pwc-archive/papers-with-abstracts huggingface dataset:
Multi-armed Bandit Problems with Strategic Arms
Authors: Mark Braverman, Jieming Mao, Jon Schneider, S. Matthew Weinberg
Date: 2017-06-27 00:00:00

We study a strategic version of the multi-armed bandit problem, where each
arm is an individual strategic agent and we, the principal, pull one arm each
round. When pulled, the arm receives some private reward $v_a$ and can choose
an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All
non-pulled arms get reward $0$. Each strategic arm tries to maximize its own
utility over the course of $T$ rounds. Our goal is to design an algorithm for
the principal incentivizing these arms to pass on as much of their private
rewards as possible.
  When private rewards are stochastically drawn each round ($v_a^t \leftarrow
D_a$), we show that:
  - Algorithms that perform well in the classic adversarial multi-armed bandit
setting necessarily perform poorly: For all algorithms that guarantee low
regret in an adversarial setting, there exist distributions $D_1,\ldots,D_k$
and an approximate Nash equilibrium for the arms where the principal receives
reward $o(T)$.
  - Still, there exists an algorithm for the principal that induces a game
among the arms where each arm has a dominant strategy. When each arm plays its
dominant strategy, the principal sees expected reward $\mu'T - o(T)$, where
$\mu'$ is the second-largest of the means $\mathbb{E}[D_{a}]$. This algorithm
maintains its guarantee if the arms are non-strategic ($x_a = v_a$), and also
if there is a mix of strategic and non-strategic arms.
Reconstructing Subject-Specific Effect Maps
Authors: Ender Konukoglu, Ben Glocker
Date: 2017-01-10 00:00:00
Tasks: Diagnostic

Predictive models allow subject-specific inference when analyzing disease
related alterations in neuroimaging data. Given a subject's data, inference can
be made at two levels: global, i.e. identifiying condition presence for the
subject, and local, i.e. detecting condition effect on each individual
measurement extracted from the subject's data. While global inference is widely
used, local inference, which can be used to form subject-specific effect maps,
is rarely used because existing models often yield noisy detections composed of
dispersed isolated islands. In this article, we propose a reconstruction
method, named RSM, to improve subject-specific detections of predictive
modeling approaches and in particular, binary classifiers. RSM specifically
aims to reduce noise due to sampling error associated with using a finite
sample of examples to train classifiers. The proposed method is a wrapper-type
algorithm that can be used with different binary classifiers in a diagnostic
manner, i.e. without information on condition presence. Reconstruction is posed
as a Maximum-A-Posteriori problem with a prior model whose parameters are
estimated from training data in a classifier-specific fashion. Experimental
evaluation is performed on synthetically generated data and data from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results on
synthetic data demonstrate that using RSM yields higher detection accuracy
compared to using models directly or with bootstrap averaging. Analyses on the
ADNI dataset show that RSM can also improve correlation between
subject-specific detections in cortical thickness data and non-imaging markers
of Alzheimer's Disease (AD), such as the Mini Mental State Examination Score
and Cerebrospinal Fluid amyloid-$\beta$ levels. Further reliability studies on
the longitudinal ADNI dataset show improvement on detection reliability when
RSM is used.
Acoustic Modeling Using a Shallow CNN-HTSVM Architecture
Authors: Christopher Dane Shulby, Martha Dais Ferreira, Rodrigo F. de Mello, Sandra Maria Aluisio
Date: 2017-06-27 00:00:00
Tasks: speech-recognition, Speech Recognition

