pwc-archive/papers-with-abstracts huggingface dataset:
Aerial Spectral Super-Resolution using Conditional Adversarial Networks
Authors: Aneesh Rangnekar, Nilay Mokashi, Emmett Ientilucci, Christopher Kanan, Matthew Hoffman
Date: 2017-12-23 00:00:00
Tasks: Spectral Super-Resolution, Super-Resolution

Inferring spectral signatures from ground based natural images has acquired a
lot of interest in applied deep learning. In contrast to the spectra of ground
based images, aerial spectral images have low spatial resolution and suffer
from higher noise interference. In this paper, we train a conditional
adversarial network to learn an inverse mapping from a trichromatic space to 31
spectral bands within 400 to 700 nm. The network is trained on AeroCampus, a
first of its kind aerial hyperspectral dataset. AeroCampus consists of high
spatial resolution color images and low spatial resolution hyperspectral images
(HSI). Color images synthesized from 31 spectral bands are used to train our
network. With a baseline root mean square error of 2.48 on the synthesized RGB
test data, we show that it is possible to generate spectral signatures in
aerial imagery.
A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks
Authors: Farshad Harirchi, Doohyun Kim, Omar A. Khalil, Sijia Liu, Paolo Elvati, Angela Violi, Alfred O. Hero
Date: 2017-12-18 00:00:00
Tasks: Sparse Learning

Chemical kinetic mechanisms can be represented by sets of elementary
reactions that are easily translated into mathematical terms using
physicochemical relationships. The schematic representation of reactions
captures the interactions between reacting species and products. Determining
the minimal chemical interactions underlying the dynamic behavior of systems is
a major task. In this paper, we introduce a novel approach for the
identification of the influential reactions in chemical reaction networks for
combustion applications, using a data-driven sparse-learning technique. The
proposed approach identifies a set of influential reactions using species
concentrations and reaction rates, with minimal computational cost without
requiring additional data or simulations. The new approach is applied to
analyze the combustion chemistry of H2 and C3H8 in a constant-volume
homogeneous reactor. The influential reactions identified by the
sparse-learning method are consistent with the current kinetics knowledge of
chemical mechanisms. Additionally, we show that a reduced version of the parent
mechanism can be generated as a combination of the influential reactions
identified at different times and conditions and that for both H2 and C3H8 this
reduced mechanism performs closely to the parent mechanism as a function of
ignition delay over a wide range of conditions. Our results demonstrate the
potential of the sparse-learning approach as an effective and efficient tool
for mechanism analysis and mechanism reduction.
Graph Embedding Techniques, Applications, and Performance: A Survey
Authors: Palash Goyal, Emilio Ferrara
Date: 2017-05-08 00:00:00
Tasks: Graph Embedding, Survey

Graphs, such as social networks, word co-occurrence networks, and
communication networks, occur naturally in various real-world applications.
Analyzing them yields insight into the structure of society, language, and
different patterns of communication. Many approaches have been proposed to
perform the analysis. Recently, methods which use the representation of graph
nodes in vector space have gained traction from the research community. In this
survey, we provide a comprehensive and structured analysis of various graph
embedding techniques proposed in the literature. We first introduce the
embedding task and its challenges such as scalability, choice of
dimensionality, and features to be preserved, and their possible solutions. We
then present three categories of approaches based on factorization methods,
random walks, and deep learning, with examples of representative algorithms in
each category and analysis of their performance on various tasks. We evaluate
these state-of-the-art methods on a few common datasets and compare their
performance against one another. Our analysis concludes by suggesting some
potential applications and future directions. We finally present the
open-source Python library we developed, named GEM (Graph Embedding Methods,
available at https://github.com/palash1992/GEM), which provides all presented
algorithms within a unified interface to foster and facilitate research on the
topic.
A short variational proof of equivalence between policy gradients and soft Q learning
Authors: Pierre H. Richemond, Brendan Maginnis
Date: 2017-12-22 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Two main families of reinforcement learning algorithms, Q-learning and policy
gradients, have recently been proven to be equivalent when using a softmax
relaxation on one part, and an entropic regularization on the other. We relate
this result to the well-known convex duality of Shannon entropy and the softmax
function. Such a result is also known as the Donsker-Varadhan formula. This
provides a short proof of the equivalence. We then interpret this duality
further, and use ideas of convex analysis to prove a new policy inequality
relative to soft Q-learning.
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Authors: Stephen Tu, Benjamin Recht
Date: 2017-12-22 00:00:00
Proceeding: ICML 2018 7
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has been successfully used to solve many
continuous control tasks. Despite its impressive results however, fundamental
questions regarding the sample complexity of RL on continuous problems remain
open. We study the performance of RL in this setting by considering the
behavior of the Least-Squares Temporal Difference (LSTD) estimator on the
classic Linear Quadratic Regulator (LQR) problem from optimal control. We give
the first finite-time analysis of the number of samples needed to estimate the
value function for a fixed static state-feedback policy to within
$\varepsilon$-relative error. In the process of deriving our result, we give a
general characterization for when the minimum eigenvalue of the empirical
covariance matrix formed along the sample path of a fast-mixing stochastic
process concentrates above zero, extending a result by Koltchinskii and
Mendelson in the independent covariates setting. Finally, we provide
experimental evidence indicating that our analysis correctly captures the
qualitative behavior of LSTD on several LQR instances.
Find the Conversation Killers: a Predictive Study of Thread-ending Posts
Authors: Yunhao Jiao, Cheng Li, Fei Wu, Qiaozhu Mei
Date: 2017-12-22 00:00:00

