pwc-archive/papers-with-abstracts huggingface dataset:
Supervision via Competition: Robot Adversaries for Learning Tasks
Authors: Lerrel Pinto, James Davidson, Abhinav Gupta
Date: 2016-10-05 00:00:00

There has been a recent paradigm shift in robotics to data-driven learning
for planning and control. Due to large number of experiences required for
training, most of these approaches use a self-supervised paradigm: using
sensors to measure success/failure. However, in most cases, these sensors
provide weak supervision at best. In this work, we propose an adversarial
learning framework that pits an adversary against the robot learning the task.
In an effort to defeat the adversary, the original robot learns to perform the
task with more robustness leading to overall improved performance. We show that
this adversarial framework forces the the robot to learn a better grasping
model in order to overcome the adversary. By grasping 82% of presented novel
objects compared to 68% without an adversary, we demonstrate the utility of
creating adversaries. We also demonstrate via experiments that having robots in
adversarial setting might be a better learning strategy as compared to having
collaborative multiple robots.
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
Authors: Orestis Tsinalis, Paul M. Matthews, Yike Guo, Stefanos Zafeiriou
Date: 2016-10-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We used convolutional neural networks (CNNs) for automatic sleep stage
scoring based on single-channel electroencephalography (EEG) to learn
task-specific filters for classification without using prior domain knowledge.
We used an openly available dataset from 20 healthy young adults for evaluation
and applied 20-fold cross-validation. We used class-balanced random sampling
within the stochastic gradient descent (SGD) optimization of the CNN to avoid
skewed performance in favor of the most represented sleep stages. We achieved
high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep
stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all
subjects. By analyzing and visualizing the filters that our CNN learns, we
found that rules learned by the filters correspond to sleep scoring criteria in
the American Academy of Sleep Medicine (AASM) manual that human experts follow.
Our method's performance is balanced across classes and our results are
comparable to state-of-the-art methods with hand-engineered features. We show
that, without using prior domain knowledge, a CNN can automatically learn to
distinguish among different normal sleep stages.
Data Poisoning Attacks on Factorization-Based Collaborative Filtering
Authors: Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik
Date: 2016-08-29 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Collaborative Filtering, Data Poisoning, Decision Making

Recommendation and collaborative filtering systems are important in modern
information and e-commerce applications. As these systems are becoming
increasingly popular in the industry, their outputs could affect business
decision making, introducing incentives for an adversarial party to compromise
the availability or integrity of such systems. We introduce a data poisoning
attack on collaborative filtering systems. We demonstrate how a powerful
attacker with full knowledge of the learner can generate malicious data so as
to maximize his/her malicious objectives, while at the same time mimicking
normal user behavior to avoid being detected. While the complete knowledge
assumption seems extreme, it enables a robust assessment of the vulnerability
of collaborative filtering schemes to highly motivated attacks. We present
efficient solutions for two popular factorization-based collaborative filtering
algorithms: the \emph{alternative minimization} formulation and the
\emph{nuclear norm minimization} method. Finally, we test the effectiveness of
our proposed algorithms on real-world data and discuss potential defensive
strategies.
Extending Unification in $\mathcal{EL}$ to Disunification: The Case of Dismatching and Local Disunification
Authors: Franz Baader, Stefan Borgwardt, Barbara Morawska
Date: 2016-09-19 00:00:00

Unification in Description Logics has been introduced as a means to detect
redundancies in ontologies. We try to extend the known decidability results for
unification in the Description Logic $\mathcal{EL}$ to disunification since
negative constraints can be used to avoid unwanted unifiers. While decidability
of the solvability of general $\mathcal{EL}$-disunification problems remains an
open problem, we obtain NP-completeness results for two interesting special
cases: dismatching problems, where one side of each negative constraint must be
ground, and local solvability of disunification problems, where we consider
only solutions that are constructed from terms occurring in the input problem.
More precisely, we first show that dismatching can be reduced to local
disunification, and then provide two complementary NP-algorithms for finding
local solutions of disunification problems.
Learning Protein Dynamics with Metastable Switching Systems
Authors: Bharath Ramsundar, Vijay S. Pande
Date: 2016-10-05 00:00:00
Tasks: Drug Design

