our models
achieve very competitive results on textual entailment. A qualitative analysis
demonstrates that long range dependencies between source and target-sequence
can be bridged effectively using Dual AM-RNNs. However, an initial experiment
on auto-encoding reveals that these benefits are not exploited by the system
when learning to solve sequence-to-sequence tasks which indicates that
additional supervision or regularization is needed.

negative:
In this work, a region-based Deep Convolutional Neural Network framework is
proposed for document structure learning. The contribution of this work
involves efficient training of region based classifiers and effective
ensembling for document image classification. A primary level of `inter-domain'
transfer learning is used by exporting weights from a pre-trained VGG16
architecture on the ImageNet dataset to train a document classifier on whole
document images. Exploiting the nature of region based influence modelling, a
secondary level of `intra-domain' transfer learning is used for rapid training
of deep learning models for image segments. Finally, stacked generalization
based ensembling is utilized for combining the predictions of the base deep
neural network models. The proposed method achieves state-of-the-art accuracy
of 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks
set by existing algorithms.

negative:
Despite significant progress on current state-of-the-art image generation
models, synthesis of document images containing multiple and complex object
layouts is a challenging task. This paper presents a novel approach, called
DocSynth, to automatically synthesize document images based on a given layout.
In this work, given a spatial layout (bounding boxes with object categories) as
a reference by the user, our proposed DocSynth model learns to generate a set
of realistic document images consistent with the defined layout. Also, this
framework has been adapted to this work as a superior baseline model for
creating synthetic document image datasets for augmenting real data during
training for document layout analysis tasks. Different sets of learning
objectives have been also used to improve the model performance.
Quantitatively, we also compare the generated results of our model with real
data using standard evaluation metrics. The results highlight that our model
can successfully generate realistic and diverse document images with multiple
objects. We also present a comprehensive qualitative analysis summary of the
different scopes of synthetic image generation tasks. Lastly, to our knowledge
this is the first work of its kind.

negative:
Additive Manufacturing (AM) is a crucial component of the smart industry. In
this paper, we propose an automated quality grading system for the AM process
using a deep convolutional neural network (CNN) model. The CNN model is trained
offline using the images of the internal and surface defects in the
layer-by-layer deposition of materials and tested online by studying the
performance of detecting and classifying the failure in AM process at different
extruder speeds and temperatures. The model demonstrates the accuracy of 94%
and specificity of 96%, as well as above 75% in three classifier measures of
the Fscore, the sensitivity, and precision for classifying the quality of the
printing process in five grades in real-time. The proposed online model adds an
automated, consistent, and non-contact quality control signal to the AM process
that eliminates the manual inspection of parts after they are entirely built.
The quality monitoring signal can also be used by the machine to suggest
remedial actions by adjusting the parameters in real-time. The proposed quality
predictive model serves as a proof-of-concept for any type of AM machines to
produce reliable parts with fewer quality hiccups while limiting the waste of
both time and materials.

negative:
In this paper we address the problem of modeling relational data, which
appear in many applications such as social network analysis, recommender
systems and bioinformatics. Previous studies either consider latent feature
based models but disregarding local structure in the network, or focus
exclusively on capturing local structure of objects based on latent blockmodels
without coupling with latent characteristics of objects. To combine the
benefits of the previous work, we propose a novel model that can simultaneously
incorporate the effect of latent features and covariates if any, as well as the
effect of latent structure that may exist in the data. To achieve this, we
model the relation graph as a function of both latent feature factors and
latent cluster memberships of objects to collectively discover globally
predictive intrinsic properties of objects and capture latent block structure
in the network to improve prediction performance. We also develop an
optimization transfer algorithm based on the generalized EM-style strategy to
learn the latent factors. We prove the efficacy of our proposed model through
the link prediction task and cluster analysis task, and extensive experiments
on the synthetic data and several real world datasets suggest that our proposed
LFBM model outperforms the other state of the art approaches in the evaluated
tasks.

negative:
Deep Learning has revolutionized machine learning and artificial
intelligence, achieving superhuman performance in several standard benchmarks.
It is well-known that deep learning models are inefficient to train; they learn
by processing millions of training data multiple times and require powerful
computational resources to process large batches of data in parallel at the
same time rather than sequentially. Deep learning models also have unexpected
failure modes; they can be fooled into misbehaviour, producing unexpectedly
incorrect predictions.
  In this thesis, we study approaches to improve the training efficiency and
robustness of deep learning models. In the context of learning visual-semantic
embeddings, we find that prioritizing learning on more informative training
data increases convergence speed and improves generalization performance on
test data. We formalize a simple trick called hard negative mining as a
modification to the learning objective function with no computational overhead.
Next, we seek improvements to optimization speed in general-purpose
optimization methods in deep learning. We show that a redundancy-aware
modification to the sampling of training data improves the training speed and
develops an efficient method for detecting the diversity of training signal,
namely, gradient clustering. Finally, we study adversarial robustness in deep
learning and approaches to achieve maximal adversarial robustness without
training with additional data. For linear models, we prove guaranteed maximal
robustness achieved only by appropriate choice of the optimizer,
regularization, or architecture.

negative:
There is an explosion of data, documents, and other content, and people
require tools to analyze and interpret these, tools to turn the content into
information and knowledge. Topic modeling have been developed to solve these
problems. Topic models such as LDA [Blei et. al. 2003] allow salient patterns
in data to be extracted automatically. When analyzing texts, these patterns are
called topics. Among numerous extensions of LDA, few of them can reliably
analyze multiple groups of documents and extract topic similarities. Recently,
the introduction of differential topic modeling (SPDP) [Chen et. al. 2012]
performs uniformly better than many topic models in a discriminative setting.
  There is also a need to improve the sampling speed for topic models. While
some effort has been made for distributed algorithms, there is no work
currently done using graphical processing units (GPU). Note the GPU framework
has already become the most cost-efficient platform for many problems.
  In this thesis, I propose and implement a scalable multi-GPU distributed
parallel framework which approximates SPDP. Through experiments, I have shown
my algorithms have a gain in speed of about 50 times while being almost as
accurate, with only one single cheap laptop GPU. Furthermore, I have shown the
speed improvement is sublinearly scalable when multiple GPUs are used, while
fairly maintaining the accuracy. Therefore on a medium-sized GPU cluster, the
speed improvement could potentially reach a factor of a thousand.
  Note SPDP is just a representative of other extensions of LDA. Although my
algorithm is implemented to work with SPDP, it is designed to be a general
enough to work with other topic models. The speed-up on smaller collections
(i.e., 1000s of documents), means that these more complex LDA extensions could
now be done in real-time, thus opening up a new way of using these LDA models
in industry.

negative:
We propose a Label Propagation based algorithm for weakly supervised text
classification. We construct a graph where each document is represented by a
node and edge weights represent similarities among the documents. Additionally,
we discover underlying topics using Latent Dirichlet Allocation (LDA) and
enrich the document graph by including the topics in the form of additional
nodes. The edge weights between a topic and a text document represent level of
"affinity" between them. Our approach does not require document level
labelling, instead it expects manual labels only for topic nodes. This
significantly minimizes the level of supervision needed as only a few topics
are observed to be enough for achieving sufficiently high accuracy. The Label
Propagation Algorithm is employed on this enriched graph to propagate labels
among the nodes. Our approach combines the advantages of Label Propagation
(through document-document similarities) and Topic Modelling (for minimal but
smart supervision). We demonstrate the effectiveness of our approach on various
datasets and compare with state-of-the-art weakly supervised text
classification approaches.

negative:
Capturing the compositional process which maps the meaning of words to that
of documents is a central challenge for researchers in Natural Language
Processing and Information Retrieval. We introduce a model that is able to
represent the meaning of documents by embedding them in a low dimensional
vector space, while preserving distinctions of word and sentence order crucial
for capturing nuanced semantics. Our model is based on an extended Dynamic
Convolution Neural Network, which learns convolution filters at both the
sentence and document level, hierarchically learning to capture and compose low
level lexical features into high level semantic concepts. We demonstrate the
effectiveness of this model on a range of document modelling tasks, achieving
strong results with no feature engineering and with a more compact model.
Inspired by recent advances in visualising deep convolution networks for
computer vision, we present a novel visualisation technique for our document
networks which not only provides insight into their learning process, but also
can be interpreted to produce a compelling automatic summarisation system for
texts.

negative:
Robustness of neural networks has recently attracted a great amount of
interest. The many investigations in this area lack a precise common foundation
of robustness concepts. Therefore, in this paper, we propose a rigorous and
flexible framework for defining different types of robustness properties for
classifiers. Our robustness concept is based on postulates that robustness of a
classifier should be considered as a property that is independent of accuracy,
and that it should be defined in purely mathematical terms without reliance on
algorithmic procedures for its measurement. We develop a very general
robustness framework that is applicable to any type of classification model,
and that encompasses relevant robustness concepts for investigations ranging
from safety against adversarial attacks to transferability of models to new
domains. For two prototypical, distinct robustness objectives we then propose
new learning approaches based on neural network co-training strategies for
obtaining image classifiers optimized for these respective objectives.

