pwc-archive/papers-with-abstracts huggingface dataset:
Learning in games via reinforcement and regularization
Authors: Panayotis Mertikopoulos, William H. Sandholm
Date: 2014-07-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We investigate a class of reinforcement learning dynamics where players
adjust their strategies based on their actions' cumulative payoffs over time -
specifically, by playing mixed strategies that maximize their expected
cumulative payoff minus a regularization term. A widely studied example is
exponential reinforcement learning, a process induced by an entropic
regularization term which leads mixed strategies to evolve according to the
replicator dynamics. However, in contrast to the class of regularization
functions used to define smooth best responses in models of stochastic
fictitious play, the functions used in this paper need not be infinitely steep
at the boundary of the simplex; in fact, dropping this requirement gives rise
to an important dichotomy between steep and nonsteep cases. In this general
framework, we extend several properties of exponential learning, including the
elimination of dominated strategies, the asymptotic stability of strict Nash
equilibria, and the convergence of time-averaged trajectories in zero-sum games
with an interior Nash equilibrium.
Local and Global Convergence of a General Inertial Proximal Splitting Scheme
Authors: Patrick R. Johnstone, Pierre Moulin
Date: 2016-02-08 00:00:00

This paper is concerned with convex composite minimization problems in a
Hilbert space. In these problems, the objective is the sum of two closed,
proper, and convex functions where one is smooth and the other admits a
computationally inexpensive proximal operator. We analyze a general family of
inertial proximal splitting algorithms (GIPSA) for solving such problems. We
establish finiteness of the sum of squared increments of the iterates and
optimality of the accumulation points. Weak convergence of the entire sequence
then follows if the minimum is attained. Our analysis unifies and extends
several previous results.
  We then focus on $\ell_1$-regularized optimization, which is the ubiquitous
special case where the nonsmooth term is the $\ell_1$-norm. For certain
parameter choices, GIPSA is amenable to a local analysis for this problem. For
these choices we show that GIPSA achieves finite "active manifold
identification", i.e. convergence in a finite number of iterations to the
optimal support and sign, after which GIPSA reduces to minimizing a local
smooth function. Local linear convergence then holds under certain conditions.
We determine the rate in terms of the inertia, stepsize, and local curvature.
Our local analysis is applicable to certain recent variants of the Fast
Iterative Shrinkage-Thresholding Algorithm (FISTA), for which we establish
active manifold identification and local linear convergence. Our analysis
motivates the use of a momentum restart scheme in these FISTA variants to
obtain the optimal local linear convergence rate.
Compressed Online Dictionary Learning for Fast fMRI Decomposition
Authors: Arthur Mensch, Gaël Varoquaux, Bertrand Thirion
Date: 2016-02-08 00:00:00
Tasks: Dictionary Learning

We present a method for fast resting-state fMRI spatial decomposi-tions of
very large datasets, based on the reduction of the temporal dimension before
applying dictionary learning on concatenated individual records from groups of
subjects. Introducing a measure of correspondence between spatial
decompositions of rest fMRI, we demonstrates that time-reduced dictionary
learning produces result as reliable as non-reduced decompositions. We also
show that this reduction significantly improves computational scalability.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Authors: Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson
Date: 2016-02-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose deep distributed recurrent Q-networks (DDRQN), which enable teams
of agents to learn to solve communication-based coordination tasks. In these
tasks, the agents are not given any pre-designed communication protocol.
Therefore, in order to successfully communicate, they must first automatically
develop and agree upon their own communication protocol. We present empirical
results on two multi-agent learning problems based on well-known riddles,
demonstrating that DDRQN can successfully solve such tasks and discover elegant
communication protocols to do so. To our knowledge, this is the first time deep
reinforcement learning has succeeded in learning communication protocols. In
addition, we present ablation experiments that confirm that each of the main
components of the DDRQN architecture are critical to its success.
The happiness paradox: your friends are happier than you
Authors: Johan Bollen, Bruno Gonçalves, Ingrid van de Leemput, Guangchen Ruan
Date: 2016-02-08 00:00:00

