pwc-archive/papers-with-abstracts huggingface dataset:
A Multilingual Study of Compressive Cross-Language Text Summarization
Authors: Elvys Linhares Pontes, Stéphane Huet, Juan-Manuel Torres-Moreno
Date: 2018-10-24 00:00:00
Tasks: Cross-Language Text Summarization, Text Summarization

Cross-Language Text Summarization (CLTS) generates summaries in a language
different from the language of the source documents. Recent methods use
information from both languages to generate summaries with the most informative
sentences. However, these methods have performance that can vary according to
languages, which can reduce the quality of summaries. In this paper, we propose
a compressive framework to generate cross-language summaries. In order to
analyze performance and especially stability, we tested our system and
extractive baselines on a dataset available in four languages (English, French,
Portuguese, and Spanish) to generate English and French summaries. An automatic
evaluation showed that our method outperformed extractive state-of-art CLTS
methods with better and more stable ROUGE scores for all languages.
Learning to Route Efficiently with End-to-End Feedback: The Value of Networked Structure
Authors: Ruihao Zhu, Eytan Modiano
Date: 2018-10-24 00:00:00

We introduce efficient algorithms which achieve nearly optimal regrets for
the problem of stochastic online shortest path routing with end-to-end
feedback. The setting is a natural application of the combinatorial stochastic
bandits problem, a special case of the linear stochastic bandits problem. We
show how the difficulties posed by the large scale action set can be overcome
by the networked structure of the action set. Our approach presents a novel
connection between bandit learning and shortest path algorithms. Our main
contribution is an adaptive exploration algorithm with nearly optimal
instance-dependent regret for any directed acyclic network. We then modify it
so that nearly optimal worst case regret is achieved simultaneously. Driven by
the carefully designed Top-Two Comparison (TTC) technique, the algorithms are
efficiently implementable. We further conduct extensive numerical experiments
to show that our proposed algorithms not only achieve superior regret
performances, but also reduce the runtime drastically.
Streaming Graph Neural Networks
Authors: Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin
Date: 2018-10-24 00:00:00
Tasks: Community Detection, General Classification, Graph Classification, Graph Neural Network, Link Prediction, Node Classification

Graphs are essential representations of many real-world data such as social
networks. Recent years have witnessed the increasing efforts made to extend the
neural network models to graph-structured data. These methods, which are
usually known as the graph neural networks, have been applied to advance many
graphs related tasks such as reasoning dynamics of the physical system, graph
classification, and node classification. Most of the existing graph neural
network models have been designed for static graphs, while many real-world
graphs are inherently dynamic. For example, social networks are naturally
evolving as new users joining and new relations being created. Current graph
neural network models cannot utilize the dynamic information in dynamic graphs.
However, the dynamic information has been proven to enhance the performance of
many graph analytic tasks such as community detection and link prediction.
Hence, it is necessary to design dedicated graph neural networks for dynamic
graphs. In this paper, we propose DGNN, a new {\bf D}ynamic {\bf G}raph {\bf
N}eural {\bf N}etwork model, which can model the dynamic information as the
graph evolving. In particular, the proposed framework can keep updating node
information by capturing the sequential information of edges (interactions),
the time intervals between edges and information propagation coherently.
Experimental results on various dynamic graphs demonstrate the effectiveness of
the proposed framework.
Robust Adversarial Learning via Sparsifying Front Ends
Authors: Soorya Gopalakrishnan, Zhinus Marzi, Metehan Cekic, Upamanyu Madhow, Ramtin Pedarsani
Date: 2018-10-24 00:00:00