How to improve the quality of conversations in online communities has
attracted considerable attention recently. Having engaged, urbane, and reactive
online conversations has a critical effect on the social life of Internet
users. In this study, we are particularly interested in identifying a post in a
multi-party conversation that is unlikely to be further replied to, which
therefore kills that thread of the conversation. For this purpose, we propose a
deep learning model called the ConverNet. ConverNet is attractive due to its
capability of modeling the internal structure of a long conversation and its
appropriate encoding of the contextual information of the conversation, through
effective integration of attention mechanisms. Empirical experiments on
real-world datasets demonstrate the effectiveness of the proposal model. For
the widely concerned topic, our analysis also offers implications for improving
the quality and user experience of online conversations.
Predicting protein-protein interactions based on rotation of proteins in 3D-space
Authors: Samaneh Aghajanbaglo, Sobhan Moosavi, Maseud Rahgozar, Amir Rahimi
Date: 2017-12-22 00:00:00

Protein-Protein Interactions (PPIs) perform essential roles in biological
functions. Although some experimental techniques have been developed to detect
PPIs, they suffer from high false positive and high false negative rates.
Consequently, efforts have been devoted during recent years to develop
computational approaches to predict the interactions utilizing various sources
of information. Therefore, a unique category of prediction approaches has been
devised which is based on the protein sequence information. However, finding an
appropriate feature encoding to characterize the sequence of proteins is a
major challenge in such methods. In presented work, a sequence based method is
proposed to predict protein-protein interactions using N-Gram encoding
approaches to describe amino acids and a Relaxed Variable Kernel Density
Estimator (RVKDE) as a machine learning tool. Moreover, since proteins can
rotate in 3D-space, amino acid compositions have been considered with
"undirected" property which leads to reduce dimensions of the vector space. The
results show that our proposed method achieves the superiority of prediction
performance with improving an F-measure of 2.5% on Human Protein Reference
Dataset (HPRD).
Learning Based on CC1 and CC4 Neural Networks
Authors: Subhash Kak
Date: 2017-12-22 00:00:00

We propose that a general learning system should have three kinds of agents
corresponding to sensory, short-term, and long-term memory that implicitly will
facilitate context-free and context-sensitive aspects of learning. These three
agents perform mututally complementary functions that capture aspects of the
human cognition system. We investigate the use of CC1 and CC4 networks for use
as models of short-term and sensory memory.
Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration
Authors: Fattaneh Jabbari, Mahdi Pakdaman Naeini, Gregory F. Cooper
Date: 2017-12-22 00:00:00
Tasks: Causal Inference

Discovery of an accurate causal Bayesian network structure from observational
data can be useful in many areas of science. Often the discoveries are made
under uncertainty, which can be expressed as probabilities. To guide the use of
such discoveries, including directing further investigation, it is important
that those probabilities be well-calibrated. In this paper, we introduce a
novel framework to derive calibrated probabilities of causal relationships from
observational data. The framework consists of three components: (1) an
approximate method for generating initial probability estimates of the edge
types for each pair of variables, (2) the availability of a relatively small
number of the causal relationships in the network for which the truth status is
known, which we call a calibration training set, and (3) a calibration method
for using the approximate probability estimates and the calibration training
set to generate calibrated probabilities for the many remaining pairs of
variables. We also introduce a new calibration method based on a shallow neural
network. Our experiments on simulated data support that the proposed approach
improves the calibration of causal edge predictions. The results also support
that the approach often improves the precision and recall of predictions.
Learning in the Machine: the Symmetries of the Deep Learning Channel
Authors: Pierre Baldi, Peter Sadowski, Zhiqin Lu
Date: 2017-12-22 00:00:00