Most individuals in social networks experience a so-called Friendship
Paradox: they are less popular than their friends on average. This effect may
explain recent findings that widespread social network media use leads to
reduced happiness. However the relation between popularity and happiness is
poorly understood. A Friendship paradox does not necessarily imply a Happiness
paradox where most individuals are less happy than their friends. Here we
report the first direct observation of a significant Happiness Paradox in a
large-scale online social network of $39,110$ Twitter users. Our results reveal
that popular individuals are indeed happier and that a majority of individuals
experience a significant Happiness paradox. The magnitude of the latter effect
is shaped by complex interactions between individual popularity, happiness, and
the fact that users cluster assortatively by level of happiness. Our results
indicate that the topology of online social networks and the distribution of
happiness in some populations can cause widespread psycho-social effects that
affect the well-being of billions of individuals.
A Variational Analysis of Stochastic Gradient Algorithms
Authors: Stephan Mandt, Matthew D. Hoffman, David M. Blei
Date: 2016-02-08 00:00:00
Tasks: Variational Inference

Stochastic Gradient Descent (SGD) is an important algorithm in machine
learning. With constant learning rates, it is a stochastic process that, after
an initial phase of convergence, generates samples from a stationary
distribution. We show that SGD with constant rates can be effectively used as
an approximate posterior inference algorithm for probabilistic modeling.
Specifically, we show how to adjust the tuning parameters of SGD such as to
match the resulting stationary distribution to the posterior. This analysis
rests on interpreting SGD as a continuous-time stochastic process and then
minimizing the Kullback-Leibler divergence between its stationary distribution
and the target posterior. (This is in the spirit of variational inference.) In
more detail, we model SGD as a multivariate Ornstein-Uhlenbeck process and then
use properties of this process to derive the optimal parameters. This
theoretical framework also connects SGD to modern scalable inference
algorithms; we analyze the recently proposed stochastic gradient Fisher scoring
under this perspective. We demonstrate that SGD with properly chosen constant
rates gives a new way to optimize hyperparameters in probabilistic models.
LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices
Authors: Marvin Coto-Jiménez, John Goddard-Close
Date: 2016-02-08 00:00:00
Tasks: Speech Synthesis

Recent developments in speech synthesis have produced systems capable of
outcome intelligible speech, but now researchers strive to create models that
more accurately mimic human voices. One such development is the incorporation
of multiple linguistic styles in various languages and accents.
  HMM-based Speech Synthesis is of great interest to many researchers, due to
its ability to produce sophisticated features with small footprint. Despite
such progress, its quality has not yet reached the level of the predominant
unit-selection approaches that choose and concatenate recordings of real
speech. Recent efforts have been made in the direction of improving these
systems.
  In this paper we present the application of Long-Short Term Memory Deep
Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order
to obtain closer spectral characteristics to those of natural speech. The
results show how HMM-voices could be improved using this approach.
Automatic Face Reenactment
Authors: Pablo Garrido, Levi Valgaerts, Ole Rehmsen, Thorsten Thormaehlen, Patrick Perez, Christian Theobalt
Date: 2016-02-08 00:00:00
Proceeding: CVPR 2014 6
Tasks: Clustering, Face Model, Face Reenactment, Face Transfer, Image Retrieval, Retrieval