It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks. In this paper, we take a bottom-up signal processing perspective to this problem and show that a systematic exploitation of sparsity in natural data is a promising tool for defense. For linear classifiers, we show that a sparsifying front end is provably effective against $\ell_{\infty}$-bounded attacks, reducing output distortion due to the attack by a factor of roughly $K/N$ where $N$ is the data dimension and $K$ is the sparsity level. We then extend this concept to deep networks, showing that a "locally linear" model can be used to develop a theoretical foundation for crafting attacks and defenses. We also devise attacks based on the locally linear model that outperform the well-known FGSM attack. We supplement our theoretical results with experiments on the MNIST and CIFAR-10 datasets, showing the efficacy of the proposed sparsity-based defense schemes.
Continual Classification Learning Using Generative Models
Authors: Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, Alexandros Kalousis
Date: 2018-10-24 00:00:00
Tasks: Classification, Continual Learning, General Classification

Continual learning is the ability to sequentially learn over time by
accommodating knowledge while retaining previously learned experiences. Neural
networks can learn multiple tasks when trained on them jointly, but cannot
maintain performance on previously learned tasks when tasks are presented one
at a time. This problem is called catastrophic forgetting. In this work, we
propose a classification model that learns continuously from sequentially
observed tasks, while preventing catastrophic forgetting. We build on the
lifelong generative capabilities of [10] and extend it to the classification
setting by deriving a new variational bound on the joint log likelihood, $\log
p(x; y)$.
Loop corrections in spin models through density consistency
Authors: Alfredo Braunstein, Giovanni Catania, Luca Dall'Asta
Date: 2018-10-24 00:00:00

Computing marginal distributions of discrete or semidiscrete Markov random fields (MRFs) is a fundamental, generally intractable problem with a vast number of applications in virtually all fields of science. We present a new family of computational schemes to approximately calculate the marginals of discrete MRFs. This method shares some desirable properties with belief propagation, in particular, providing exact marginals on acyclic graphs, but it differs with the latter in that it includes some loop corrections; i.e., it takes into account correlations coming from all cycles in the factor graph. It is also similar to the adaptive Thouless-Anderson-Palmer method, but it differs with the latter in that the consistency is not on the first two moments of the distribution but rather on the value of its density on a subset of values. The results on finite-dimensional Isinglike models show a significant improvement with respect to the Bethe-Peierls (tree) approximation in all cases and with respect to the plaquette cluster variational method approximation in many cases. In particular, for the critical inverse temperature $\beta_{c}$ of the homogeneous hypercubic lattice, the expansion of $\left(d\beta_{c}\right)^{-1}$ around $d=\infty$ of the proposed scheme is exact up to the $d^{-4}$ order, whereas the two latter are exact only up to the $d^{-2}$ order.
The speaker-independent lipreading play-off; a survey of lipreading machines
Authors: Jake Burton, David Frank, Madhi Saleh, Nassir Navab, Helen L. Bear
Date: 2018-10-24 00:00:00
Tasks: General Classification, Lipreading

Lipreading is a difficult gesture classification task. One problem in
computer lipreading is speaker-independence. Speaker-independence means to
achieve the same accuracy on test speakers not included in the training set as
speakers within the training set. Current literature is limited on
speaker-independent lipreading, the few independent test speaker accuracy
scores are usually aggregated within dependent test speaker accuracies for an
averaged performance. This leads to unclear independent results. Here we
undertake a systematic survey of experiments with the TCD-TIMIT dataset using
both conventional approaches and deep learning methods to provide a series of
wholly speaker-independent benchmarks and show that the best
speaker-independent machine scores 69.58% accuracy with CNN features and an SVM
classifier. This is less than state of the art speaker-dependent lipreading
machines, but greater than previously reported in independence experiments.
Inverse reinforcement learning for video games
Authors: Aaron Tucker, Adam Gleave, Stuart Russell
Date: 2018-10-24 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning achieves superhuman performance in a range of
video game environments, but requires that a designer manually specify a reward
function. It is often easier to provide demonstrations of a target behavior
than to design a reward function describing that behavior. Inverse
reinforcement learning (IRL) algorithms can infer a reward from demonstrations
in low-dimensional continuous control environments, but there has been little
work on applying IRL to high-dimensional video games. In our CNN-AIRL baseline,
we modify the state-of-the-art adversarial IRL (AIRL) algorithm to use CNNs for
the generator and discriminator. To stabilize training, we normalize the reward
and increase the size of the discriminator training dataset. We additionally
learn a low-dimensional state representation using a novel autoencoder
architecture tuned for video game environments. This embedding is used as input
to the reward network, improving the sample efficiency of expert
demonstrations. Our method achieves high-level performance on the simple
Catcher video game, substantially outperforming the CNN-AIRL baseline. We also
score points on the Enduro Atari racing game, but do not match expert
performance, highlighting the need for further work.
Clinical Concept Extraction with Contextual Word Embedding
Authors: Henghui Zhu, Ioannis Ch. Paschalidis, Amir Tahmasebi
Date: 2018-10-24 00:00:00
Tasks: Clinical Concept Extraction