We introduce a machine learning approach for extracting fine-grained
representations of protein evolution from molecular dynamics datasets.
Metastable switching linear dynamical systems extend standard switching models
with a physically-inspired stability constraint. This constraint enables the
learning of nuanced representations of protein dynamics that closely match
physical reality. We derive an EM algorithm for learning, where the E-step
extends the forward-backward algorithm for HMMs and the M-step requires the
solution of large biconvex optimization problems. We construct an approximate
semidefinite program solver based on the Frank-Wolfe algorithm and use it to
solve the M-step. We apply our EM algorithm to learn accurate dynamics from
large simulation datasets for the opioid peptide met-enkephalin and the
proto-oncogene Src-kinase. Our learned models demonstrate significant
improvements in temporal coherence over HMMs and standard switching models for
met-enkephalin, and sample transition paths (possibly useful in rational drug
design) for Src-kinase.
Binary classification of multi-channel EEG records based on the $ε$-complexity of continuous vector functions
Authors: Boris Darkhovsky, Alexandra Piryatinska, Alexander Kaplan
Date: 2016-10-05 00:00:00
Tasks: Binary Classification, Classification, EEG, Electroencephalogram (EEG), General Classification

A methodology for binary classification of EEG records which correspond to
different mental states is proposed. This model-free methodology is based on
our theory of the $\epsilon$-complexity of continuous functions which is
extended here (see Appendix) to the case of vector functions. This extension
permits us to handle multichannel EEG recordings. The essence of the
methodology is to use the $\epsilon$-complexity coefficients as features to
classify (using well known classifiers) different types of vector functions
representing EEG-records corresponding to different types of mental states. We
apply our methodology to the problem of classification of multichannel
EEG-records related to a group of healthy adolescents and a group of
adolescents with schizophrenia. We found that our methodology permits accurate
classification of the data in the four-dimensional feather space of the
$\epsilon$-complexity coefficients.
A new algorithm for identity verification based on the analysis of a handwritten dynamic signature
Authors: Krzysztof Cpalka, Marcin Zalasinski, Leszek Rutkowski
Date: 2016-10-05 00:00:00

Identity verification based on authenticity assessment of a handwritten
signature is an important issue in biometrics. There are many effective methods
for signature verification taking into account dynamics of a signing process.
Methods based on partitioning take a very important place among them. In this
paper we propose a new approach to signature partitioning. Its most important
feature is the possibility of selecting and processing of hybrid partitions in
order to increase a precision of the test signature analysis. Partitions are
formed by a combination of vertical and horizontal sections of the signature.
Vertical sections correspond to the initial, middle, and final time moments of
the signing process. In turn, horizontal sections correspond to the signature
areas associated with high and low pen velocity and high and low pen pressure
on the surface of a graphics tablet. Our previous research on vertical and
horizontal sections of the dynamic signature (created independently) led us to
develop the algorithm presented in this paper. Selection of sections, among
others, allows us to define the stability of the signing process in the
partitions, promoting signature areas of greater stability (and vice versa). In
the test of the proposed method two databases were used: public MCYT-100 and
paid BioSecure.
DeepGaze II: Reading fixations from deep features trained on object recognition
Authors: Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge
Date: 2016-10-05 00:00:00
Tasks: Object Recognition, Saliency Prediction, Transfer Learning

Here we present DeepGaze II, a model that predicts where people look in
images. The model uses the features from the VGG-19 deep neural network trained
to identify objects in images. Contrary to other saliency models that use deep
features, here we use the VGG features for saliency prediction with no
additional fine-tuning (rather, a few readout layers are trained on top of the
VGG features to predict saliency). The model is therefore a strong test of
transfer learning. After conservative cross-validation, DeepGaze II explains
about 87% of the explainable information gain in the patterns of fixations and
achieves top performance in area under the curve metrics on the MIT300 hold-out
benchmark. These results corroborate the finding from DeepGaze I (which
explained 56% of the explainable information gain), that deep features trained
on object recognition provide a versatile feature space for performing related
visual tasks. We explore the factors that contribute to this success and
present several informative image examples. A web service is available to
compute model predictions at http://deepgaze.bethgelab.org.
Summarizing Situational and Topical Information During Crises
Authors: Koustav Rudra, Siddhartha Banerjee, Niloy Ganguly, Pawan Goyal, Muhammad Imran, Prasenjit Mitra
Date: 2016-10-05 00:00:00
Tasks: Abstractive Text Summarization, Humanitarian

The use of microblogging platforms such as Twitter during crises has become
widespread. More importantly, information disseminated by affected people
contains useful information like reports of missing and found people, requests
for urgent needs etc. For rapid crisis response, humanitarian organizations
look for situational awareness information to understand and assess the
severity of the crisis. In this paper, we present a novel framework (i) to
generate abstractive summaries useful for situational awareness, and (ii) to
capture sub-topics and present a short informative summary for each of these
topics. A summary is generated using a two stage framework that first extracts
a set of important tweets from the whole set of information through an
Integer-linear programming (ILP) based optimization technique and then follows
a word graph and concept event based abstractive summarization technique to
produce the final summary. High accuracies obtained for all the tasks show the
effectiveness of the proposed framework.
Colorful Image Colorization
Authors: Richard Zhang, Phillip Isola, Alexei A. Efros
Date: 2016-03-28 00:00:00
Tasks: Colorization, Diversity, Image Colorization, Self-Supervised Image Classification