negative:
We present two categories of model-agnostic adversarial strategies that
reveal the weaknesses of several generative, task-oriented dialogue models:
Should-Not-Change strategies that evaluate over-sensitivity to small and
semantics-preserving edits, as well as Should-Change strategies that test if a
model is over-stable against subtle yet semantics-changing modifications. We
next perform adversarial training with each strategy, employing a max-margin
approach for negative generative examples. This not only makes the target
dialogue model more robust to the adversarial inputs, but also helps it perform
significantly better on the original inputs. Moreover, training on all
strategies combined achieves further improvements, achieving a new
state-of-the-art performance on the original task (also verified via human
evaluation). In addition to adversarial training, we also address the
robustness task at the model-level, by feeding it subword units as both inputs
and outputs, and show that the resulting model is equally competitive, requires
only 1/4 of the original vocabulary size, and is robust to one of the
adversarial strategies (to which the original model is vulnerable) even without
adversarial training.

negative:
Few-shot classification refers to learning a classifier for new classes given
only a few examples. While a plethora of models have emerged to tackle it, we
find the procedure and datasets that are used to assess their progress lacking.
To address this limitation, we propose Meta-Dataset: a new benchmark for
training and evaluating models that is large-scale, consists of diverse
datasets, and presents more realistic tasks. We experiment with popular
baselines and meta-learners on Meta-Dataset, along with a competitive method
that we propose. We analyze performance as a function of various
characteristics of test tasks and examine the models' ability to leverage
diverse training sources for improving their generalization. We also propose a
new set of baselines for quantifying the benefit of meta-learning in
Meta-Dataset. Our extensive experimentation has uncovered important research
challenges and we hope to inspire work in these directions.

negative:
Recent breakthroughs in the field of deep learning have led to advancements
in a broad spectrum of tasks in computer vision, audio processing, natural
language processing and other areas. In most instances where these tasks are
deployed in real-world scenarios, the models used in them have been shown to be
susceptible to adversarial attacks, making it imperative for us to address the
challenge of their adversarial robustness. Existing techniques for adversarial
robustness fall into three broad categories: defensive distillation techniques,
adversarial training techniques, and randomized or non-deterministic model
based techniques. In this paper, we propose a novel neural network paradigm
that falls under the category of randomized models for adversarial robustness,
but differs from all existing techniques under this category in that it models
each parameter of the network as a statistical distribution with learnable
parameters. We show experimentally that this framework is highly robust to a
variety of white-box and black-box adversarial attacks, while preserving the
task-specific performance of the traditional neural network model.

negative:
The last decade has witnessed the proliferation of Deep Learning models in
many applications, achieving unrivaled levels of predictive performance.
Unfortunately, the black-box nature of Deep Learning models has posed
unanswered questions about what they learn from data. Certain application
scenarios have highlighted the importance of assessing the bounds under which
Deep Learning models operate, a problem addressed by using assorted approaches
aimed at audiences from different domains. However, as the focus of the
application is placed more on non-expert users, it results mandatory to provide
the means for him/her to trust the model, just like a human gets familiar with
a system or process: by understanding the hypothetical circumstances under
which it fails. This is indeed the angular stone for this research work: to
undertake an adversarial analysis of a Deep Learning model. The proposed
framework constructs counterfactual examples by ensuring their plausibility,
e.g. there is a reasonable probability that a human could generate them without
resorting to a computer program. Therefore, this work must be regarded as
valuable auditing exercise of the usable bounds a certain model is constrained
within, thereby allowing for a much greater understanding of the capabilities
and pitfalls of a model used in a real application. To this end, a Generative
Adversarial Network (GAN) and multi-objective heuristics are used to furnish a
plausible attack to the audited model, efficiently trading between the
confusion of this model, the intensity and plausibility of the generated
counterfactual. Its utility is showcased within a human face classification
task, unveiling the enormous potential of the proposed framework.

negative:
In this article I describe a research agenda for securing machine learning
models against adversarial inputs at test time. This article does not present
results but instead shares some of my thoughts about where I think that the
field needs to go. Modern machine learning works very well on I.I.D. data: data
for which each example is drawn {\em independently} and for which the
distribution generating each example is {\em identical}. When these assumptions
are relaxed, modern machine learning can perform very poorly. When machine
learning is used in contexts where security is a concern, it is desirable to
design models that perform well even when the input is designed by a malicious
adversary. So far most research in this direction has focused on an adversary
who violates the {\em identical} assumption, and imposes some kind of
restricted worst-case distribution shift. I argue that machine learning
security researchers should also address the problem of relaxing the {\em
independence} assumption and that current strategies designed for robustness to
distribution shift will not do so. I recommend {\em dynamic models} that change
each time they are run as a potential solution path to this problem, and show
an example of a simple attack using correlated data that can be mitigated by a
simple dynamic defense. This is not intended as a real-world security measure,
but as a recommendation to explore this research direction and develop more
realistic defenses.

negative:
Most of the textual information available to us are temporally variable. In a
world where information is dynamic, time-stamping them is a very important
task. Documents are a good source of information and are used for many tasks
like, sentiment analysis, classification of reviews etc. The knowledge of
creation date of documents facilitates several tasks like summarization, event
extraction, temporally focused information extraction etc. Unfortunately, for
most of the documents on the web, the time-stamp meta-data is either erroneous
or missing. Thus document dating is a challenging problem which requires
inference over the temporal structure of the document alongside the contextual
information of the document. Prior document dating systems have largely relied
on handcrafted features while ignoring such document-internal structures. In
this paper we propose NeuralDater, a Graph Convolutional Network (GCN) based
document dating approach which jointly exploits syntactic and temporal graph
structures of document in a principled way. We also pointed out some
limitations of NeuralDater and tried to utilize both context and temporal
information in documents in a more flexible and intuitive manner proposing AD3:
Attentive Deep Document Dater, an attention-based document dating system. To
the best of our knowledge these are the first application of deep learning
methods for the task. Through extensive experiments on real-world datasets, we
find that our models significantly outperforms state-of-the-art baselines by a
significant margin.

negative:
In the research field of activity recognition, although it is difficult to
collect a large amount of measured sensor data, there has not been much
discussion about data augmentation (DA). In this study, I propose Octave Mix as
a new synthetic-style DA method for sensor-based activity recognition. Octave
Mix is a simple DA method that combines two types of waveforms by intersecting
low and high frequency waveforms using frequency decomposition. In addition, I
propose a DA ensemble model and its training algorithm to acquire robustness to
the original sensor data while remaining a wide variety of feature
representation. I conducted experiments to evaluate the effectiveness of my
proposed method using four different benchmark datasets of sensing-based
activity recognition. As a result, my proposed method achieved the best
estimation accuracy. Furthermore, I found that ensembling two DA strategies:
Octave Mix with rotation and mixup with rotation, make it possible to achieve
higher accuracy.

negative:
Cross-lingual document representations enable language understanding in
multilingual contexts and allow transfer learning from high-resource to
low-resource languages at the document level. Recently large pre-trained
language models such as BERT, XLM and XLM-RoBERTa have achieved great success
when fine-tuned on sentence-level downstream tasks. It is tempting to apply
these cross-lingual models to document representation learning. However, there
are two challenges: (1) these models impose high costs on long document
processing and thus many of them have strict length limit; (2) model
fine-tuning requires extra data and computational resources, which is not
practical in resource-limited settings. In this work, we address these
challenges by proposing unsupervised Language-Agnostic Weighted Document
Representations (LAWDR). We study the geometry of pre-trained sentence
embeddings and leverage it to derive document representations without
fine-tuning. Evaluated on cross-lingual document alignment, LAWDR demonstrates
comparable performance to state-of-the-art models on benchmark datasets.

negative:
Meta learning algorithms have been widely applied in many tasks for efficient
learning, such as few-shot image classification and fast reinforcement
learning. During meta training, the meta learner develops a common learning
strategy, or experience, from a variety of learning tasks. Therefore, during
meta test, the meta learner can use the learned strategy to quickly adapt to
new tasks even with a few training samples. However, there is still a dark side
about meta learning in terms of reliability and robustness. In particular, is
meta learning vulnerable to adversarial attacks? In other words, would a
well-trained meta learner utilize its learned experience to build wrong or
likely useless knowledge, if an adversary unnoticeably manipulates the given
training set? Without the understanding of this problem, it is extremely risky
to apply meta learning in safety-critical applications. Thus, in this paper, we
perform the initial study about adversarial attacks on meta learning under the
few-shot classification problem. In particular, we formally define key elements
of adversarial attacks unique to meta learning and propose the first attacking
algorithm against meta learning under various settings. We evaluate the
effectiveness of the proposed attacking strategy as well as the robustness of
several representative meta learning algorithms. Experimental results
demonstrate that the proposed attacking strategy can easily break the meta
learner and meta learning is vulnerable to adversarial attacks. The
implementation of the proposed framework will be released upon the acceptance
of this paper.