Automatic extraction of clinical concepts is an essential step for turning
the unstructured data within a clinical note into structured and actionable
information. In this work, we propose a clinical concept extraction model for
automatic annotation of clinical problems, treatments, and tests in clinical
notes utilizing domain-specific contextual word embedding. A contextual word
embedding model is first trained on a corpus with a mixture of clinical reports
and relevant Wikipedia pages in the clinical domain. Next, a bidirectional
LSTM-CRF model is trained for clinical concept extraction using the contextual
word embedding model. We tested our proposed model on the I2B2 2010 challenge
dataset. Our proposed model achieved the best performance among reported
baseline models and outperformed the state-of-the-art models by 3.4% in terms
of F1-score.
Multimodal Polynomial Fusion for Detecting Driver Distraction
Authors: Yulun Du, Chirag Raman, Alan W. black, Louis-Philippe Morency, Maxine Eskenazi
Date: 2018-10-24 00:00:00

Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.
Fast and accurate object detection in high resolution 4K and 8K video using GPUs
Authors: Vít Růžička, Franz Franchetti
Date: 2018-10-24 00:00:00
Tasks: 4k, 8k, object-detection, Object Detection, Real-Time Object Detection

Machine learning has celebrated a lot of achievements on computer vision
tasks such as object detection, but the traditionally used models work with
relatively low resolution images. The resolution of recording devices is
gradually increasing and there is a rising need for new methods of processing
high resolution data. We propose an attention pipeline method which uses two
staged evaluation of each image or video frame under rough and refined
resolution to limit the total number of necessary evaluations. For both stages,
we make use of the fast object detection model YOLO v2. We have implemented our
model in code, which distributes the work across GPUs. We maintain high
accuracy while reaching the average performance of 3-6 fps on 4K video and 2
fps on 8K video.
Segmentation Analysis in Human Centric Cyber-Physical Systems using Graphical Lasso
Authors: Hari Prasanna Das, Ioannis C. Konstantakopoulos, Aummul Baneen Manasawala, Tanya Veeravalli, Huihan Liu, Costas J. Spanos
Date: 2018-10-24 00:00:00
Tasks: Decision Making, Segmentation

A generalized gamification framework is introduced as a form of smart
infrastructure with potential to improve sustainability and energy efficiency
by leveraging humans-in-the-loop strategy. The proposed framework enables a
Human-Centric Cyber-Physical System using an interface to allow building
managers to interact with occupants. The interface is designed for occupant
engagement-integration supporting learning of their preferences over resources
in addition to understanding how preferences change as a function of external
stimuli such as physical control, time or incentives. Towards intelligent and
autonomous incentive design, a noble statistical learning algorithm performing
occupants energy usage behavior segmentation is proposed. We apply the proposed
algorithm, Graphical Lasso, on energy resource usage data by the occupants to
obtain feature correlations--dependencies. Segmentation analysis results in
characteristic clusters demonstrating different energy usage behaviors. The
features--factors characterizing human decision-making are made explainable.
A mathematical theory of semantic development in deep neural networks
Authors: Andrew M. Saxe, James L. McClelland, Surya Ganguli
Date: 2018-10-23 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