We propose an image-based, facial reenactment system that replaces the face
of an actor in an existing target video with the face of a user from a source
video, while preserving the original target performance. Our system is fully
automatic and does not require a database of source expressions. Instead, it is
able to produce convincing reenactment results from a short source video
captured with an off-the-shelf camera, such as a webcam, where the user
performs arbitrary facial gestures. Our reenactment pipeline is conceived as
part image retrieval and part face transfer: The image retrieval is based on
temporal clustering of target frames and a novel image matching metric that
combines appearance and motion to select candidate frames from the source
video, while the face transfer uses a 2D warping strategy that preserves the
user's identity. Our system excels in simplicity as it does not rely on a 3D
face model, it is robust under head motion and does not require the source and
target performance to be similar. We show convincing reenactment results for
videos that we recorded ourselves and for low-quality footage taken from the
Internet.
Adaptive imputation of missing values for incomplete pattern classification
Authors: Zhun-Ga Liu, Quan Pan, Jean Dezert, Arnaud Martin
Date: 2016-02-08 00:00:00
Tasks: Attribute, Classification, General Classification, Imputation, Missing Values, Object

In classification of incomplete pattern, the missing values can either play a
crucial role in the class determination, or have only little influence (or
eventually none) on the classification results according to the context. We
propose a credal classification method for incomplete pattern with adaptive
imputation of missing values based on belief function theory. At first, we try
to classify the object (incomplete pattern) based only on the available
attribute values. As underlying principle, we assume that the missing
information is not crucial for the classification if a specific class for the
object can be found using only the available information. In this case, the
object is committed to this particular class. However, if the object cannot be
classified without ambiguity, it means that the missing values play a main role
for achieving an accurate classification. In this case, the missing values will
be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM)
techniques, and the edited pattern with the imputation is then classified. The
(original or edited) pattern is respectively classified according to each
training class, and the classification results represented by basic belief
assignments are fused with proper combination rules for making the credal
classification. The object is allowed to belong with different masses of belief
to the specific classes and meta-classes (which are particular disjunctions of
several single classes). The credal classification captures well the
uncertainty and imprecision of classification, and reduces effectively the rate
of misclassifications thanks to the introduction of meta-classes. The
effectiveness of the proposed method with respect to other classical methods is
demonstrated based on several experiments using artificial and real data sets.
God(s) Know(s): Developmental and Cross-Cultural Patterns in Children Drawings
Authors: Ksenia Konyushkova, Nikolaos Arvanitopoulos, Zhargalma Dandarova Robert, Pierre-Yves Brandt, Sabine Süsstrunk
Date: 2015-11-11 00:00:00

This paper introduces a novel approach to data analysis designed for the
needs of specialists in psychology of religion. We detect developmental and
cross-cultural patterns in children's drawings of God(s) and other supernatural
agents. We develop methods to objectively evaluate our empirical observations
of the drawings with respect to: (1) the gravity center, (2) the average
intensities of the colors \emph{green} and \emph{yellow}, (3) the use of
different colors (palette) and (4) the visual complexity of the drawings. We
find statistically significant differences across ages and countries in the
gravity centers and in the average intensities of colors. These findings
support the hypotheses of the experts and raise new questions for further
investigation.
Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer
Authors: Hamid. R. Tizhoosh, Mehrdad J. Gangeh, Hadi Tadayyon, Gregory J. Czarnota
Date: 2016-02-08 00:00:00

Quantitative ultrasound (QUS) methods provide a promising framework that can
non-invasively and inexpensively be used to predict or assess the tumour
response to cancer treatment. The first step in using the QUS methods is to
select a region of interest (ROI) inside the tumour in ultrasound images.
Manual segmentation, however, is very time consuming and tedious. In this
paper, a semi-automated approach will be proposed to roughly localize an ROI
for a tumour in ultrasound images of patients with locally advanced breast
cancer (LABC). Content-based barcodes, a recently introduced binary descriptor
based on Radon transform, were used in order to find similar cases and estimate
a bounding box surrounding the tumour. Experiments with 33 B-scan images
resulted in promising results with an accuracy of $81\%$.
Homogeneity of Cluster Ensembles
Authors: Brijnesh J. Jain
Date: 2016-02-08 00:00:00
Tasks: Clustering, Diversity