negative:
One major drawback of state of the art Neural Networks (NN)-based approaches
for document classification purposes is the large number of training samples
required to obtain an efficient classification. The minimum required number is
around one thousand annotated documents for each class. In many cases it is
very difficult, if not impossible, to gather this number of samples in real
industrial processes. In this paper, we analyse the efficiency of NN-based
document classification systems in a sub-optimal training case, based on the
situation of a company document stream. We evaluated three different
approaches, one based on image content and two on textual content. The
evaluation was divided into four parts: a reference case, to assess the
performance of the system in the lab; two cases that each simulate a specific
difficulty linked to document stream processing; and a realistic case that
combined all of these difficulties. The realistic case highlighted the fact
that there is a significant drop in the efficiency of NN-Based document
classification systems. Although they remain efficient for well represented
classes (with an over-fitting of the system for those classes), it is
impossible for them to handle appropriately less well represented classes.
NN-Based document classification systems need to be adapted to resolve these
two problems before they can be considered for use in a company document
stream.

negative:
My overarching research goal is to provide robots with perceptional abilities
that allow interactions with humans in a human-like manner. To develop these
perceptional abilities, I believe that it is useful to study the principles of
the human visual system. I use these principles to develop new computer vision
algorithms and validate their effectiveness in intelligent robotic systems. I
am enthusiastic about this approach as it offers the dual benefit of uncovering
principles inherent in the human visual system, as well as applying these
principles to its artificial counterpart. Fig. 1 contains a depiction of my
research.

negative:
Analyzing the layout of a document to identify headers, sections, tables,
figures etc. is critical to understanding its content. Deep learning based
approaches for detecting the layout structure of document images have been
promising. However, these methods require a large number of annotated examples
during training, which are both expensive and time consuming to obtain. We
describe here a synthetic document generator that automatically produces
realistic documents with labels for spatial positions, extents and categories
of the layout elements. The proposed generative process treats every physical
component of a document as a random variable and models their intrinsic
dependencies using a Bayesian Network graph. Our hierarchical formulation using
stochastic templates allow parameter sharing between documents for retaining
broad themes and yet the distributional characteristics produces visually
unique samples, thereby capturing complex and diverse layouts. We empirically
illustrate that a deep layout detection model trained purely on the synthetic
documents can match the performance of a model that uses real documents.

negative:
Additive Manufacturing (AM) is a manufacturing paradigm that builds
three-dimensional objects from a computer-aided design model by successively
adding material layer by layer. AM has become very popular in the past decade
due to its utility for fast prototyping such as 3D printing as well as
manufacturing functional parts with complex geometries using processes such as
laser metal deposition that would be difficult to create using traditional
machining. As the process for creating an intricate part for an expensive metal
such as Titanium is prohibitive with respect to cost, computational models are
used to simulate the behavior of AM processes before the experimental run.
However, as the simulations are computationally costly and time-consuming for
predicting multiscale multi-physics phenomena in AM, physics-informed
data-driven machine-learning systems for predicting the behavior of AM
processes are immensely beneficial. Such models accelerate not only multiscale
simulation tools but also empower real-time control systems using in-situ data.
In this paper, we design and develop essential components of a scientific
framework for developing a data-driven model-based real-time control system.
Finite element methods are employed for solving time-dependent heat equations
and developing the database. The proposed framework uses extremely randomized
trees - an ensemble of bagged decision trees as the regression algorithm
iteratively using temperatures of prior voxels and laser information as inputs
to predict temperatures of subsequent voxels. The models achieve mean absolute
percentage errors below 1% for predicting temperature profiles for AM
processes. The code is made available for the research community at
https://github.com/paularindam/ml-iter-additive.

negative:
Recent studies have shown that state-of-the-art deep learning models are
vulnerable to the inputs with small perturbations (adversarial examples). We
observe two critical obstacles in adversarial examples: (i) Strong adversarial
attacks (e.g., C&W attack) require manually tuning hyper-parameters and take a
long time to construct an adversarial example, making it impractical to attack
real-time systems; (ii) Most of the studies focus on non-sequential tasks, such
as image classification, yet only a few consider sequential tasks. In this
work, we speed up adversarial attacks, especially on sequential learning tasks.
By leveraging the uncertainty of each task, we directly learn the adaptive
multi-task weightings, without manually searching hyper-parameters. A unified
architecture is developed and evaluated for both non-sequential tasks and
sequential ones. To validate the effectiveness, we take the scene text
recognition task as a case study. To our best knowledge, our proposed method is
the first attempt to adversarial attack for scene text recognition. Adaptive
Attack achieves over 99.9\% success rate with 3-6X speedup compared to
state-of-the-art adversarial attacks.

negative:
Deep learning has emerged as a strong and efficient framework that can be
applied to a broad spectrum of complex learning problems which were difficult
to solve using the traditional machine learning techniques in the past. In the
last few years, deep learning has advanced radically in such a way that it can
surpass human-level performance on a number of tasks. As a consequence, deep
learning is being extensively used in most of the recent day-to-day
applications. However, security of deep learning systems are vulnerable to
crafted adversarial examples, which may be imperceptible to the human eye, but
can lead the model to misclassify the output. In recent times, different types
of adversaries based on their threat model leverage these vulnerabilities to
compromise a deep learning system where adversaries have high incentives.
Hence, it is extremely important to provide robustness to deep learning
algorithms against these adversaries. However, there are only a few strong
countermeasures which can be used in all types of attack scenarios to design a
robust deep learning system. In this paper, we attempt to provide a detailed
discussion on different types of adversarial attacks with various threat models
and also elaborate the efficiency and challenges of recent countermeasures
against them.

negative:
Due to the increasing amount of data on the internet, finding a
highly-informative, low-dimensional representation for text is one of the main
challenges for efficient natural language processing tasks including text
classification. This representation should capture the semantic information of
the text while retaining their relevance level for document classification.
This approach maps the documents with similar topics to a similar space in
vector space representation. To obtain representation for large text, we
propose the utilization of deep Siamese neural networks. To embed document
relevance in topics in the distributed representation, we use a Siamese neural
network to jointly learn document representations. Our Siamese network consists
of two sub-network of multi-layer perceptron. We examine our representation for
the text categorization task on BBC news dataset. The results show that the
proposed representations outperform the conventional and state-of-the-art
representations in the text classification task on this dataset.

negative:
Inspired by the success of self attention mechanism and Transformer
architecture in sequence transduction and image generation applications, we
propose novel self attention-based architectures to improve the performance of
adversarial latent code- based schemes in text generation. Adversarial latent
code-based text generation has recently gained a lot of attention due to their
promising results. In this paper, we take a step to fortify the architectures
used in these setups, specifically AAE and ARAE. We benchmark two latent
code-based methods (AAE and ARAE) designed based on adversarial setups. In our
experiments, the Google sentence compression dataset is utilized to compare our
method with these methods using various objective and subjective measures. The
experiments demonstrate the proposed (self) attention-based models outperform
the state-of-the-art in adversarial code-based text generation.

negative:
Topic models are frequently used in machine learning owing to their high
interpretability and modular structure. However, extending a topic model to
include a supervisory signal, to incorporate pre-trained word embedding vectors
and to include a nonlinear output function is not an easy task because one has
to resort to a highly intricate approximate inference procedure. The present
paper shows that topic modeling with pre-trained word embedding vectors can be
viewed as implementing a neighborhood aggregation algorithm where messages are
passed through a network defined over words. From the network view of topic
models, nodes correspond to words in a document and edges correspond to either
a relationship describing co-occurring words in a document or a relationship
describing the same word in the corpus. The network view allows us to extend
the model to include supervisory signals, incorporate pre-trained word
embedding vectors and include a nonlinear output function in a simple manner.
In experiments, we show that our approach outperforms the state-of-the-art
supervised Latent Dirichlet Allocation implementation in terms of held-out
document classification tasks.

negative:
In this dissertation, I present my work towards exploring temporal
information for better video understanding. Specifically, I have worked on two
problems: action recognition and semantic segmentation. For action recognition,
I have proposed a framework, termed hidden two-stream networks, to learn an
optimal motion representation that does not require the computation of optical
flow. My framework alleviates several challenges faced in video classification,
such as learning motion representations, real-time inference, multi-framerate
handling, generalizability to unseen actions, etc. For semantic segmentation, I
have introduced a general framework that uses video prediction models to
synthesize new training samples. By scaling up the training dataset, my trained
models are more accurate and robust than previous models even without
modifications to the network architectures or objective functions. I believe
videos have much more potential to be mined, and temporal information is one of
the most important cues for machines to perceive the visual world better.

