pwc-archive/papers-with-abstracts huggingface dataset:
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Authors: Niklas Pfister, Stefan Bauer, Jonas Peters
Date: 2018-10-28 00:00:00
Tasks: Causal Inference, Model Selection

Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well established approaches focusing solely on predictive performance, especially for out-of-sample generalization.
Learning with Analytical Models
Authors: Huda Ibeid, Siping Meng, Oliver Dobon, Luke Olson, William Gropp
Date: 2018-10-28 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

To understand and predict the performance of scientific applications, several
analytical and machine learning approaches have been proposed, each having its
advantages and disadvantages. In this paper, we propose and validate a hybrid
approach for performance modeling and prediction, which combines analytical and
machine learning models. The proposed hybrid model aims to minimize prediction
cost while providing reasonable prediction accuracy. Our validation results
show that the hybrid model is able to learn and correct the analytical models
to better match the actual performance. Furthermore, the proposed hybrid model
improves the prediction accuracy in comparison to pure machine learning
techniques while using small training datasets, thus making it suitable for
hardware and workload changes.
Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods
Authors: Vladislav Ostankovich, Geesara Prathap, Ilya Afanasyev
Date: 2018-10-28 00:00:00
Tasks: blind source separation

Human heartbeat can be measured using several different ways appropriately
based on the patient condition which includes contact base such as measured by
using instruments and non-contact base such as computer vision assisted
techniques. Non-contact based approached are getting popular due to those
techniques are capable of mitigating some of the limitations of contact-based
techniques especially in clinical section. However, existing vision guided
approaches are not able to prove high accurate result due to various reason
such as the property of camera, illumination changes, skin tones in face image,
etc. We propose a technique that uses video as an input and returns pulse rate
in output. Initially, key point detection is carried out on two facial
subregions: forehead and nose-mouth. After removing unstable features, the
temporal filtering is applied to isolate frequencies of interest. Then four
component analysis methods are employed in order to distinguish the
cardiovascular pulse signal from extraneous noise caused by respiration,
vestibular activity and other changes in facial expression. Afterwards,
proposed peak detection technique is applied for each component which extracted
from one of the four different component selection algorithms. This will enable
to locate the positions of peaks in each component. Proposed automatic
components selection technique is employed in order to select an optimal
component which will be used to calculate the heartbeat. Finally, we conclude
with a comparison of four component analysis methods (PCA, FastICA, JADE,
SHIBBS), processing face video datasets of fifteen volunteers with verification
by an ECG/EKG Workstation as a ground truth.
Learning Sparse Neural Networks via Sensitivity-Driven Regularization
Authors: Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Sensitivity

The ever-increasing number of parameters in deep neural networks poses
challenges for memory-limited applications. Regularize-and-prune methods aim at
meeting these challenges by sparsifying the network weights. In this context we
quantify the output sensitivity to the parameters (i.e. their relevance to the
network output) and introduce a regularization term that gradually lowers the
absolute value of parameters with low sensitivity. Thus, a very large fraction
of the parameters approach zero and are eventually set to zero by simple
thresholding. Our method surpasses most of the recent techniques both in terms
of sparsity and error rates. In some cases, the method reaches twice the
sparsity obtained by other techniques at equal error rates.
Machine Learning in Network Centrality Measures: Tutorial and Outlook
Authors: Felipe Grando, Lisando Z. Granville, Luis C. Lamb
Date: 2018-10-28 00:00:00
Tasks: BIG-bench Machine Learning