Given a grayscale photograph as input, this paper attacks the problem of
hallucinating a plausible color version of the photograph. This problem is
clearly underconstrained, so previous approaches have either relied on
significant user interaction or resulted in desaturated colorizations. We
propose a fully automatic approach that produces vibrant and realistic
colorizations. We embrace the underlying uncertainty of the problem by posing
it as a classification task and use class-rebalancing at training time to
increase the diversity of colors in the result. The system is implemented as a
feed-forward pass in a CNN at test time and is trained on over a million color
images. We evaluate our algorithm using a "colorization Turing test," asking
human participants to choose between a generated and ground truth color image.
Our method successfully fools humans on 32% of the trials, significantly higher
than previous methods. Moreover, we show that colorization can be a powerful
pretext task for self-supervised feature learning, acting as a cross-channel
encoder. This approach results in state-of-the-art performance on several
feature learning benchmarks.
A Geometric Framework for Convolutional Neural Networks
Authors: Anthony L. Caterini, Dong Eui Chang
Date: 2016-08-15 00:00:00
Tasks: Form

In this paper, a geometric framework for neural networks is proposed. This
framework uses the inner product space structure underlying the parameter set
to perform gradient descent not in a component-based form, but in a
coordinate-free manner. Convolutional neural networks are described in this
framework in a compact form, with the gradients of standard --- and
higher-order --- loss functions calculated for each layer of the network. This
approach can be applied to other network structures and provides a basis on
which to create new networks.
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value
Authors: Joseph Ramsey
Date: 2016-10-03 00:00:00

A number of attempts have been made to improve accuracy and/or scalability of
the PC (Peter and Clark) algorithm, some well known (Buhlmann, et al., 2010;
Kalisch and Buhlmann, 2007; 2008; Zhang, 2012, to give some examples). We add
here one more tool to the toolbox: the simple observation that if one is forced
to choose between a variety of possible conditioning sets for a pair of
variables, one should choose the one with the highest p-value. One can use the
CPC (Conservative PC, Ramsey et al., 2012) algorithm as a guide to possible
sepsets for a pair of variables. However, whereas CPC uses a voting rule to
classify colliders versus noncolliders, our proposed algorithm, PC-Max, picks
the conditioning set with the highest p-value, so that there are no
ambiguities. We combine this with two other optimizations: (a) avoiding
bidirected edges in the orientation of colliders, and (b) parallelization. For
(b) we borrow ideas from the PC-Stable algorithm (Colombo and Maathuis, 2014).
The result is an algorithm that scales quite well both in terms of accuracy and
time, with no risk of bidirected edges.
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Authors: Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra
Date: 2016-06-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Multiple-choice

Many practical perception systems exist within larger processes that include
interactions with users or additional components capable of evaluating the
quality of predicted solutions. In these contexts, it is beneficial to provide
these oracle mechanisms with multiple highly likely hypotheses rather than a
single prediction. In this work, we pose the task of producing multiple outputs
as a learning problem over an ensemble of deep networks -- introducing a novel
stochastic gradient descent based approach to minimize the loss with respect to
an oracle. Our method is simple to implement, agnostic to both architecture and
loss function, and parameter-free. Our approach achieves lower oracle error
compared to existing methods on a wide range of tasks and deep architectures.
We also show qualitatively that the diverse solutions produced often provide
interpretable representations of task ambiguity.
Lifted Message Passing for the Generalized Belief Propagation
Authors: Udi Apsel
Date: 2016-10-05 00:00:00
Tasks: Symmetry Detection

We introduce the lifted Generalized Belief Propagation (GBP) message passing
algorithm, for the computation of sum-product queries in Probabilistic
Relational Models (e.g. Markov logic network). The algorithm forms a compact
region graph and establishes a modified version of message passing, which
mimics the GBP behavior in a corresponding ground model. The compact graph is
obtained by exploiting a graphical representation of clusters, which reduces
cluster symmetry detection to isomorphism tests on small local graphs. The
framework is thus capable of handling complex models, while remaining
domain-size independent.
VoxML: A Visualization Modeling Language
Authors: James Pustejovsky, Nikhil Krishnaswamy
Date: 2016-10-05 00:00:00
Proceeding: LREC 2016 5