negative:
We present an approach to analyze $C^1(\mathbb{R}^m)$ functions that
addresses limitations present in the Active Subspaces (AS) method of
Constantine et al.(2015; 2014). Under appropriate hypotheses, our Active
Manifolds (AM) method identifies a 1-D curve in the domain (the active
manifold) on which nearly all values of the unknown function are attained, and
which can be exploited for approximation or analysis, especially when $m$ is
large (high-dimensional input space). We provide theorems justifying our AM
technique and an algorithm permitting functional approximation and sensitivity
analysis. Using accessible, low-dimensional functions as initial examples, we
show AM reduces approximation error by an order of magnitude compared to AS, at
the expense of more computation. Following this, we revisit the sensitivity
analysis by Glaws et al. (2017), who apply AS to analyze a magnetohydrodynamic
power generator model, and compare the performance of AM on the same data. Our
analysis provides detailed information not captured by AS, exhibiting the
influence of each parameter individually along an active manifold. Overall, AM
represents a novel technique for analyzing functional models with benefits
including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more
accurate regression than AS (at more computational expense), enabling more
informative sensitivity analysis, and granting accessible visualizations(2-D
plots) of parameter sensitivity along the AM.

negative:
A neuroscience method to understanding the brain is to find and study the
preferred stimuli that highly activate an individual cell or groups of cells.
Recent advances in machine learning enable a family of methods to synthesize
preferred stimuli that cause a neuron in an artificial or biological brain to
fire strongly. Those methods are known as Activation Maximization (AM) or
Feature Visualization via Optimization. In this chapter, we (1) review existing
AM techniques in the literature; (2) discuss a probabilistic interpretation for
AM; and (3) review the applications of AM in debugging and explaining networks.

negative:
Deep learning has demonstrated state-of-the-art performance for a variety of
challenging computer vision tasks. On one hand, this has enabled deep visual
models to pave the way for a plethora of critical applications like disease
prognostics and smart surveillance. On the other, deep learning has also been
found vulnerable to adversarial attacks, which calls for new techniques to
defend deep models against these attacks. Among the attack algorithms, the
black-box schemes are of serious practical concern since they only need
publicly available knowledge of the targeted model. We carefully analyze the
inherent weakness of deep models in black-box settings where the attacker may
develop the attack using a model similar to the targeted model. Based on our
analysis, we introduce a novel gradient regularization scheme that encourages
the internal representation of a deep model to be orthogonal to another, even
if the architectures of the two models are similar. Our unique constraint
allows a model to concomitantly endeavour for higher accuracy while maintaining
near orthogonal alignment of gradients with respect to a reference model.
Detailed empirical study verifies that controlled misalignment of gradients
under our orthogonality objective significantly boosts a model's robustness
against transferable black-box adversarial attacks. In comparison to regular
models, the orthogonal models are significantly more robust to a range of $l_p$
norm bounded perturbations. We verify the effectiveness of our technique on a
variety of large-scale models.

negative:
Deep Learning has become interestingly popular in computer vision, mostly
attaining near or above human-level performance in various vision tasks. But
recent work has also demonstrated that these deep neural networks are very
vulnerable to adversarial examples (adversarial examples - inputs to a model
which are naturally similar to original data but fools the model in classifying
it into a wrong class). Humans are very robust against such perturbations; one
possible reason could be that humans do not learn to classify based on an error
between "target label" and "predicted label" but possibly due to reinforcements
that they receive on their predictions. In this work, we proposed a novel
method to train deep learning models on an image classification task. We used a
reward-based optimization function, similar to the vanilla policy gradient
method used in reinforcement learning, to train our model instead of
conventional cross-entropy loss. An empirical evaluation on the cifar10 dataset
showed that our method learns a more robust classifier than the same model
architecture trained using cross-entropy loss function (on adversarial
training). At the same time, our method shows a better generalization with the
difference in test accuracy and train accuracy $< 2\%$ for most of the time
compared to the cross-entropy one, whose difference most of the time remains $>
2\%$.

negative:
The burgeoning success of deep learning has raised the security and privacy
concerns as more and more tasks are accompanied with sensitive data.
Adversarial attacks in deep learning have emerged as one of the dominating
security threat to a range of mission-critical deep learning systems and
applications. This paper takes a holistic and principled approach to perform
statistical characterization of adversarial examples in deep learning. We
provide a general formulation of adversarial examples and elaborate on the
basic principle for adversarial attack algorithm design. We introduce easy and
hard categorization of adversarial attacks to analyze the effectiveness of
adversarial examples in terms of attack success rate, degree of change in
adversarial perturbation, average entropy of prediction qualities, and fraction
of adversarial examples that lead to successful attacks. We conduct extensive
experimental study on adversarial behavior in easy and hard attacks under deep
learning models with different hyperparameters and different deep learning
frameworks. We show that the same adversarial attack behaves differently under
different hyperparameters and across different frameworks due to the different
features learned under different deep learning model training process. Our
statistical characterization with strong empirical evidence provides a
transformative enlightenment on mitigation strategies towards effective
countermeasures against present and future adversarial attacks.

negative:
Automatic document content processing is affected by artifacts caused by the
shape of the paper, non-uniform and diverse color of lighting conditions.
Fully-supervised methods on real data are impossible due to the large amount of
data needed. Hence, the current state of the art deep learning models are
trained on fully or partially synthetic images. However, document shadow or
shading removal results still suffer because: (a) prior methods rely on
uniformity of local color statistics, which limit their application on
real-scenarios with complex document shapes and textures and; (b) synthetic or
hybrid datasets with non-realistic, simulated lighting conditions are used to
train the models. In this paper we tackle these problems with our two main
contributions. First, a physically constrained learning-based method that
directly estimates document reflectance based on intrinsic image formation
which generalizes to challenging illumination conditions. Second, a new dataset
that clearly improves previous synthetic ones, by adding a large range of
realistic shading and diverse multi-illuminant conditions, uniquely customized
to deal with documents in-the-wild. The proposed architecture works in a
self-supervised manner where only the synthetic texture is used as a weak
training signal (obviating the need for very costly ground truth with
disentangled versions of shading and reflectance). The proposed approach leads
to a significant generalization of document reflectance estimation in real
scenes with challenging illumination. We extensively evaluate on the real
benchmark datasets available for intrinsic image decomposition and document
shadow removal tasks. Our reflectance estimation scheme, when used as a
pre-processing step of an OCR pipeline, shows a 26% improvement of character
error rate (CER), thus, proving the practical applicability.

negative:
The quality of a document is affected by various factors, including
grammaticality, readability, stylistics, and expertise depth, making the task
of document quality assessment a complex one. In this paper, we explore this
task in the context of assessing the quality of Wikipedia articles and academic
papers. Observing that the visual rendering of a document can capture implicit
quality indicators that are not present in the document text --- such as
images, font choices, and visual layout --- we propose a joint model that
combines the text content with a visual rendering of the document for document
quality assessment. Experimental results over two datasets reveal that textual
and visual features are complementary, achieving state-of-the-art results.

negative:
This paper describes an application of reinforcement learning to the mention
detection task. We define a novel action-based formulation for the mention
detection task, in which a model can flexibly revise past labeling decisions by
grouping together tokens and assigning partial mention labels. We devise a
method to create mention-level episodes and we train a model by rewarding
correctly labeled complete mentions, irrespective of the inner structure
created. The model yields results which are on par with a competitive
supervised counterpart while being more flexible in terms of achieving targeted
behavior through reward modeling and generating internal mention structure,
especially on longer mentions.

negative:
Early detection of psychological distress is key to effective treatment.
Automatic detection of distress, such as depression, is an active area of
research. Current approaches utilise vocal, facial, and bodily modalities. Of
these, the bodily modality is the least investigated, partially due to the
difficulty in extracting bodily representations from videos, and partially due
to the lack of viable datasets. Existing body modality approaches use automatic
categorization of expressions to represent body language as a series of
specific expressions, much like words within natural language. In this
dissertation I present a new type of feature, within the body modality, that
represents meta information of gestures, such as speed, and use it to predict a
non-clinical depression label. This differs to existing work by representing
overall behaviour as a small set of aggregated meta features derived from a
person's movement. In my method I extract pose estimation from videos, detect
gestures within body parts, extract meta information from individual gestures,
and finally aggregate these features to generate a small feature vector for use
in prediction tasks. I introduce a new dataset of 65 video recordings of
interviews with self-evaluated distress, personality, and demographic labels.
This dataset enables the development of features utilising the whole body in
distress detection tasks. I evaluate my newly introduced meta-features for
predicting depression, anxiety, perceived stress, somatic stress, five standard
personality measures, and gender. A linear regression based classifier using
these features achieves a 82.70% F1 score for predicting depression within my
novel dataset.