Complex networks are ubiquitous to several Computer Science domains.
Centrality measures are an important analysis mechanism to uncover vital
elements of complex networks. However, these metrics have high computational
costs and requirements that hinder their applications in large real-world
networks. In this tutorial, we explain how the use of neural network learning
algorithms can render the application of the metrics in complex networks of
arbitrary size. Moreover, the tutorial describes how to identify the best
configuration for neural network training and learning such for tasks, besides
presenting an easy way to generate and acquire training data. We do so by means
of a general methodology, using complex network models adaptable to any
application. We show that a regression model generated by the neural network
successfully approximates the metric values and therefore are a robust,
effective alternative in real-world applications. The methodology and proposed
machine learning model use only a fraction of time with respect to other
approximation algorithms, which is crucial in complex network applications.
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach
Authors: Hao-Hsuan Chang, Hao Song, Yang Yi, Jianzhong Zhang, Haibo He, Lingjia Liu
Date: 2018-10-28 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dynamic spectrum access (DSA) is regarded as an effective and efficient
technology to share radio spectrum among different networks. As a secondary
user (SU), a DSA device will face two critical problems: avoiding causing
harmful interference to primary users (PUs), and conducting effective
interference coordination with other secondary users. These two problems become
even more challenging for a distributed DSA network where there is no
centralized controllers for SUs. In this paper, we investigate communication
strategies of a distributive DSA network under the presence of spectrum sensing
errors. To be specific, we apply the powerful machine learning tool, deep
reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access
strategies in a distributed fashion assuming NO knowledge of the underlying
system statistics. Furthermore, a special type of recurrent neural network
(RNN), called the reservoir computing (RC), is utilized to realize DRL by
taking advantage of the underlying temporal correlation of the DSA network.
Using the introduced machine learning-based strategy, SUs could make spectrum
access decisions distributedly relying only on their own current and past
spectrum sensing outcomes. Through extensive experiments, our results suggest
that the RC-based spectrum access strategy can help the SU to significantly
reduce the chances of collision with PUs and other SUs. We also show that our
scheme outperforms the myopic method which assumes the knowledge of system
statistics, and converges faster than the Q-learning method when the number of
channels is large.
Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search
Authors: Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, Ji Liu
Date: 2018-10-28 00:00:00
Proceeding: ICLR 2020 1

Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e.g., Computer Go). However, they generally require a large number of rollouts, making their applications costly. Furthermore, it is also extremely challenging to parallelize MCTS due to its inherent sequential nature: each rollout heavily relies on the statistics (e.g., node visitation counts) estimated from previous simulations to achieve an effective exploration-exploitation tradeoff. In spite of these difficulties, we develop an algorithm, WU-UCT, to effectively parallelize MCTS, which achieves linear speedup and exhibits only limited performance loss with an increasing number of workers. The key idea in WU-UCT is a set of statistics that we introduce to track the number of on-going yet incomplete simulation queries (named as unobserved samples). These statistics are used to modify the UCT tree policy in the selection steps in a principled manner to retain effective exploration-exploitation tradeoff when we parallelize the most time-consuming expansion and simulation steps. Experiments on a proprietary benchmark and the Atari Game benchmark demonstrate the linear speedup and the superior performance of WU-UCT comparing to existing techniques.
On Learning Markov Chains
Authors: Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12

The problem of estimating an unknown discrete distribution from its samples
is a fundamental tenet of statistical learning. Over the past decade, it
attracted significant research effort and has been solved for a variety of
divergence measures. Surprisingly, an equally important problem, estimating an
unknown Markov chain from its samples, is still far from understood. We
consider two problems related to the min-max risk (expected loss) of estimating
an unknown $k$-state Markov chain from its $n$ sequential samples: predicting
the conditional distribution of the next sample with respect to the
KL-divergence, and estimating the transition matrix with respect to a natural
loss induced by KL or a more general $f$-divergence measure.
  For the first measure, we determine the min-max prediction risk to within a
linear factor in the alphabet size, showing it is $\Omega(k\log\log n\ / n)$
and $\mathcal{O}(k^2\log\log n\ / n)$. For the second, if the transition
probabilities can be arbitrarily small, then only trivial uniform risk upper
bounds can be derived. We therefore consider transition probabilities that are
bounded away from zero, and resolve the problem for essentially all
sufficiently smooth $f$-divergences, including KL-, $L_2$-, Chi-squared,
Hellinger, and Alpha-divergences.
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Authors: Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, John Hopcroft
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12