In a physical neural system, learning rules must be local both in space and
time. In order for learning to occur, non-local information must be
communicated to the deep synapses through a communication channel, the deep
learning channel. We identify several possible architectures for this learning
channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges:
1) symmetry of architectures; 2) symmetry of weights; 3) symmetry of neurons;
4) symmetry of derivatives; 5) symmetry of processing; and 6) symmetry of
learning rules. Random backpropagation (RBP) addresses the second and third
symmetry, and some of its variations, such as skipped RBP (SRBP) address the
first and the fourth symmetry. Here we address the last two desirable
symmetries showing through simulations that they can be achieved and that the
learning channel is particularly robust to symmetry variations. Specifically,
random backpropagation and its variations can be performed with the same
non-linear neurons used in the main input-output forward channel, and the
connections in the learning channel can be adapted using the same algorithm
used in the forward channel, removing the need for any specialized hardware in
the learning channel. Finally, we provide mathematical results in simple cases
showing that the learning equations in the forward and backward channels
converge to fixed points, for almost any initial conditions. In symmetric
architectures, if the weights in both channels are small at initialization,
adaptation in both channels leads to weights that are essentially symmetric
during and after learning. Biological connections are discussed.
Automated Surgical Skill Assessment in RMIS Training
Authors: Aneeq Zia, Irfan Essa
Date: 2017-12-22 00:00:00
Tasks: General Classification

Purpose: Manual feedback in basic RMIS training can consume a significant
amount of time from expert surgeons' schedule and is prone to subjectivity.
While VR-based training tasks can generate automated score reports, there is no
mechanism of generating automated feedback for surgeons performing basic
surgical tasks in RMIS training. In this paper, we explore the usage of
different holistic features for automated skill assessment using only robot
kinematic data and propose a weighted feature fusion technique for improving
score prediction performance.
  Methods: We perform our experiments on the publicly available JIGSAWS dataset
and evaluate four different types of holistic features from robot kinematic
data - Sequential Motion Texture (SMT), Discrete Fourier Transform (DFT),
Discrete Cosine Transform (DCT) and Approximate Entropy (ApEn). The features
are then used for skill classification and exact skill score prediction. Along
with using these features individually, we also evaluate the performance using
our proposed weighted combination technique.
  Results: Our results demonstrate that these holistic features outperform all
previous HMM based state-of-the-art methods for skill classification on the
JIGSAWS dataset. Also, our proposed feature fusion strategy significantly
improves performance for skill score predictions achieving up to 0.61 average
spearman correlation coefficient.
  Conclusions: Holistic features capturing global information from robot
kinematic data can successfully be used for evaluating surgeon skill in basic
surgical tasks on the da Vinci robot. Using the framework presented can
potentially allow for real time score feedback in RMIS training.
On Singleton Arc Consistency for CSPs Defined by Monotone Patterns
Authors: Clement Carbonnel, David A. Cohen, Martin C. Cooper, Stanislav Zivny
Date: 2017-04-20 00:00:00
Tasks: ARC, General Classification

Singleton arc consistency is an important type of local consistency which has
been recently shown to solve all constraint satisfaction problems (CSPs) over
constraint languages of bounded width. We aim to characterise all classes of
CSPs defined by a forbidden pattern that are solved by singleton arc
consistency and closed under removing constraints. We identify five new
patterns whose absence ensures solvability by singleton arc consistency, four
of which are provably maximal and three of which generalise 2-SAT. Combined
with simple counter-examples for other patterns, we make significant progress
towards a complete classification.
Letter-Based Speech Recognition with Gated ConvNets
Authors: Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert
Date: 2017-12-22 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In the recent literature, "end-to-end" speech systems often refer to
letter-based acoustic models trained in a sequence-to-sequence manner, either
via a recurrent model or via a structured output learning approach (such as
CTC). In contrast to traditional phone (or senone)-based approaches, these
"end-to-end'' approaches alleviate the need of word pronunciation modeling, and
do not require a "forced alignment" step at training time. Phone-based
approaches remain however state of the art on classical benchmarks. In this
paper, we propose a letter-based speech recognition system, leveraging a
ConvNet acoustic model. Key ingredients of the ConvNet are Gated Linear Units
and high dropout. The ConvNet is trained to map audio sequences to their
corresponding letter transcriptions, either via a classical CTC approach, or
via a recent variant called ASG. Coupled with a simple decoder at inference
time, our system matches the best existing letter-based systems on WSJ (in word
error rate), and shows near state of the art performance on LibriSpeech.
Rank Pruning for Dominance Queries in CP-Nets
Authors: Kathryn Laing, Peter Adam Thwaites, John Paul Gosling
Date: 2017-12-22 00:00:00

Conditional preference networks (CP-nets) are a graphical representation of a
person's (conditional) preferences over a set of discrete variables. In this
paper, we introduce a novel method of quantifying preference for any given
outcome based on a CP-net representation of a user's preferences. We
demonstrate that these values are useful for reasoning about user preferences.
In particular, they allow us to order (any subset of) the possible outcomes in
accordance with the user's preferences. Further, these values can be used to
improve the efficiency of outcome dominance testing. That is, given a pair of
outcomes, we can determine which the user prefers more efficiently. Through
experimental results, we show that this method is more effective than existing
techniques for improving dominance testing efficiency. We show that the above
results also hold for CP-nets that express indifference between variable
values.
Evaluation of PPG Biometrics for Authentication in different states
Authors: Umang Yadav, Sherif N Abbas, Dimitrios Hatzinakos
Date: 2017-12-22 00:00:00
Tasks: Dimensionality Reduction