negative:
Document clustering and topic modeling are two closely related tasks which
can mutually benefit each other. Topic modeling can project documents into a
topic space which facilitates effective document clustering. Cluster labels
discovered by document clustering can be incorporated into topic models to
extract local topics specific to each cluster and global topics shared by all
clusters. In this paper, we propose a multi-grain clustering topic model
(MGCTM) which integrates document clustering and topic modeling into a unified
framework and jointly performs the two tasks to achieve the overall best
performance. Our model tightly couples two components: a mixture component used
for discovering latent groups in document collection and a topic model
component used for mining multi-grain topics including local topics specific to
each cluster and global topics shared across clusters.We employ variational
inference to approximate the posterior of hidden variables and learn model
parameters. Experiments on two datasets demonstrate the effectiveness of our
model.

negative:
Document clustering is a text mining technique used to provide better
document search and browsing in digital libraries or online corpora. A lot of
research has been done on biomedical document clustering that is based on using
existing ontology. But, associations and co-occurrences of the medical concepts
are not well represented by using ontology. In this research, a vector
representation of concepts of diseases and similarity measurement between
concepts are proposed. They identify the closest concepts of diseases in the
context of a corpus. Each document is represented by using the vector space
model. A weight scheme is proposed to consider both local content and
associations between concepts. A Self-Organizing Map is used as document
clustering algorithm. The vector projection and visualization features of SOM
enable visualization and analysis of the clusters distributions and
relationships on the two dimensional space. The experimental results show that
the proposed document clustering framework generates meaningful clusters and
facilitate visualization of the clusters based on the concepts of diseases.

negative:
Text Mining is a field that aims at extracting information from textual data.
One of the challenges of such field of study comes from the pre-processing
stage in which a vector (and structured) representation should be extracted
from unstructured data. The common extraction creates large and sparse vectors
representing the importance of each term to a document. As such, this usually
leads to the curse-of-dimensionality that plagues most machine learning
algorithms. To cope with this issue, in this paper we propose a new supervised
feature extraction and reduction algorithm, named DCDistance, that creates
features based on the distance between a document to a representative of each
class label. As such, the proposed technique can reduce the features set in
more than 99% of the original set. Additionally, this algorithm was also
capable of improving the classification accuracy over a set of benchmark
datasets when compared to traditional and state-of-the-art features selection
algorithms.

negative:
Rapid increase of digitized document give birth to high demand of document
image retrieval. While conventional document image retrieval approaches depend
on complex OCR-based text recognition and text similarity detection, this paper
proposes a new content-based approach, in which more attention is paid to
features extraction and fusion. In the proposed approach, multiple features of
document images are extracted by different CNN models. After that, the
extracted CNN features are reduced and fused into weighted average feature.
Finally, the document images are ranked based on feature similarity to a
provided query image. Experimental procedure is performed on a group of
document images that transformed from academic papers, which contain both
English and Chinese document, the results show that the proposed approach has
good ability to retrieve document images with similar text content, and the
fusion of CNN features can effectively improve the retrieval accuracy.

negative:
Convolututional Neural Networks have achieved state of the art in image
classification, object detection and other image related tasks. In this paper I
present another use of CNNs i.e. if given a set of images and then giving a
single test image the network identifies that the test image is part of which
image from the images given before. This is a task somehow similar to measuring
image similarity and can be done using a simple CNN. Doing this task manually
by looping can be quite a time consuming problem and won't be a generalizable
solution. The task is quite similar to doing object detection but for that lots
training data should be given or in the case of sliding window it takes lot of
time and my algorithm can work with much fewer examples, is totally
unsupervised and works much efficiently. Also, I explain that how unsupervised
algorithm like K-Means or supervised algorithm like K-NN are not good enough to
perform this task. The basic idea is that image encodings are collected for
each image from a CNN, when a test image comes it is replaced by a part of
original image, the encoding is generated using the same network, the frobenius
norm is calculated and if it comes under a tolerance level then the test image
is said to be the part of the original image.

negative:
Computation of document image quality metrics often depends upon the
availability of a ground truth image corresponding to the document. This limits
the applicability of quality metrics in applications such as hyperparameter
optimization of image processing algorithms that operate on-the-fly on unseen
documents. This work proposes the use of surrogate models to learn the behavior
of a given document quality metric on existing datasets where ground truth
images are available. The trained surrogate model can later be used to predict
the metric value on previously unseen document images without requiring access
to ground truth images. The surrogate model is empirically evaluated on the
Document Image Binarization Competition (DIBCO) and the Handwritten Document
Image Binarization Competition (H-DIBCO) datasets.

negative:
Machine Learning (ML) and Deep Learning (DL) models have achieved
state-of-the-art performance on multiple learning tasks, from vision to natural
language modelling. With the growing adoption of ML and DL to many areas of
computer science, recent research has also started focusing on the security
properties of these models. There has been a lot of work undertaken to
understand if (deep) neural network architectures are resilient to black-box
adversarial attacks which craft perturbed input samples that fool the
classifier without knowing the architecture used. Recent work has also focused
on the transferability of adversarial attacks and found that adversarial
attacks are generally easily transferable between models, datasets, and
techniques. However, such attacks and their analysis have not been covered from
the perspective of unsupervised machine learning algorithms. In this paper, we
seek to bridge this gap through multiple contributions. We first provide a
strong (iterative) black-box adversarial attack that can craft adversarial
samples which will be incorrectly clustered irrespective of the choice of
clustering algorithm. We choose 4 prominent clustering algorithms, and a
real-world dataset to show the working of the proposed adversarial algorithm.
Using these clustering algorithms we also carry out a simple study of
cross-technique adversarial attack transferability.

negative:
This paper presents results of Document Visual Question Answering Challenge
organized as part of "Text and Documents in the Deep Learning Era" workshop, in
CVPR 2020. The challenge introduces a new problem - Visual Question Answering
on document images. The challenge comprised two tasks. The first task concerns
with asking questions on a single document image. On the other hand, the second
task is set as a retrieval task where the question is posed over a collection
of images. For the task 1 a new dataset is introduced comprising 50,000
questions-answer(s) pairs defined over 12,767 document images. For task 2
another dataset has been created comprising 20 questions over 14,362 document
images which share the same document template.

negative:
Sentence position is a strong feature for news summarization, since the lead
often (but not always) summarizes the key points of the article. In this paper,
we show that recent neural systems excessively exploit this trend, which
although powerful for many inputs, is also detrimental when summarizing
documents where important content should be extracted from later parts of the
article. We propose two techniques to make systems sensitive to the importance
of content in different parts of the article. The first technique employs
'unbiased' data; i.e., randomly shuffled sentences of the source document, to
pretrain the model. The second technique uses an auxiliary ROUGE-based loss
that encourages the model to distribute importance scores throughout a document
by mimicking sentence-level ROUGE scores on the training data. We show that
these techniques significantly improve the performance of a competitive
reinforcement learning based extractive system, with the auxiliary loss being
more powerful than pretraining.

negative:
Graphical elements: particularly tables and figures contain a visual summary
of the most valuable information contained in a document. Therefore,
localization of such graphical objects in the document images is the initial
step to understand the content of such graphical objects or document images. In
this paper, we present a novel end-to-end trainable deep learning based
framework to localize graphical objects in the document images called as
Graphical Object Detection (GOD). Our framework is data-driven and does not
require any heuristics or meta-data to locate graphical objects in the document
images. The GOD explores the concept of transfer learning and domain adaptation
to handle scarcity of labeled training images for graphical object detection
task in the document images. Performance analysis carried out on the various
public benchmark data sets: ICDAR-2013, ICDAR-POD2017,and UNLV shows that our
model yields promising results as compared to state-of-the-art techniques.

negative:
In information retrieval (IR) and related tasks, term weighting approaches
typically consider the frequency of the term in the document and in the
collection in order to compute a score reflecting the importance of the term
for the document. In tasks characterized by the presence of training data (such
as text classification) it seems logical that the term weighting function
should take into account the distribution (as estimated from training data) of
the term across the classes of interest. Although `supervised term weighting'
approaches that use this intuition have been described before, they have failed
to show consistent improvements. In this article we analyse the possible
reasons for this failure, and call consolidated assumptions into question.
Following this criticism we propose a novel supervised term weighting approach
that, instead of relying on any predefined formula, learns a term weighting
function optimised on the training set of interest; we dub this approach
\emph{Learning to Weight} (LTW). The experiments that we run on several
well-known benchmarks, and using different learning methods, show that our
method outperforms previous term weighting approaches in text classification.