It is widely believed that learning good representations is one of the main
reasons for the success of deep neural networks. Although highly intuitive,
there is a lack of theory and systematic approach quantitatively characterizing
what representations do deep neural networks learn. In this work, we move a
tiny step towards a theory and better understanding of the representations.
Specifically, we study a simpler problem: How similar are the representations
learned by two networks with identical architecture but trained from different
initializations. We develop a rigorous theory based on the neuron activation
subspace match model. The theory gives a complete characterization of the
structure of neuron activation subspace matches, where the core concepts are
maximum match and simple match which describe the overall and the finest
similarity between sets of neurons in two networks respectively. We also
propose efficient algorithms to find the maximum match and simple matches.
Finally, we conduct extensive experiments using our algorithms. Experimental
results suggest that, surprisingly, representations learned by the same
convolutional layers of networks trained from different initializations are not
as similar as prevalently expected, at least in terms of subspace match.
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback
Authors: Riku Arakawa, Sosuke Kobayashi, Yuya Unno, Yuta Tsuboi, Shin-ichi Maeda
Date: 2018-10-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration has been one of the greatest challenges in reinforcement learning
(RL), which is a large obstacle in the application of RL to robotics. Even with
state-of-the-art RL algorithms, building a well-learned agent often requires
too many trials, mainly due to the difficulty of matching its actions with
rewards in the distant future. A remedy for this is to train an agent with
real-time feedback from a human observer who immediately gives rewards for some
actions. This study tackles a series of challenges for introducing such a
human-in-the-loop RL scheme. The first contribution of this work is our
experiments with a precisely modeled human observer: binary, delay,
stochasticity, unsustainability, and natural reaction. We also propose an RL
method called DQN-TAMER, which efficiently uses both human feedback and distant
rewards. We find that DQN-TAMER agents outperform their baselines in Maze and
Taxi simulated environments. Furthermore, we demonstrate a real-world
human-in-the-loop RL application where a camera automatically recognizes a
user's facial expressions as feedback to the agent while the agent explores a
maze.
A Convex Duality Framework for GANs
Authors: Farzan Farnia, David Tse
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Generative Adversarial Network

Generative adversarial network (GAN) is a minimax game between a generator
mimicking the true model and a discriminator distinguishing the samples
produced by the generator from the real training samples. Given an
unconstrained discriminator able to approximate any function, this game reduces
to finding the generative model minimizing a divergence measure, e.g. the
Jensen-Shannon (JS) divergence, to the data distribution. However, in practice
the discriminator is constrained to be in a smaller class $\mathcal{F}$ such as
neural nets. Then, a natural question is how the divergence minimization
interpretation changes as we constrain $\mathcal{F}$. In this work, we address
this question by developing a convex duality framework for analyzing GANs. For
a convex set $\mathcal{F}$, this duality framework interprets the original GAN
formulation as finding the generative model with minimum JS-divergence to the
distributions penalized to match the moments of the data distribution, with the
moments specified by the discriminators in $\mathcal{F}$. We show that this
interpretation more generally holds for f-GAN and Wasserstein GAN. As a
byproduct, we apply the duality framework to a hybrid of f-divergence and
Wasserstein distance. Unlike the f-divergence, we prove that the proposed
hybrid divergence changes continuously with the generative model, which
suggests regularizing the discriminator's Lipschitz constant in f-GAN and
vanilla GAN. We numerically evaluate the power of the suggested regularization
schemes for improving GAN's training performance.
Gaussian Process Prior Variational Autoencoders
Authors: Francesco Paolo Casale, Adrian V. Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Time Series, Time Series Analysis

Variational autoencoders (VAE) are a powerful and widely-used class of models
to learn complex data distributions in an unsupervised fashion. One important
limitation of VAEs is the prior assumption that latent sample representations
are independent and identically distributed. However, for many important
datasets, such as time-series of images, this assumption is too strong:
accounting for covariances between samples, such as those in time, can yield to
a more appropriate model specification and improve performance in downstream
tasks. In this work, we introduce a new model, the Gaussian Process (GP) Prior
Variational Autoencoder (GPPVAE), to specifically address this issue. The
GPPVAE aims to combine the power of VAEs with the ability to model correlations
afforded by GP priors. To achieve efficient inference in this new class of
models, we leverage structure in the covariance matrix, and introduce a new
stochastic backpropagation strategy that allows for computing stochastic
gradients in a distributed and low-memory fashion. We show that our method
outperforms conditional VAEs (CVAEs) and an adaptation of standard VAEs in two
image data applications.
Middle-Out Decoding
Authors: Shikib Mehri, Leonid Sigal
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder, Diversity, Video Captioning

Despite being virtually ubiquitous, sequence-to-sequence models are
challenged by their lack of diversity and inability to be externally
controlled. In this paper, we speculate that a fundamental shortcoming of
sequence generation models is that the decoding is done strictly from
left-to-right, meaning that outputs values generated earlier have a profound
effect on those generated later. To address this issue, we propose a novel
middle-out decoder architecture that begins from an initial middle-word and
simultaneously expands the sequence in both directions. To facilitate
information flow and maintain consistent decoding, we introduce a dual
self-attention mechanism that allows us to model complex dependencies between
the outputs. We illustrate the performance of our model on the task of video
captioning, as well as a synthetic sequence de-noising task. Our middle-out
decoder achieves significant improvements on de-noising and competitive
performance in the task of video captioning, while quantifiably improving the
caption diversity. Furthermore, we perform a qualitative analysis that
demonstrates our ability to effectively control the generation process of our
decoder.
Post-prognostics decision in Cyber-Physical Systems
Authors: Safa Meraghni, Labib Sadek Terrissa, Soheyb Ayad, Noureddine Zerhouni, Christophe Varnier
Date: 2018-10-27 00:00:00
Tasks: Management