An extensive body of empirical research has revealed remarkable regularities
in the acquisition, organization, deployment, and neural representation of
human semantic knowledge, thereby raising a fundamental conceptual question:
what are the theoretical principles governing the ability of neural networks to
acquire, organize, and deploy abstract knowledge by integrating across many
individual experiences? We address this question by mathematically analyzing
the nonlinear dynamics of learning in deep linear networks. We find exact
solutions to this learning dynamics that yield a conceptual explanation for the
prevalence of many disparate phenomena in semantic cognition, including the
hierarchical differentiation of concepts through rapid developmental
transitions, the ubiquity of semantic illusions between such transitions, the
emergence of item typicality and category coherence as factors controlling the
speed of semantic processing, changing patterns of inductive projection over
development, and the conservation of semantic similarity in neural
representations across species. Thus, surprisingly, our simple neural model
qualitatively recapitulates many diverse regularities underlying semantic
development, while providing analytic insight into how the statistical
structure of an environment can interact with nonlinear deep learning dynamics
to give rise to these regularities.
Convolutional Set Matching for Graph Similarity
Authors: Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang
Date: 2018-10-23 00:00:00
Tasks: Graph Similarity, set matching

We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural
Networks) for predicting the similarity score between two graphs. As the core
operation of graph similarity search, pairwise graph similarity computation is
a challenging problem due to the NP-hard nature of computing many graph
distance/similarity metrics. We demonstrate our model using the Graph Edit
Distance (GED) as the example metric. Experiments on three real graph datasets
demonstrate that our model achieves the state-of-the-art performance on graph
similarity search.
Classifying and Visualizing Emotions with Emotional DAN
Authors: Ivona Tautkute, Tomasz Trzcinski
Date: 2018-10-23 00:00:00
Tasks: Classification, Emotion Classification, Emotion Recognition, General Classification

Classification of human emotions remains an important and challenging task
for many computer vision algorithms, especially in the era of humanoid robots
which coexist with humans in their everyday life. Currently proposed methods
for emotion recognition solve this task using multi-layered convolutional
networks that do not explicitly infer any facial features in the classification
phase. In this work, we postulate a fundamentally different approach to solve
emotion recognition task that relies on incorporating facial landmarks as a
part of the classification loss function. To that end, we extend a recently
proposed Deep Alignment Network (DAN) with a term related to facial features.
Thanks to this simple modification, our model called EmotionalDAN is able to
outperform state-of-the-art emotion classification methods on two challenging
benchmark dataset by up to 5%. Furthermore, we visualize image regions analyzed
by the network when making a decision and the results indicate that our
EmotionalDAN model is able to correctly identify facial landmarks responsible
for expressing the emotions.
Using the Fast Fourier Transform in Binding Free Energy Calculations
Authors: Trung Hai Nguyen, Huan-Xiang Zhou, David D. L. Minh
Date: 2017-07-27 00:00:00
Tasks: Molecular Docking