negative:
Neural networks with deep architectures have demonstrated significant
performance improvements in computer vision, speech recognition, and natural
language processing. The challenges in information retrieval (IR), however, are
different from these other application areas. A common form of IR involves
ranking of documents--or short passages--in response to keyword-based queries.
Effective IR systems must deal with query-document vocabulary mismatch problem,
by modeling relationships between different query and document terms and how
they indicate relevance. Models should also consider lexical matches when the
query contains rare terms--such as a person's name or a product model
number--not seen during training, and to avoid retrieving semantically related
but irrelevant results. In many real-life IR tasks, the retrieval involves
extremely large collections--such as the document index of a commercial Web
search engine--containing billions of documents. Efficient IR methods should
take advantage of specialized IR data structures, such as inverted index, to
efficiently retrieve from large collections. Given an information need, the IR
system also mediates how much exposure an information artifact receives by
deciding whether it should be displayed, and where it should be positioned,
among other results. Exposure-aware IR systems may optimize for additional
objectives, besides relevance, such as parity of exposure for retrieved items
and content publishers. In this thesis, we present novel neural architectures
and methods motivated by the specific needs and challenges of IR tasks.

negative:
Identity documents recognition is an important sub-field of document
analysis, which deals with tasks of robust document detection, type
identification, text fields recognition, as well as identity fraud prevention
and document authenticity validation given photos, scans, or video frames of an
identity document capture. Significant amount of research has been published on
this topic in recent years, however a chief difficulty for such research is
scarcity of datasets, due to the subject matter being protected by security
requirements. A few datasets of identity documents which are available lack
diversity of document types, capturing conditions, or variability of document
field values. In addition, the published datasets were typically designed only
for a subset of document recognition problems, not for a complex identity
document analysis. In this paper, we present a dataset MIDV-2020 which consists
of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock
identity documents, each with unique text field values and unique artificially
generated faces, with rich annotation. For the presented benchmark dataset
baselines are provided for such tasks as document location and identification,
text fields recognition, and face detection. With 72409 annotated images in
total, to the date of publication the proposed dataset is the largest publicly
available identity documents dataset with variable artificially generated data,
and we believe that it will prove invaluable for advancement of the field of
document analysis and recognition. The dataset is available for download at
ftp://smartengines.com/midv-2020 and http://l3i-share.univ-lr.fr .

negative:
This paper describes the short-term competition on the Components
Segmentation Task of Document Photos that was prepared in the context of the
16th International Conference on Document Analysis and Recognition (ICDAR
2021). This competition aims to bring together researchers working in the field
of identification document image processing and provides them a suitable
benchmark to compare their techniques on the component segmentation task of
document images. Three challenge tasks were proposed entailing different
segmentation assignments to be performed on a provided dataset. The collected
data are from several types of Brazilian ID documents, whose personal
information was conveniently replaced. There were 16 participants whose results
obtained for some or all the three tasks show different rates for the adopted
metrics, like Dice Similarity Coefficient ranging from 0.06 to 0.99. Different
Deep Learning models were applied by the entrants with diverse strategies to
achieve the best results in each of the tasks. Obtained results show that the
currently applied methods for solving one of the proposed tasks (document
boundary detection) are already well established. However, for the other two
challenge tasks (text zone and handwritten sign detection) research and
development of more robust approaches are still required to achieve acceptable
results.

negative:
The Query Focused Text Summarization (QFTS) task aims at building systems
that generate the summary of the text document(s) based on the given query. A
key challenge in addressing this task is the lack of large labeled data for
training the summarization model. In this paper, we address this challenge by
exploring a series of domain adaptation techniques. Given the recent success of
pre-trained transformer models in a wide range of natural language processing
tasks, we utilize such models to generate abstractive summaries for the QFTS
task for both single-document and multi-document scenarios. For domain
adaptation, we apply a variety of techniques using pre-trained
transformer-based summarization models including transfer learning, weakly
supervised learning, and distant supervision. Extensive experiments on six
datasets show that our proposed approach is very effective in generating
abstractive summaries for the QFTS task while setting a new state-of-the-art
result in several datasets across a set of automatic and human evaluation
metrics.

negative:
We present a novel Bayesian topic model for learning discourse-level document
structure. Our model leverages insights from discourse theory to constrain
latent topic assignments in a way that reflects the underlying organization of
document topics. We propose a global model in which both topic selection and
ordering are biased to be similar across a collection of related documents. We
show that this space of orderings can be effectively represented using a
distribution over permutations called the Generalized Mallows Model. We apply
our method to three complementary discourse-level tasks: cross-document
alignment, document segmentation, and information ordering. Our experiments
show that incorporating our permutation-based model in these applications
yields substantial improvements in performance over previously proposed
methods.

negative:
What should I work on first? What can wait until later? Which projects should
I prioritize and which tasks are not worth my time? These are challenging
questions that many people face every day. People's intuitive strategy is to
prioritize their immediate experience over the long-term consequences. This
leads to procrastination and the neglect of important long-term projects in
favor of seemingly urgent tasks that are less important. Optimal gamification
strives to help people overcome these problems by incentivizing each task by a
number of points that communicates how valuable it is in the long-run.
Unfortunately, computing the optimal number of points with standard dynamic
programming methods quickly becomes intractable as the number of a person's
projects and the number of tasks required by each project increase. Here, we
introduce and evaluate a scalable method for identifying which tasks are most
important in the long run and incentivizing each task according to its
long-term value. Our method makes it possible to create to-do list gamification
apps that can handle the size and complexity of people's to-do lists in the
real world.

negative:
With the increase of information, document classification as one of the
methods of text mining, plays vital role in many management and organizing
information. Document classification is the process of assigning a document to
one or more predefined category labels. Document classification includes
different parts such as text processing, term selection, term weighting and
final classification. The accuracy of document classification is very
important. Thus improvement in each part of classification should lead to
better results and higher precision. Term weighting has a great impact on the
accuracy of the classification. Most of the existing weighting methods exploit
the statistical information of terms in documents and do not consider semantic
relations between words. In this paper, an automated document classification
system is presented that uses a novel term weighting method based on semantic
relations between terms. To evaluate the proposed method, three standard
Persian corpuses are used. Experiment results show 2 to 4 percent improvement
in classification accuracy compared with the best previous designed system for
Persian documents.

negative:
This paper addresses the problem of unsupervised clustering which remains one
of the most fundamental challenges in machine learning and artificial
intelligence. We propose the clustered generator model for clustering which
contains both continuous and discrete latent variables. Discrete latent
variables model the cluster label while the continuous ones model variations
within each cluster. The learning of the model proceeds in a unified
probabilistic framework and incorporates the unsupervised clustering as an
inner step without the need for an extra inference model as in existing
variational-based models. The latent variables learned serve as both observed
data embedding or latent representation for data distribution. Our experiments
show that the proposed model can achieve competitive unsupervised clustering
accuracy and can learn disentangled latent representations to generate
realistic samples. In addition, the model can be naturally extended to
per-pixel unsupervised clustering which remains largely unexplored.

negative:
Video feeds are often deliberately used as evidence, as in the case of CCTV
footage; but more often than not, the existence of footage of a supposed event
is perceived as proof of fact in the eyes of the public at large. This reliance
represents a societal vulnerability given the existence of easy-to-use editing
tools and means to fabricate entire video feeds using machine learning. And, as
the recent barrage of fake news and fake porn videos have shown, this isn't
merely an academic concern, it is actively been exploited. I posit that this
exploitation is only going to get more insidious. In this position paper, I
introduce a long term project that aims to mitigate some of the most egregious
forms of manipulation by embedding trustworthy components in the video
transmission chain. Unlike earlier works, I am not aiming to do tamper
detection or other forms of forensics -- approaches I think are bound to fail
in the face of the reality of necessary editing and compression -- instead, the
aim here is to provide a way for the video publisher to prove the integrity of
the video feed as well as make explicit any edits they may have performed. To
do this, I present a novel data structure, a video-edit specification language
and supporting infrastructure that provides end-to-end video provenance, from
the camera sensor to the viewer. I have implemented a prototype of this system
and am in talks with journalists and video editors to discuss the best ways
forward with introducing this idea to the mainstream.

negative:
Named entity linking is to map an ambiguous mention in documents to an entity
in a knowledge base. The named entity linking is challenging, given the fact
that there are multiple candidate entities for a mention in a document. It is
difficult to link a mention when it appears multiple times in a document, since
there are conflicts by the contexts around the appearances of the mention. In
addition, it is difficult since the given training dataset is small due to the
reason that it is done manually to link a mention to its mapping entity. In the
literature, there are many reported studies among which the recent embedding
methods learn vectors of entities from the training dataset at document level.
To address these issues, we focus on how to link entity for mentions at a
sentence level, which reduces the noises introduced by different appearances of
the same mention in a document at the expense of insufficient information to be
used. We propose a new unified embedding method by maximizing the relationships
learned from knowledge graphs. We confirm the effectiveness of our method in
our experimental studies.