The expectation and the mean of partitions generated by a cluster ensemble
are not unique in general. This issue poses challenges in statistical inference
and cluster stability. In this contribution, we state sufficient conditions for
uniqueness of expectation and mean. The proposed conditions show that a unique
mean is neither exceptional nor generic. To cope with this issue, we introduce
homogeneity as a measure of how likely is a unique mean for a sample of
partitions. We show that homogeneity is related to cluster stability. This
result points to a possible conflict between cluster stability and diversity in
consensus clustering. To assess homogeneity in a practical setting, we propose
an efficient way to compute a lower bound of homogeneity. Empirical results
using the k-means algorithm suggest that uniqueness of the mean partition is
not exceptional for real-world data. Moreover, for samples of high homogeneity,
uniqueness can be enforced by increasing the number of data points or by
removing outlier partitions. In a broader context, this contribution can be
placed as a further step towards a statistical theory of partitions.
Using Shortlists to Support Decision Making and Improve Recommender System Performance
Authors: Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims
Date: 2015-10-26 00:00:00
Tasks: Decision Making, Movie Recommendation, Recommendation Systems

In this paper, we study shortlists as an interface component for recommender
systems with the dual goal of supporting the user's decision process, as well
as improving implicit feedback elicitation for increased recommendation
quality. A shortlist is a temporary list of candidates that the user is
currently considering, e.g., a list of a few movies the user is currently
considering for viewing. From a cognitive perspective, shortlists serve as
digital short-term memory where users can off-load the items under
consideration -- thereby decreasing their cognitive load. From a machine
learning perspective, adding items to the shortlist generates a new implicit
feedback signal as a by-product of exploration and decision making which can
improve recommendation quality. Shortlisting therefore provides additional data
for training recommendation systems without the increases in cognitive load
that requesting explicit feedback would incur.
  We perform an user study with a movie recommendation setup to compare
interfaces that offer shortlist support with those that do not. From the user
studies we conclude: (i) users make better decisions with a shortlist; (ii)
users prefer an interface with shortlist support; and (iii) the additional
implicit feedback from sessions with a shortlist improves the quality of
recommendations by nearly a factor of two.
Data-Efficient Reinforcement Learning in Continuous-State POMDPs
Authors: Rowan McAllister, Carl Edward Rasmussen
Date: 2016-02-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a data-efficient reinforcement learning algorithm resistant to
observation noise. Our method extends the highly data-efficient PILCO algorithm
(Deisenroth & Rasmussen, 2011) into partially observed Markov decision
processes (POMDPs) by considering the filtering process during policy
evaluation. PILCO conducts policy search, evaluating each policy by first
predicting an analytic distribution of possible system trajectories. We
additionally predict trajectories w.r.t. a filtering process, achieving
significantly higher performance than combining a filter with a policy
optimised by the original (unfiltered) framework. Our test setup is the
cartpole swing-up task with sensor noise, which involves nonlinear dynamics and
requires nonlinear control.
A Semi-Automated Method for Object Segmentation in Infant's Egocentric Videos to Study Object Perception
Authors: Qazaleh Mirsharif, Sidharth Sadani, Shishir Shah, Hanako Yoshida, Joseph Burling
Date: 2016-02-08 00:00:00
Tasks: Object, Optical Flow Estimation, Segmentation, Semantic Segmentation

Object segmentation in infant's egocentric videos is a fundamental step in
studying how children perceive objects in early stages of development. From the
computer vision perspective, object segmentation in such videos pose quite a
few challenges because the child's view is unfocused, often with large head
movements, effecting in sudden changes in the child's point of view which leads
to frequent change in object properties such as size, shape and illumination.
In this paper, we develop a semi-automated, domain specific, method to address
these concerns and facilitate the object annotation process for cognitive
scientists allowing them to select and monitor the object under segmentation.
The method starts with an annotation from the user of the desired object and
employs graph cut segmentation and optical flow computation to predict the
object mask for subsequent video frames automatically. To maintain accuracy, we
use domain specific heuristic rules to re-initialize the program with new user
input whenever object properties change dramatically. The evaluations
demonstrate the high speed and accuracy of the presented method for object
segmentation in voluminous egocentric videos. We apply the proposed method to
investigate potential patterns in object distribution in child's view at
progressive ages.
Multi-view Kernel Completion
Authors: Sahely Bhadra, Samuel Kaski, Juho Rousu
Date: 2016-02-08 00:00:00