High-accuracy speech recognition is especially challenging when large
datasets are not available. It is possible to bridge this gap with careful and
knowledge-driven parsing combined with the biologically inspired CNN and the
learning guarantees of the Vapnik Chervonenkis (VC) theory. This work presents
a Shallow-CNN-HTSVM (Hierarchical Tree Support Vector Machine classifier)
architecture which uses a predefined knowledge-based set of rules with
statistical machine learning techniques. Here we show that gross errors present
even in state-of-the-art systems can be avoided and that an accurate acoustic
model can be built in a hierarchical fashion. The CNN-HTSVM acoustic model
outperforms traditional GMM-HMM models and the HTSVM structure outperforms a
MLP multi-class classifier. More importantly we isolate the performance of the
acoustic model and provide results on both the frame and phoneme level
considering the true robustness of the model. We show that even with a small
amount of data accurate and robust recognition rates can be obtained.
Strategyproof Mechanisms for Additively Separable Hedonic Games and Fractional Hedonic Games
Authors: Michele Flammini, Gianpiero Monaco, Qiang Zhang
Date: 2017-06-27 00:00:00

Additively separable hedonic games and fractional hedonic games have received
considerable attention. They are coalition forming games of selfish agents
based on their mutual preferences. Most of the work in the literature
characterizes the existence and structure of stable outcomes (i.e., partitions
in coalitions), assuming that preferences are given. However, there is little
discussion on this assumption. In fact, agents receive different utilities if
they belong to different partitions, and thus it is natural for them to declare
their preferences strategically in order to maximize their benefit. In this
paper we consider strategyproof mechanisms for additively separable hedonic
games and fractional hedonic games, that is, partitioning methods without
payments such that utility maximizing agents have no incentive to lie about
their true preferences. We focus on social welfare maximization and provide
several lower and upper bounds on the performance achievable by strategyproof
mechanisms for general and specific additive functions. In most of the cases we
provide tight or asymptotically tight results. All our mechanisms are simple
and can be computed in polynomial time. Moreover, all the lower bounds are
unconditional, that is, they do not rely on any computational or complexity
assumptions.
Unsupervised Learning via Total Correlation Explanation
Authors: Greg Ver Steeg
Date: 2017-06-27 00:00:00

Learning by children and animals occurs effortlessly and largely without
obvious supervision. Successes in automating supervised learning have not
translated to the more ambiguous realm of unsupervised learning where goals and
labels are not provided. Barlow (1961) suggested that the signal that brains
leverage for unsupervised learning is dependence, or redundancy, in the sensory
environment. Dependence can be characterized using the information-theoretic
multivariate mutual information measure called total correlation. The principle
of Total Cor-relation Ex-planation (CorEx) is to learn representations of data
that "explain" as much dependence in the data as possible. We review some
manifestations of this principle along with successes in unsupervised learning
problems across diverse domains including human behavior, biology, and
language.
An Approach for Self-Training Audio Event Detectors Using Web Data
Authors: Benjamin Elizalde, Ankit Shah, Siddharth Dalmia, Min Hun Lee, Rohan Badlani, Anurag Kumar, Bhiksha Raj, Ian Lane
Date: 2016-09-20 00:00:00
Tasks: Diversity, Event Detection

Audio Event Detection (AED) aims to recognize sounds within audio and video
recordings. AED employs machine learning algorithms commonly trained and tested
on annotated datasets. However, available datasets are limited in number of
samples and hence it is difficult to model acoustic diversity. Therefore, we
propose combining labeled audio from a dataset and unlabeled audio from the web
to improve the sound models. The audio event detectors are trained on the
labeled audio and ran on the unlabeled audio downloaded from YouTube. Whenever
the detectors recognized any of the known sounds with high confidence, the
unlabeled audio was use to re-train the detectors. The performance of the
re-trained detectors is compared to the one from the original detectors using
the annotated test set. Results showed an improvement of the AED, and uncovered
challenges of using web audio from videos.
Sample, computation vs storage tradeoffs for classification using tensor subspace models
Authors: Mohammadhossein Chaghazardi, Shuchin Aeron
Date: 2017-06-18 00:00:00
Tasks: General Classification