Amongst all medical biometric traits, Photoplethysmograph (PPG) is the
easiest to acquire. PPG records the blood volume change with just combination
of Light Emitting Diode and Photodiode from any part of the body. With IoT and
smart homes' penetration, PPG recording can easily be integrated with other
vital wearable devices. PPG represents peculiarity of hemodynamics and
cardiovascular system for each individual. This paper presents non-fiducial
method for PPG based biometric authentication. Being a physiological signal,
PPG signal alters with physical/mental stress and time. For robustness, these
variations cannot be ignored. While, most of the previous works focused only on
single session, this paper demonstrates extensive performance evaluation of PPG
biometrics against single session data, different emotions, physical exercise
and time-lapse using Continuous Wavelet Transform (CWT) and Direct Linear
Discriminant Analysis (DLDA). When evaluated on different states and datasets,
equal error rate (EER) of $0.5\%$-$6\%$ was achieved for $45$-$60$s average
training time. Our CWT/DLDA based technique outperformed all other
dimensionality reduction techniques and previous work.
Learning in the Machine: Random Backpropagation and the Deep Learning Channel
Authors: Pierre Baldi, Peter Sadowski, Zhiqin Lu
Date: 2016-12-08 00:00:00

Random backpropagation (RBP) is a variant of the backpropagation algorithm
for training neural networks, where the transpose of the forward matrices are
replaced by fixed random matrices in the calculation of the weight updates. It
is remarkable both because of its effectiveness, in spite of using random
matrices to communicate error information, and because it completely removes
the taxing requirement of maintaining symmetric weights in a physical neural
system. To better understand random backpropagation, we first connect it to the
notions of local learning and learning channels. Through this connection, we
derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP
(ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their
computational complexity. We then study their behavior through simulations
using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that
most of these variants work robustly, almost as well as backpropagation, and
that multiplication by the derivatives of the activation functions is
important. As a follow-up, we study also the low-end of the number of bits
required to communicate error information over the learning channel. We then
provide partial intuitive explanations for some of the remarkable properties of
RBP and its variations. Finally, we prove several mathematical results,
including the convergence to fixed points of linear chains of arbitrary length,
the convergence to fixed points of linear autoencoders with decorrelated data,
the long-term existence of solutions for linear systems with a single hidden
layer and convergence in special cases, and the convergence to fixed points of
non-linear chains, when the derivative of the activation functions is included.
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
Authors: Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien
Date: 2017-12-22 00:00:00
Tasks: Binary Classification, General Classification

This work investigates the training of conditional random fields (CRFs) via
the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and
Zhang (2016). SDCA enjoys a linear convergence rate and a strong empirical
performance for binary classification problems. However, it has never been used
to train CRFs. Yet it benefits from an `exact' line search with a single
marginalization oracle call, unlike previous approaches. In this paper, we
adapt SDCA to train CRFs, and we enhance it with an adaptive non-uniform
sampling strategy based on block duality gaps. We perform experiments on four
standard sequence prediction tasks. SDCA demonstrates performances on par with
the state of the art, and improves over it on three of the four datasets, which
have in common the use of sparse features.
Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts
Authors: Shounak Datta, Sayak Nag, Sankha Subhra Mullick, Swagatam Das
Date: 2017-12-22 00:00:00
Tasks: Decision Making

The diversification (generating slightly varying separating discriminators)
of Support Vector Machines (SVMs) for boosting has proven to be a challenge due
to the strong learning nature of SVMs. Based on the insight that perturbing the
SVM kernel may help in diversifying SVMs, we propose two kernel perturbation
based boosting schemes where the kernel is modified in each round so as to
increase the resolution of the kernel-induced Reimannian metric in the vicinity
of the datapoints misclassified in the previous round. We propose a method for
identifying the disjuncts in a dataset, dispelling the dependence on rule-based
learning methods for identifying the disjuncts. We also present a new
performance measure called Geometric Small Disjunct Index (GSDI) to quantify
the performance on small disjuncts for balanced as well as class imbalanced
datasets. Experimental comparison with a variety of state-of-the-art algorithms
is carried out using the best classifiers of each type selected by a new
approach inspired by multi-criteria decision making. The proposed method is
found to outperform the contending state-of-the-art methods on different
datasets (ranging from mildly imbalanced to highly imbalanced and characterized
by varying number of disjuncts) in terms of three different performance indices
(including the proposed GSDI).