According to implicit ligand theory, the standard binding free energy is an
exponential average of the binding potential of mean force (BPMF), an
exponential average of the interaction energy between the ligand apo ensemble
and a rigid receptor. Here, we use the Fast Fourier Transform (FFT) to
efficiently estimate BPMFs by calculating interaction energies as rigid ligand
configurations from the apo ensemble are discretely translated across rigid
receptor conformations. Results for standard binding free energies between T4
lysozyme and 141 small organic molecules are in good agreement with previous
alchemical calculations based on (1) a flexible complex (R ~ 0.9 for 24
systems) and (2) flexible ligand with multiple rigid receptor configurations (R
~ 0.8 for 141 systems). While the FFT is routinely used for molecular docking,
to our knowledge this is the first time that the algorithm has been used for
rigorous binding free energy calculations.
Quantum reinforcement learning
Authors: Daoyi Dong, Chunlin Chen, Hanxiong Li, Tzyh-Jong Tarn
Date: 2008-10-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The key approaches for machine learning, especially learning in unknown
probabilistic environments are new representations and computation mechanisms.
In this paper, a novel quantum reinforcement learning (QRL) method is proposed
by combining quantum theory and reinforcement learning (RL). Inspired by the
state superposition principle and quantum parallelism, a framework of value
updating algorithm is introduced. The state (action) in traditional RL is
identified as the eigen state (eigen action) in QRL. The state (action) set can
be represented with a quantum superposition state and the eigen state (eigen
action) can be obtained by randomly observing the simulated quantum state
according to the collapse postulate of quantum measurement. The probability of
the eigen action is determined by the probability amplitude, which is
parallelly updated according to rewards. Some related characteristics of QRL
such as convergence, optimality and balancing between exploration and
exploitation are also analyzed, which shows that this approach makes a good
tradeoff between exploration and exploitation using the probability amplitude
and can speed up learning through the quantum parallelism. To evaluate the
performance and practicability of QRL, several simulated experiments are given
and the results demonstrate the effectiveness and superiority of QRL algorithm
for some complex problems. The present work is also an effective exploration on
the application of quantum computation to artificial intelligence.
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Authors: Sebastian Glatz, Julien N. P. Martel, Raphaela Kreiser, Ning Qiao, Yulia Sandamirskaya
Date: 2018-10-25 00:00:00
Tasks: Efficient Neural Network

Neuromorphic computing is a new paradigm for design of both the computing
hardware and algorithms inspired by biological neural networks. The event-based
nature and the inherent parallelism make neuromorphic computing a promising
paradigm for building efficient neural network based architectures for control
of fast and agile robots. In this paper, we present a spiking neural network
architecture that uses sensory feedback to control rotational velocity of a
robotic vehicle. When the velocity reaches the target value, the mapping from
the target velocity of the vehicle to the correct motor command, both
represented in the spiking neural network on the neuromorphic device, is
autonomously stored on the device using on-chip plastic synaptic weights. We
validate the controller using a wheel motor of a miniature mobile vehicle and
inertia measurement unit as the sensory feedback and demonstrate online
learning of a simple 'inverse model' in a two-layer spiking neural network on
the neuromorphic chip. The prototype neuromorphic device that features 256
spiking neurons allows us to realise a simple proof of concept architecture for
the purely neuromorphic motor control and learning. The architecture can be
easily scaled-up if a larger neuromorphic device is available.
A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks
Authors: Arman Hasanzadeh, Xi Liu, Nick Duffield, Krishna R. Narayanan, Byron Chigoy
Date: 2017-11-19 00:00:00
Tasks: Clustering, Management, Prediction, Time Series, Time Series Analysis, Traffic Prediction

Accurate real-time traffic prediction has a key role in traffic management
strategies and intelligent transportation systems. Building a prediction model
for transportation networks is challenging because spatio-temporal dependencies
of traffic data in different roads are complex and the graph constructed from
road networks is very large. Thus it is computationally expensive to build and
run a prediction algorithm for the whole network. In this paper, we propose a
method to address these challenges. First, we introduce a novel spatio-temporal
clustering algorithm in order to split the large graph into multiple connected
disjoint subgraphs. Then within each subgraph, we propose to use a Graph Signal
Processing (GSP) approach to decouple spatial dependencies and obtain
independent time series in graph frequency domain. We make predictions along
independent graph frequencies using adaptive ARMA models and later transform
the predicted time series along each graph frequency to the subgraph vertex
domain. Evaluation of our model on an extensive dataset of fine-grained highway
travel times in the Dallas-Fort Worth area shows substantial improvement
achieved by our proposed method compared to existing methods.