In this paper, we exhibit the tradeoffs between the (training) sample,
computation and storage complexity for the problem of supervised classification
using signal subspace estimation. Our main tool is the use of tensor subspaces,
i.e. subspaces with a Kronecker structure, for embedding the data into lower
dimensions. Among the subspaces with a Kronecker structure, we show that using
subspaces with a hierarchical structure for representing data leads to improved
tradeoffs. One of the main reasons for the improvement is that embedding data
into these hierarchical Kronecker structured subspaces prevents overfitting at
higher latent dimensions.
Classical Music Clustering Based on Acoustic Features
Authors: Xindi Wang, Syed Arefinul Haque
Date: 2017-06-27 00:00:00
Tasks: Clustering

In this paper we cluster 330 classical music pieces collected from MusicNet
database based on their musical note sequence. We use shingling and chord
trajectory matrices to create signature for each music piece and performed
spectral clustering to find the clusters. Based on different resolution, the
output clusters distinctively indicate composition from different classical
music era and different composing style of the musicians.
Cross-Country Skiing Gears Classification using Deep Learning
Authors: Aliaa Rassem, Mohammed El-Beltagy, Mohamed Saleh
Date: 2017-06-27 00:00:00
Tasks: Activity Recognition, Classification, Deep Learning, General Classification, Human Activity Recognition

Human Activity Recognition has witnessed a significant progress in the last
decade. Although a great deal of work in this field goes in recognizing normal
human activities, few studies focused on identifying motion in sports.
Recognizing human movements in different sports has high impact on
understanding the different styles of humans in the play and on improving their
performance. As deep learning models proved to have good results in many
classification problems, this paper will utilize deep learning to classify
cross-country skiing movements, known as gears, collected using a 3D
accelerometer. It will also provide a comparison between different deep
learning models such as convolutional and recurrent neural networks versus
standard multi-layer perceptron. Results show that deep learning is more
effective and has the highest classification accuracy.
Invariant components of synergy, redundancy, and unique information among three variables
Authors: Giuseppe Pica, Eugenio Piasini, Daniel Chicharro, Stefano Panzeri
Date: 2017-06-27 00:00:00

In a system of three stochastic variables, the Partial Information
Decomposition (PID) of Williams and Beer dissects the information that two
variables (sources) carry about a third variable (target) into nonnegative
information atoms that describe redundant, unique, and synergistic modes of
dependencies among the variables. However, the classification of the three
variables into two sources and one target limits the dependency modes that can
be quantitatively resolved, and does not naturally suit all systems. Here, we
extend the PID to describe trivariate modes of dependencies in full generality,
without introducing additional decomposition axioms or making assumptions about
the target/source nature of the variables. By comparing different PID lattices
of the same system, we unveil a finer PID structure made of seven nonnegative
information subatoms that are invariant to different target/source
classifications and that are sufficient to construct any PID lattice. This
finer structure naturally splits redundant information into two nonnegative
components: the source redundancy, which arises from the pairwise correlations
between the source variables, and the non-source redundancy, which does not,
and relates to the synergistic information the sources carry about the target.
The invariant structure is also sufficient to construct the system's entropy,
hence it characterizes completely all the interdependencies in the system.
Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling
Authors: Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, Charudatta Phatak
Date: 2017-06-27 00:00:00

Analytical electron microscopy and spectroscopy of biological specimens,
polymers, and other beam sensitive materials has been a challenging area due to
irradiation damage. There is a pressing need to develop novel imaging and
spectroscopic imaging methods that will minimize such sample damage as well as
reduce the data acquisition time. The latter is useful for high-throughput
analysis of materials structure and chemistry. In this work, we present a novel
machine learning based method for dynamic sparse sampling of EDS data using a
scanning electron microscope. Our method, based on the supervised learning
approach for dynamic sampling algorithm and neural networks based
classification of EDS data, allows a dramatic reduction in the total sampling
of up to 90%, while maintaining the fidelity of the reconstructed elemental
maps and spectroscopic data. We believe this approach will enable imaging and
elemental mapping of materials that would otherwise be inaccessible to these
analysis techniques.
Unsupervised Feature Selection Based on Space Filling Concept
Authors: Mohamed Laib, Mikhail Kanevski
Date: 2017-06-27 00:00:00
Tasks: feature selection