Prognostics and Health Management (PHM) offers several benefits for
predictive maintenance. It predicts the future behavior of a system as well as
its Remaining Useful Life (RUL). This RUL is used to planned the maintenance
operation to avoid the failure, the stop time and optimize the cost of the
maintenance and failure. However, with the development of the industry the
assets are nowadays distributed this is why the PHM needs to be developed using
the new IT. In our work we propose a PHM solution based on Cyber physical
system where the physical side is connected to the analyze process of the PHM
which are developed in the cloud to be shared and to benefit of the cloud
characteristics
Real-time Action Recognition with Dissimilarity-based Training of Specialized Module Networks
Authors: Marian K. Y. Boktor, Ahmad Al-Kabbany, Radwa Khalil, Said El-Khamy
Date: 2018-10-27 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper addresses the problem of real-time action recognition in trimmed
videos, for which deep neural networks have defined the state-of-the-art
performance in the recent literature. For attaining higher recognition
accuracies with efficient computations, researchers have addressed the various
aspects of limitations in the recognition pipeline. This includes network
architecture, the number of input streams (where additional streams augment the
color information), the cost function to be optimized, in addition to others.
The literature has always aimed, though, at assigning the adopted network (or
networks, in case of multiple streams) the task of recognizing the whole number
of action classes in the dataset at hand. We propose to train multiple
specialized module networks instead. Each module is trained to recognize a
subset of the action classes. Towards this goal, we present a
dissimilarity-based optimized procedure for distributing the action classes
over the modules, which can be trained simultaneously offline. On two standard
datasets--UCF-101 and HMDB-51--the proposed method demonstrates a comparable
performance, that is superior in some aspects, to the state-of-the-art, and
that satisfies the real-time constraint. We achieved 72.5\% accuracy on the
challenging HMDB-51 dataset. By assigning fewer and unalike classes to each
module network, this research paves the way to benefit from light-weight
architectures without compromising recognition accuracy.
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Authors: Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Data Augmentation, Generative Adversarial Network, Small Data Image Classification

Deep neural networks suffer from over-fitting and catastrophic forgetting
when trained with small data. One natural remedy for this problem is data
augmentation, which has been recently shown to be effective. However, previous
works either assume that intra-class variances can always be generalized to new
classes, or employ naive generation methods to hallucinate finite examples
without modeling their latent distributions. In this work, we propose
Covariance-Preserving Adversarial Augmentation Networks to overcome existing
limits of low-shot learning. Specifically, a novel Generative Adversarial
Network is designed to model the latent distribution of each novel class given
its related base counterparts. Since direct estimation of novel classes can be
inductively biased, we explicitly preserve covariance information as the
`variability' of base examples during the generation process. Empirical results
show that our model can generate realistic yet diverse examples, leading to
substantial improvements on the ImageNet benchmark over the state of the art.
Towards Robust Deep Neural Networks
Authors: Timothy E. Wang, Yiming Gu, Dhagash Mehta, Xiaojun Zhao, Edgar A. Bernal
Date: 2018-10-27 00:00:00
Tasks: Adversarial Robustness, Computational chemistry, Sensitivity

We investigate the topics of sensitivity and robustness in feedforward and
convolutional neural networks. Combining energy landscape techniques developed
in computational chemistry with tools drawn from formal methods, we produce
empirical evidence indicating that networks corresponding to lower-lying minima
in the optimization landscape of the learning objective tend to be more robust.
The robustness estimate used is the inverse of a proposed sensitivity measure,
which we define as the volume of an over-approximation of the reachable set of
network outputs under all additive $l_{\infty}$-bounded perturbations on the
input data. We present a novel loss function which includes a sensitivity term
in addition to the traditional task-oriented and regularization terms. In our
experiments on standard machine learning and computer vision datasets, we show
that the proposed loss function leads to networks which reliably optimize the
robustness measure as well as other related metrics of adversarial robustness
without significant degradation in the classification error. Experimental
results indicate that the proposed method outperforms state-of-the-art
sensitivity-based learning approaches with regards to robustness to adversarial
attacks. We also show that although the introduced framework does not
explicitly enforce an adversarial loss, it achieves competitive overall
performance relative to methods that do.