negative:
Social and behavioral interventions are a critical tool for governments and
communities to tackle deep-rooted societal challenges such as homelessness,
disease, and poverty. However, real-world interventions are almost always
plagued by limited resources and limited data, which creates a computational
challenge: how can we use algorithmic techniques to enhance the targeting and
delivery of social and behavioral interventions? The goal of my thesis is to
provide a unified study of such questions, collectively considered under the
name "algorithmic social intervention". This proposal introduces algorithmic
social intervention as a distinct area with characteristic technical
challenges, presents my published research in the context of these challenges,
and outlines open problems for future work. A common technical theme is
decision making under uncertainty: how can we find actions which will impact a
social system in desirable ways under limitations of knowledge and resources?
The primary application area for my work thus far is public health, e.g. HIV or
tuberculosis prevention. For instance, I have developed a series of algorithms
which optimize social network interventions for HIV prevention. Two of these
algorithms have been pilot-tested in collaboration with LA-area service
providers for homeless youth, with preliminary results showing substantial
improvement over status-quo approaches. My work also spans other topics in
infectious disease prevention and underlying algorithmic questions in robust
and risk-aware submodular optimization.

negative:
The focus of my PhD thesis is on exploring parallel approaches to efficiently
solve problems modeled by constraints and presenting a new proposal. Current
solvers are very advanced; they are carefully designed to effectively manage
the high-level problems' description and include refined strategies to avoid
useless work. Despite this, finding a solution can take an unacceptable amount
of time. Parallelization can mitigate this problem when the instance of the
problem modeled is large, as it happens in real world problems. It is done by
propagating constraints in parallel and concurrently exploring different parts
of the search space. I am developing on a constraint solver that exploits the
many cores available on Graphics Processing Units (GPU) to speed up the search.

negative:
Document date is essential for many important tasks, such as document
retrieval, summarization, event detection, etc. While existing approaches for
these tasks assume accurate knowledge of the document date, this is not always
available, especially for arbitrary documents from the Web. Document Dating is
a challenging problem which requires inference over the temporal structure of
the document. Prior document dating systems have largely relied on handcrafted
features while ignoring such document internal structures. In this paper, we
propose NeuralDater, a Graph Convolutional Network (GCN) based document dating
approach which jointly exploits syntactic and temporal graph structures of
document in a principled way. To the best of our knowledge, this is the first
application of deep learning for the problem of document dating. Through
extensive experiments on real-world datasets, we find that NeuralDater
significantly outperforms state-of-the-art baseline by 19% absolute (45%
relative) accuracy points.

negative:
It is well-known that machine learning models are vulnerable to small but
cleverly-designed adversarial perturbations that can cause misclassification.
While there has been major progress in designing attacks and defenses for
various adversarial settings, many fundamental and theoretical problems are yet
to be resolved. In this paper, we consider classification in the presence of
$\ell_0$-bounded adversarial perturbations, a.k.a. sparse attacks. This setting
is significantly different from other $\ell_p$-adversarial settings, with
$p\geq 1$, as the $\ell_0$-ball is non-convex and highly non-smooth. Under the
assumption that data is distributed according to the Gaussian mixture model,
our goal is to characterize the optimal robust classifier and the corresponding
robust classification error as well as a variety of trade-offs between
robustness, accuracy, and the adversary's budget. To this end, we develop a
novel classification algorithm called FilTrun that has two main modules:
Filtration and Truncation. The key idea of our method is to first filter out
the non-robust coordinates of the input and then apply a carefully-designed
truncated inner product for classification. By analyzing the performance of
FilTrun, we derive an upper bound on the optimal robust classification error.
We also find a lower bound by designing a specific adversarial strategy that
enables us to derive the corresponding robust classifier and its achieved
error. For the case that the covariance matrix of the Gaussian mixtures is
diagonal, we show that as the input's dimension gets large, the upper and lower
bounds converge; i.e. we characterize the asymptotically-optimal robust
classifier. Throughout, we discuss several examples that illustrate interesting
behaviors such as the existence of a phase transition for adversary's budget
determining whether the effect of adversarial perturbation can be fully
neutralized.

negative:
Due to its decentralized nature, Federated Learning (FL) lends itself to
adversarial attacks in the form of backdoors during training. The goal of a
backdoor is to corrupt the performance of the trained model on specific
sub-tasks (e.g., by classifying green cars as frogs). A range of FL backdoor
attacks have been introduced in the literature, but also methods to defend
against them, and it is currently an open question whether FL systems can be
tailored to be robust against backdoors. In this work, we provide evidence to
the contrary. We first establish that, in the general case, robustness to
backdoors implies model robustness to adversarial examples, a major open
problem in itself. Furthermore, detecting the presence of a backdoor in a FL
model is unlikely assuming first order oracles or polynomial time. We couple
our theoretical results with a new family of backdoor attacks, which we refer
to as edge-case backdoors. An edge-case backdoor forces a model to misclassify
on seemingly easy inputs that are however unlikely to be part of the training,
or test data, i.e., they live on the tail of the input distribution. We explain
how these edge-case backdoors can lead to unsavory failures and may have
serious repercussions on fairness, and exhibit that with careful tuning at the
side of the adversary, one can insert them across a range of machine learning
tasks (e.g., image classification, OCR, text prediction, sentiment analysis).

negative:
With the advent of mobile and hand-held cameras, document images have found
their way into almost every domain. Dewarping of these images for the removal
of perspective distortions and folds is essential so that they can be
understood by document recognition algorithms. For this, we propose an
end-to-end CNN architecture that can produce distortion free document images
from warped documents it takes as input. We train this model on warped document
images simulated synthetically to compensate for lack of enough natural data.
Our method is novel in the use of a bifurcated decoder with shared weights to
prevent intermingling of grid coordinates, in the use of residual networks in
the U-Net skip connections to allow flow of data from different receptive
fields in the model, and in the use of a gated network to help the model focus
on structure and line level detail of the document image. We evaluate our
method on the DocUNet dataset, a benchmark in this domain, and obtain results
comparable to state-of-the-art methods.

negative:
Despite the remarkable performance and generalization levels of deep learning
models in a wide range of artificial intelligence tasks, it has been
demonstrated that these models can be easily fooled by the addition of
imperceptible yet malicious perturbations to natural inputs. These altered
inputs are known in the literature as adversarial examples. In this paper, we
propose a novel probabilistic framework to generalize and extend adversarial
attacks in order to produce a desired probability distribution for the classes
when we apply the attack method to a large number of inputs. This novel attack
paradigm provides the adversary with greater control over the target model,
thereby exposing, in a wide range of scenarios, threats against deep learning
models that cannot be conducted by the conventional paradigms. We introduce
four different strategies to efficiently generate such attacks, and illustrate
our approach by extending multiple adversarial attack algorithms. We also
experimentally validate our approach for the spoken command classification task
and the Tweet emotion classification task, two exemplary machine learning
problems in the audio and text domain, respectively. Our results demonstrate
that we can closely approximate any probability distribution for the classes
while maintaining a high fooling rate and even prevent the attacks from being
detected by label-shift detection methods.

negative:
Deep neural networks (DNNs) are vulnerable to adversarial attack which is
maliciously implemented by adding human-imperceptible perturbation to images
and thus leads to incorrect prediction. Existing studies have proposed various
methods to detect the new adversarial attacks. However, new attack methods keep
evolving constantly and yield new adversarial examples to bypass the existing
detectors. It needs to collect tens of thousands samples to train detectors,
while the new attacks evolve much more frequently than the high-cost data
collection. Thus, this situation leads the newly evolved attack samples to
remain in small scales. To solve such few-shot problem with the evolving
attack, we propose a meta-learning based robust detection method to detect new
adversarial attacks with limited examples. Specifically, the learning consists
of a double-network framework: a task-dedicated network and a master network
which alternatively learn the detection capability for either seen attack or a
new attack. To validate the effectiveness of our approach, we construct the
benchmarks with few-shot-fashion protocols based on three conventional
datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are
conducted on them to verify the superiority of our approach with respect to the
traditional adversarial attack detection methods.

negative:
Additive manufacturing (AM) is growing as fast as anyone can imagine, and it
is now a multi-billion-dollar industry. AM becomes popular in a variety of
sectors, such as automotive, aerospace, biomedical, and pharmaceutical, for
producing parts/ components/ subsystems. However, current AM technologies can
face vast risks of security issues and privacy loss. For the security of AM
process, many researchers are working on the defense mechanism to
countermeasure such security concerns and finding efficient ways to eliminate
those risks. Researchers have also been conducting experiments to establish a
secure framework for the user's privacy and security components. This survey
consists of four sections. In the first section, we will explore the relevant
limitations of additive manufacturing in terms of printing capability,
security, and possible solutions. The second section will present different
kinds of attacks on AM and their effects. The next part will analyze and
discuss the mechanisms and frameworks for access control and authentication for
AM devices. The final section examines the security issues in various
industrial sectors and provides the observations on the security of the
additive manufacturing process.