In this paper, we introduce the first method that (1) can complete kernel
matrices with completely missing rows and columns as opposed to individual
missing kernel values, (2) does not require any of the kernels to be complete a
priori, and (3) can tackle non-linear kernels. These aspects are necessary in
practical applications such as integrating legacy data sets, learning under
sensor failures and learning when measurements are costly for some of the
views. The proposed approach predicts missing rows by modelling both
within-view and between-view relationships among kernel values. We show, both
on simulated data and real world data, that the proposed method outperforms
existing techniques in the restricted settings where they are available, and
extends applicability to new settings.
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
Authors: André F. T. Martins, Ramón Fernandez Astudillo
Date: 2016-02-05 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Natural Language Inference

We propose sparsemax, a new activation function similar to the traditional
softmax, but able to output sparse probabilities. After deriving its
properties, we show how its Jacobian can be efficiently computed, enabling its
use in a network trained with backpropagation. Then, we propose a new smooth
and convex loss function which is the sparsemax analogue of the logistic loss.
We reveal an unexpected connection between this new loss and the Huber
classification loss. We obtain promising empirical results in multi-label
classification problems and in attention-based neural networks for natural
language inference. For the latter, we achieve a similar performance as the
traditional softmax, but with a selective, more compact, attention focus.
Co-Occurrence Patterns in the Voynich Manuscript
Authors: Torsten Timm
Date: 2016-01-27 00:00:00

The Voynich Manuscript is a medieval book written in an unknown script. This
paper studies the distribution of similarly spelled words in the Voynich
Manuscript. It shows that the distribution of words within the manuscript is
not compatible with natural languages.
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Authors: Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi
Date: 2016-02-08 00:00:00

The problem of learning a sparse model is conceptually interpreted as the
process of identifying active features/samples and then optimizing the model
over them. Recently introduced safe screening allows us to identify a part of
non-active features/samples. So far, safe screening has been individually
studied either for feature screening or for sample screening. In this paper, we
introduce a new approach for safely screening features and samples
simultaneously by alternatively iterating feature and sample screening steps. A
significant advantage of considering them simultaneously rather than
individually is that they have a synergy effect in the sense that the results
of the previous safe feature screening can be exploited for improving the next
safe sample screening performances, and vice-versa. We first theoretically
investigate the synergy effect, and then illustrate the practical advantage
through intensive numerical experiments for problems with large numbers of
features and samples.
Autonomous Perceptron Neural Network Inspired from Quantum computing
Authors: M. Zidan, A. Sagheer, N. Metwally
Date: 2015-10-02 00:00:00

This abstract will be modified after correcting the minor error in Eq.(2)
Particle Swarm Optimized Power Consumption of Trilateration
Authors: Hussein S. Al-Olimat, Robert C. Green II, Mansoor Alam, Vijay Devabhaktuni, Wei Cheng
Date: 2016-02-08 00:00:00

Trilateration-based localization (TBL) has become a corner stone of modern
technology. This study formulates the concern on how wireless sensor networks
can take advantage of the computational intelligent techniques using both
single- and multi-objective particle swarm optimization (PSO) with an overall
aim of concurrently minimizing the required time for localization, minimizing
energy consumed during localization, and maximizing the number of nodes fully
localized through the adjustment of wireless sensor transmission ranges while
using TBL process. A parameter-study of the applied PSO variants is performed,
leading to results that show algorithmic improvements of up to 32% in the
evaluated objectives.