The paper deals with the adaptation of a new measure for the unsupervised
feature selection problems. The proposed measure is based on space filling
concept and is called the coverage measure. This measure was used for judging
the quality of an experimental space filling design. In the present work, the
coverage measure is adapted for selecting the smallest informative subset of
variables by reducing redundancy in data. This paper proposes a simple analogy
to apply this measure. It is implemented in a filter algorithm for unsupervised
feature selection problems.
  The proposed filter algorithm is robust with high dimensional data and can be
implemented without extra parameters. Further, it is tested with simulated data
and real world case studies including environmental data and hyperspectral
image. Finally, the results are evaluated by using random forest algorithm.
When Neurons Fail
Authors: El Mahdi El Mhamdi, Rachid Guerraoui
Date: 2017-06-27 00:00:00

We view a neural network as a distributed system of which neurons can fail
independently, and we evaluate its robustness in the absence of any (recovery)
learning phase. We give tight bounds on the number of neurons that can fail
without harming the result of a computation. To determine our bounds, we
leverage the fact that neural activation functions are Lipschitz-continuous.
Our bound is on a quantity, we call the \textit{Forward Error Propagation},
capturing how much error is propagated by a neural network when a given number
of components is failing, computing this quantity only requires looking at the
topology of the network, while experimentally assessing the robustness of a
network requires the costly experiment of looking at all the possible inputs
and testing all the possible configurations of the network corresponding to
different failure situations, facing a discouraging combinatorial explosion.
  We distinguish the case of neurons that can fail and stop their activity
(crashed neurons) from the case of neurons that can fail by transmitting
arbitrary values (Byzantine neurons). Interestingly, as we show in the paper,
our bound can easily be extended to the case where synapses can fail.
  We show how our bound can be leveraged to quantify the effect of memory cost
reduction on the accuracy of a neural network, to estimate the amount of
information any neuron needs from its preceding layer, enabling thereby a
boosting scheme that prevents neurons from waiting for unnecessary signals. We
finally discuss the trade-off between neural networks robustness and learning
cost.
Rate-Distortion Classification for Self-Tuning IoT Networks
Authors: Davide Zordan, Michele Rossi, Michele Zorzi
Date: 2017-06-27 00:00:00
Tasks: Classification, General Classification

Many future wireless sensor networks and the Internet of Things are expected
to follow a software defined paradigm, where protocol parameters and behaviors
will be dynamically tuned as a function of the signal statistics. New protocols
will be then injected as a software as certain events occur. For instance, new
data compressors could be (re)programmed on-the-fly as the monitored signal
type or its statistical properties change. We consider a lossy compression
scenario, where the application tolerates some distortion of the gathered
signal in return for improved energy efficiency. To reap the full benefits of
this paradigm, we discuss an automatic sensor profiling approach where the
signal class, and in particular the corresponding rate-distortion curve, is
automatically assessed using machine learning tools (namely, support vector
machines and neural networks). We show that this curve can be reliably
estimated on-the-fly through the computation of a small number (from ten to
twenty) of statistical features on time windows of a few hundreds samples.
Gabor frames and deep scattering networks in audio processing
Authors: Roswitha Bammer, Monika Dörfler, Pavol Harar
Date: 2017-06-27 00:00:00

This paper introduces Gabor scattering, a feature extractor based on Gabor frames and Mallat's scattering transform. By using a simple signal model for audio signals specific properties of Gabor scattering are studied. It is shown that for each layer, specific invariances to certain signal characteristics occur. Furthermore, deformation stability of the coefficient vector generated by the feature extractor is derived by using a decoupling technique which exploits the contractivity of general scattering networks. Deformations are introduced as changes in spectral shape and frequency modulation. The theoretical results are illustrated by numerical examples and experiments. Numerical evidence is given by evaluation on a synthetic and a "real" data set, that the invariances encoded by the Gabor scattering transform lead to higher performance in comparison with just using Gabor transform, especially when few training samples are available.
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Authors: Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
Date: 2017-06-27 00:00:00
Tasks: Time Series, Time Series Analysis