negative:
I am Joachim Jansen and this is my research summary, part of my application
to the Doctoral Consortium at ICLP'14. I am a PhD student in the Knowledge
Representation and Reasoning (KRR) research group, a subgroup of the
Declarative Languages and Artificial Intelligence (DTAI) group at the
department of Computer Science at KU Leuven. I started my PhD in September
2012. My promotor is prof. dr. ir. Gerda Janssens and my co-promotor is prof.
dr. Marc Denecker. I can be contacted at joachim.jansen@cs.kuleuven.be or at:
Room 01.167 Celestijnenlaan 200A 3001 Heverlee Belgium An extended abstract /
full version of a paper accepted to be presented at the Doctoral Consortium of
the 30th International Conference on Logic Programming (ICLP 2014), July 19-22,
Vienna, Austria

negative:
In this paper, I will discuss the work I am currently doing as a Ph.D.
student at the University of Potsdam, under the tutoring of T. Schaub. I'm
currently looking into action description in ASP. More precisely, my goal is to
explore how to represent actions with durations in ASP, in different contexts.
Right now, I'm focused on Multi-Agent Path Finding (MAPF), looking at how to
represent speeds for different agents and contexts.
  Before tackling duration, I wanted to explore and compare different
representations of action taking in ASP. For this, I started comparing
different simple encodings tackling the MAPF problem. Even in simple code,
choices and assumptions have been made in their creations. The objective of my
work is to present the consequences of those design decisions in terms of
performance and knowledge representation. As far as I know, there is no current
research on this topic.
  Besides that, I'm also exploring different ways to represent duration and to
solve related problems. I planed to compare them the same way I described
before. I also want this to help me find innovative and effective ways to solve
problems with duration.

negative:
"Information Processing" is a recently launched buzzword whose meaning is
vague and obscure even for the majority of its users. The reason for this is
the lack of a suitable definition for the term "information". In my attempt to
amend this bizarre situation, I have realized that, following the insights of
Kolmogorov's Complexity theory, information can be defined as a description of
structures observable in a given data set. Two types of structures could be
easily distinguished in every data set - in this regard, two types of
information (information descriptions) should be designated: physical
information and semantic information. Kolmogorov's theory also posits that the
information descriptions should be provided as a linguistic text structure.
This inevitably leads us to an assertion that information processing has to be
seen as a kind of text processing. The idea is not new - inspired by the
observation that human information processing is deeply rooted in natural
language handling customs, Lotfi Zadeh and his followers have introduced the
so-called "Computing With Words" paradigm. Despite of promotional efforts, the
idea is not taking off yet. The reason - a lack of a coherent understanding of
what should be called "information", and, as a result, misleading research
roadmaps and objectives. I hope my humble attempt to clarify these issues would
be helpful in avoiding common traps and pitfalls.

negative:
Document segmentation is a method of rending the document into distinct
regions. A document is an assortment of information and a standard mode of
conveying information to others. Pursuance of data from documents involves ton
of human effort, time intense and might severely prohibit the usage of data
systems. So, automatic information pursuance from the document has become a big
issue. It is been shown that document segmentation will facilitate to beat such
problems. This paper proposes a new approach to segment and classify the
document regions as text, image, drawings and table. Document image is divided
into blocks using Run length smearing rule and features are extracted from
every blocks. Discipulus tool has been used to construct the Genetic
programming based classifier model and located 97.5% classification accuracy.

negative:
The recent introduction of Find My app by Apple will open a large window of
opportunities for whistleblowers. Based on a short range Bluetooth signals, an
EC P-224 encryption, and an end-to-end encrypted manner using iCloud Keychain,
Find My app is probably the first application broadcasting a large number of
anonymous public key on this scale. Hence, this new Apple's application may
introduce a revolution in secret communication, if we divert it from its
primordial use and transform it into a powerful tool to put in the hands of
whistleblowers. By using Find My app and an entity authentication protocol
based on artificial intelligence, our goal is to make mass surveillance and
kleptographic backdoors ineffective in the lifting of the whistleblower's
anonymity. However, in some case, Find my app may also be a powerful tool in
the hands of dictatorships governments in their fight against whistleblowers
and political adversaries. Thus, the aim of this paper is to show with simple
examples, how these two previous situation can happen.

negative:
Probability theory, epistemically interpreted, provides an excellent, if not
the best available account of inductive reasoning. This is so because there are
general and definite rules for the change of subjective probabilities through
information or experience; induction and belief change are one and same topic,
after all. The most basic of these rules is simply to conditionalize with
respect to the information received; and there are similar and more general
rules. 1 Hence, a fundamental reason for the epistemological success of
probability theory is that there at all exists a well-behaved concept of
conditional probability. Still, people have, and have reasons for, various
concerns over probability theory. One of these is my starting point:
Intuitively, we have the notion of plain belief; we believe propositions2 to be
true (or to be false or neither). Probability theory, however, offers no formal
counterpart to this notion. Believing A is not the same as having probability 1
for A, because probability 1 is incorrigible3; but plain belief is clearly
corrigible. And believing A is not the same as giving A a probability larger
than some 1 - c, because believing A and believing B is usually taken to be
equivalent to believing A & B.4 Thus, it seems that the formal representation
of plain belief has to take a non-probabilistic route. Indeed, representing
plain belief seems easy enough: simply represent an epistemic state by the set
of all propositions believed true in it or, since I make the common assumption
that plain belief is deductively closed, by the conjunction of all propositions
believed true in it. But this does not yet provide a theory of induction, i.e.
an answer to the question how epistemic states so represented are changed
tbrough information or experience. There is a convincing partial answer: if the
new information is compatible with the old epistemic state, then the new
epistemic state is simply represented by the conjunction of the new information
and the old beliefs. This answer is partial because it does not cover the quite
common case where the new information is incompatible with the old beliefs. It
is, however, important to complete the answer and to cover this case, too;
otherwise, we would not represent plain belief as conigible. The crucial
problem is that there is no good completion. When epistemic states are
represented simply by the conjunction of all propositions believed true in it,
the answer cannot be completed; and though there is a lot of fruitful work, no
other representation of epistemic states has been proposed, as far as I know,
which provides a complete solution to this problem. In this paper, I want to
suggest such a solution. In [4], I have more fully argued that this is the only
solution, if certain plausible desiderata are to be satisfied. Here, in section
2, I will be content with formally defining and intuitively explaining my
proposal. I will compare my proposal with probability theory in section 3. It
will turn out that the theory I am proposing is structurally homomorphic to
probability theory in important respects and that it is thus equally easily
implementable, but moreover computationally simpler. Section 4 contains a very
brief comparison with various kinds of logics, in particular conditional logic,
with Shackle's functions of potential surprise and related theories, and with
the Dempster - Shafer theory of belief functions.

negative:
The increasing adoption of Cloud-based data processing and storage poses a
number of privacy issues. Users wish to preserve full control over their
sensitive data and cannot accept it to be fully accessible to an external
storage provider. Previous research in this area was mostly addressed at
techniques to protect data stored on untrusted database servers; however, I
argue that the Cloud architecture presents a number of specific problems and
issues. This dissertation contains a detailed analysis of open issues. To
handle them, I present a novel approach where confidential data is stored in a
highly distributed partitioned database, partly located on the Cloud and partly
on the clients. In my approach, data can be either private or shared; the
latter is shared in a secure manner by means of simple grant-and-revoke
permissions. I have developed a proof-of-concept implementation using an
in-memory RDBMS with row-level data encryption in order to achieve fine-grained
data access control. This type of approach is rarely adopted in conventional
outsourced RDBMSs because it requires several complex steps. Benchmarks of my
proofof-concept implementation show that my approach overcomes most of the
problems.

negative:
We address the problem of solving mixed random linear equations. We have
unlabeled observations coming from multiple linear regressions, and each
observation corresponds to exactly one of the regression models. The goal is to
learn the linear regressors from the observations. Classically, Alternating
Minimization (AM) (which is a variant of Expectation Maximization (EM)) is used
to solve this problem. AM iteratively alternates between the estimation of
labels and solving the regression problems with the estimated labels.
Empirically, it is observed that, for a large variety of non-convex problems
including mixed linear regression, AM converges at a much faster rate compared
to gradient based algorithms. However, the existing theory suggests similar
rate of convergence for AM and gradient based methods, failing to capture this
empirical behavior. In this paper, we close this gap between theory and
practice for the special case of a mixture of $2$ linear regressions. We show
that, provided initialized properly, AM enjoys a \emph{super-linear} rate of
convergence in certain parameter regimes. To the best of our knowledge, this is
the first work that theoretically establishes such rate for AM. Hence, if we
want to recover the unknown regressors upto an error (in $\ell_2$ norm) of
$\epsilon$, AM only takes $\mathcal{O}(\log \log (1/\epsilon))$ iterations.
Furthermore, we compare AM with a gradient based heuristic algorithm
empirically and show that AM dominates in iteration complexity as well as
wall-clock time.