We present the specification for a modeling language, VoxML, which encodes
semantic knowledge of real-world objects represented as three-dimensional
models, and of events and attributes related to and enacted over these objects.
VoxML is intended to overcome the limitations of existing 3D visual markup
languages by allowing for the encoding of a broad range of semantic knowledge
that can be exploited by a variety of systems and platforms, leading to
multimodal simulations of real-world scenarios using conceptual objects that
represent their semantic values.
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates
Authors: Jiali Mei, Yohann de Castro, Yannig Goude, Georges Hébrail
Date: 2016-10-05 00:00:00
Tasks: Time Series, Time Series Analysis

Motivated by electricity consumption metering, we extend existing nonnegative
matrix factorization (NMF) algorithms to use linear measurements as
observations, instead of matrix entries. The objective is to estimate multiple
time series at a fine temporal scale from temporal aggregates measured on each
individual series. Furthermore, our algorithm is extended to take into account
individual autocorrelation to provide better estimation, using a recent convex
relaxation of quadratically constrained quadratic program. Extensive
experiments on synthetic and real-world electricity consumption datasets
illustrate the effectiveness of our matrix recovery algorithms.
$\ell_1$ Regularized Gradient Temporal-Difference Learning
Authors: Dominik Meyer, Hao Shen, Klaus Diepold
Date: 2016-10-05 00:00:00

In this paper, we study the Temporal Difference (TD) learning with linear
value function approximation. It is well known that most TD learning algorithms
are unstable with linear function approximation and off-policy learning. Recent
development of Gradient TD (GTD) algorithms has addressed this problem
successfully. However, the success of GTD algorithms requires a set of well
chosen features, which are not always available. When the number of features is
huge, the GTD algorithms might face the problem of overfitting and being
computationally expensive. To cope with this difficulty, regularization
techniques, in particular $\ell_1$ regularization, have attracted significant
attentions in developing TD learning algorithms. The present work combines the
GTD algorithms with $\ell_1$ regularization. We propose a family of $\ell_1$
regularized GTD algorithms, which employ the well known soft thresholding
operator. We investigate convergence properties of the proposed algorithms, and
depict their performance with several numerical experiments.
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Authors: Yarin Gal, Zoubin Ghahramani
Date: 2015-12-16 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Inference, Deep Learning, Language Modelling, Sentiment Analysis, Variational Inference

Recurrent neural networks (RNNs) stand at the forefront of many recent
developments in deep learning. Yet a major difficulty with these models is
their tendency to overfit, with dropout shown to fail when applied to recurrent
layers. Recent results at the intersection of Bayesian modelling and deep
learning offer a Bayesian interpretation of common deep learning techniques
such as dropout. This grounding of dropout in approximate Bayesian inference
suggests an extension of the theoretical results, offering insights into the
use of dropout with RNN models. We apply this new variational inference based
dropout technique in LSTM and GRU models, assessing it on language modelling
and sentiment analysis tasks. The new approach outperforms existing techniques,
and to the best of our knowledge improves on the single model state-of-the-art
in language modelling with the Penn Treebank (73.4 test perplexity). This
extends our arsenal of variational tools in deep learning.
Scaled stochastic gradient descent for low-rank matrix completion
Authors: Bamdev Mishra, Rodolphe Sepulchre
Date: 2016-03-16 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

The paper looks at a scaled variant of the stochastic gradient descent
algorithm for the matrix completion problem. Specifically, we propose a novel
matrix-scaling of the partial derivatives that acts as an efficient
preconditioning for the standard stochastic gradient descent algorithm. This
proposed matrix-scaling provides a trade-off between local and global second
order information. It also resolves the issue of scale invariance that exists
in matrix factorization models. The overall computational complexity is linear
with the number of known entries, thereby extending to a large-scale setup.
Numerical comparisons show that the proposed algorithm competes favorably with
state-of-the-art algorithms on various different benchmarks.
Compressive Imaging with Iterative Forward Models
Authors: Hsiou-Yuan Liu, Ulugbek S. Kamilov, Dehong Liu, Hassan Mansour, Petros T. Boufounos
Date: 2016-10-05 00:00:00

We propose a new compressive imaging method for reconstructing 2D or 3D
objects from their scattered wave-field measurements. Our method relies on a
novel, nonlinear measurement model that can account for the multiple scattering
phenomenon, which makes the method preferable in applications where linear
measurement models are inaccurate. We construct the measurement model by
expanding the scattered wave-field with an accelerated-gradient method, which
is guaranteed to converge and is suitable for large-scale problems. We provide
explicit formulas for computing the gradient of our measurement model with
respect to the unknown image, which enables image formation with a sparsity-
driven numerical optimization algorithm. We validate the method both
analytically and with numerical simulations.