Traditional linear methods for forecasting multivariate time series are not
able to satisfactorily model the non-linear dependencies that may exist in
non-Gaussian series. We build on the theory of learning vector-valued functions
in the reproducing kernel Hilbert space and develop a method for learning
prediction functions that accommodate such non-linearities. The method not only
learns the predictive function but also the matrix-valued kernel underlying the
function search space directly from the data. Our approach is based on learning
multiple matrix-valued kernels, each of those composed of a set of input
kernels and a set of output kernels learned in the cone of positive
semi-definite matrices. In addition to superior predictive performance in the
presence of strong non-linearities, our method also recovers the hidden dynamic
relationships between the series and thus is a new alternative to existing
graphical Granger techniques.
Recurrent Residual Learning for Action Recognition
Authors: Ahsan Iqbal, Alexander Richard, Hilde Kuehne, Juergen Gall
Date: 2017-06-27 00:00:00
Tasks: Action Recognition, image-classification, Image Classification, Temporal Action Localization

Action recognition is a fundamental problem in computer vision with a lot of
potential applications such as video surveillance, human computer interaction,
and robot learning. Given pre-segmented videos, the task is to recognize
actions happening within videos. Historically, hand crafted video features were
used to address the task of action recognition. With the success of Deep
ConvNets as an image analysis method, a lot of extensions of standard ConvNets
were purposed to process variable length video data. In this work, we propose a
novel recurrent ConvNet architecture called recurrent residual networks to
address the task of action recognition. The approach extends ResNet, a state of
the art model for image classification. While the original formulation of
ResNet aims at learning spatial residuals in its layers, we extend the approach
by introducing recurrent connections that allow to learn a spatio-temporal
residual. In contrast to fully recurrent networks, our temporal connections
only allow a limited range of preceding frames to contribute to the output for
the current frame, enabling efficient training and inference as well as
limiting the temporal context to a reasonable local range around each frame. On
a large-scale action recognition dataset, we show that our model improves over
both, the standard ResNet architecture and a ResNet extended by a fully
recurrent layer.
Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis
Authors: Pengyuan Lyu, Xiang Bai, Cong Yao, Zhen Zhu, Tengteng Huang, Wenyu Liu
Date: 2017-06-27 00:00:00
Tasks: Image-to-Image Translation, Translation

In this paper, we investigate the Chinese calligraphy synthesis problem:
synthesizing Chinese calligraphy images with specified style from standard
font(eg. Hei font) images (Fig. 1(a)). Recent works mostly follow the stroke
extraction and assemble pipeline which is complex in the process and limited by
the effect of stroke extraction. We treat the calligraphy synthesis problem as
an image-to-image translation problem and propose a deep neural network based
model which can generate calligraphy images from standard font images directly.
Besides, we also construct a large scale benchmark that contains various styles
for Chinese calligraphy synthesis. We evaluate our method as well as some
baseline methods on the proposed dataset, and the experimental results
demonstrate the effectiveness of our proposed model.
Topometric Localization with Deep Learning
Authors: Gabriel L. Oliveira, Noha Radwan, Wolfram Burgard, Thomas Brox
Date: 2017-06-27 00:00:00
Tasks: Deep Learning, Visual Localization, Visual Odometry

Compared to LiDAR-based localization methods, which provide high accuracy but
rely on expensive sensors, visual localization approaches only require a camera
and thus are more cost-effective while their accuracy and reliability typically
is inferior to LiDAR-based methods. In this work, we propose a vision-based
localization approach that learns from LiDAR-based localization methods by
using their output as training data, thus combining a cheap, passive sensor
with an accuracy that is on-par with LiDAR-based localization. The approach
consists of two deep networks trained on visual odometry and topological
localization, respectively, and a successive optimization to combine the
predictions of these two networks. We evaluate the approach on a new
challenging pedestrian-based dataset captured over the course of six months in
varying weather conditions with a high degree of noise. The experiments
demonstrate that the localization errors are up to 10 times smaller than with
traditional vision-based localization methods.