pwc-archive/papers-with-abstracts huggingface dataset:
Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets
Authors: Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff, Margrit Betke
Date: 2016-05-02 00:00:00
Tasks: Pose Estimation

Our work introduces a novel way to increase pose estimation accuracy by
discovering parts from unannotated regions of training images. Discovered parts
are used to generate more accurate appearance likelihoods for traditional
part-based models like Pictorial Structures [13] and its derivatives. Our
experiments on images of a hawkmoth in flight show that our proposed approach
significantly improves over existing work [27] for this application, while also
being more generally applicable. Our proposed approach localizes landmarks at
least twice as accurately as a baseline based on a Mixture of Pictorial
Structures (MPS) model. Our unique High-Resolution Moth Flight (HRMF) dataset
is made publicly available with annotations.
Predicting online extremism, content adopters, and interaction reciprocity
Authors: Emilio Ferrara, Wen-Qiang Wang, Onur Varol, Alessandro Flammini, Aram Galstyan
Date: 2016-05-02 00:00:00

We present a machine learning framework that leverages a mixture of metadata,
network, and temporal features to detect extremist users, and predict content
adopters and interaction reciprocity in social media. We exploit a unique
dataset containing millions of tweets generated by more than 25 thousand users
who have been manually identified, reported, and suspended by Twitter due to
their involvement with extremist campaigns. We also leverage millions of tweets
generated by a random sample of 25 thousand regular users who were exposed to,
or consumed, extremist content. We carry out three forecasting tasks, (i) to
detect extremist users, (ii) to estimate whether regular users will adopt
extremist content, and finally (iii) to predict whether users will reciprocate
contacts initiated by extremists. All forecasting tasks are set up in two
scenarios: a post hoc (time independent) prediction task on aggregated data,
and a simulated real-time prediction task. The performance of our framework is
extremely promising, yielding in the different forecasting scenarios up to 93%
AUC for extremist user detection, up to 80% AUC for content adoption
prediction, and finally up to 72% AUC for interaction reciprocity forecasting.
We conclude by providing a thorough feature analysis that helps determine which
are the emerging signals that provide predictive power in different scenarios.
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis
Authors: Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic
Date: 2016-04-11 00:00:00
Tasks: Domain Adaptation, Gaussian Processes, General Classification

We present a novel approach for supervised domain adaptation that is based
upon the probabilistic framework of Gaussian processes (GPs). Specifically, we
introduce domain-specific GPs as local experts for facial expression
classification from face images. The adaptation of the classifier is
facilitated in probabilistic fashion by conditioning the target expert on
multiple source experts. Furthermore, in contrast to existing adaptation
approaches, we also learn a target expert from available target data solely.
Then, a single and confident classifier is obtained by combining the
predictions from multiple experts based on their confidence. Learning of the
model is efficient and requires no retraining/reweighting of the source
classifiers. We evaluate the proposed approach on two publicly available
datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression
classification. To this end, we perform adaptation of two contextual factors:
'where' (view) and 'who' (subject). We show in our experiments that the
proposed approach consistently outperforms both source and target classifiers,
while using as few as 30 target examples. It also outperforms the
state-of-the-art approaches for supervised domain adaptation.
Decentralized Q-Learning for Stochastic Teams and Games
Authors: Gürdal Arslan, Serdar Yüksel
Date: 2015-06-25 00:00:00
Tasks: Q-Learning

There are only a few learning algorithms applicable to stochastic dynamic
teams and games which generalize Markov decision processes to decentralized
stochastic control problems involving possibly self-interested decision makers.
Learning in games is generally difficult because of the non-stationary
environment in which each decision maker aims to learn its optimal decisions
with minimal information in the presence of the other decision makers who are
also learning. In stochastic dynamic games, learning is more challenging
because, while learning, the decision makers alter the state of the system and
hence the future cost. In this paper, we present decentralized Q-learning
algorithms for stochastic games, and study their convergence for the weakly
acyclic case which includes team problems as an important special case. The
algorithm is decentralized in that each decision maker has access to only its
local information, the state information, and the local cost realizations;
furthermore, it is completely oblivious to the presence of other decision
makers. We show that these algorithms converge to equilibrium policies almost
surely in large classes of stochastic games.
Graph Clustering Bandits for Recommendation
Authors: Shuai Li, Claudio Gentile, Alexandros Karatzoglou
Date: 2016-05-02 00:00:00
Tasks: Clustering, Graph Clustering, Multi-Armed Bandits, Recommendation Systems

We investigate an efficient context-dependent clustering technique for
recommender systems based on exploration-exploitation strategies through
multi-armed bandits over multiple users. Our algorithm dynamically groups users
based on their observed behavioral similarity during a sequence of logged
activities. In doing so, the algorithm reacts to the currently served user by
shaping clusters around him/her but, at the same time, it explores the
generation of clusters over users which are not currently engaged. We motivate
the effectiveness of this clustering policy, and provide an extensive empirical
analysis on real-world datasets, showing scalability and improved prediction
performance over state-of-the-art methods for sequential clustering of users in
multi-armed bandit scenarios.
Sequential visibility-graph motifs
Authors: Jacopo Iacovacci, Lucas Lacasa
Date: 2015-12-01 00:00:00
Tasks: Time Series, Time Series Analysis

Visibility algorithms transform time series into graphs and encode dynamical
information in their topology, paving the way for graph-theoretical time series
analysis as well as building a bridge between nonlinear dynamics and network
science. In this work we introduce and study the concept of sequential
visibility graph motifs, smaller substructures of n consecutive nodes that
appear with characteristic frequencies. We develop a theory to compute in an
exact way the motif profiles associated to general classes of deterministic and
stochastic dynamics. We find that this simple property is indeed a highly
informative and computationally efficient feature capable to distinguish among
different dynamics and robust against noise contamination. We finally confirm
that it can be used in practice to perform unsupervised learning, by extracting
motif profiles from experimental heart-rate series and being able, accordingly,
to disentangle meditative from other relaxation states. Applications of this
general theory include the automatic classification and description of
physical, biological, and financial time series.
Comparison of Optimization Methods in Optical Flow Estimation
Authors: Noranart Vesdapunt, Utkarsh Sinha
Date: 2016-05-02 00:00:00
Tasks: Optical Flow Estimation

Optical flow estimation is a widely known problem in computer vision
introduced by Gibson, J.J(1950) to describe the visual perception of human by
stimulus objects. Estimation of optical flow model can be achieved by solving
for the motion vectors from region of interest in the the different timeline.
In this paper, we assumed slightly uniform change of velocity between two
nearby frames, and solve the optical flow problem by traditional method,
Lucas-Kanade(1981). This method performs minimization of errors between
template and target frame warped back onto the template. Solving minimization
steps requires optimization methods which have diverse convergence rate and
error. We explored first and second order optimization methods, and compare
their results with Gauss-Newton method in Lucas-Kanade. We generated 105 videos
with 10,500 frames by synthetic objects, and 10 videos with 1,000 frames from
real world footage. Our experimental results could be used as tuning parameters
for Lucas-Kanade method.
Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model based on Generalized Gaussian Priors
Authors: Ningning Zhao, Adrian Basarab, Denis Kouame, Jean-Yves Tourneret
Date: 2014-12-08 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This paper proposes a joint segmentation and deconvolution Bayesian method
for medical ultrasound (US) images. Contrary to piecewise homogeneous images,
US images exhibit heavy characteristic speckle patterns correlated with the
tissue structures. The generalized Gaussian distribution (GGD) has been shown
to be one of the most relevant distributions for characterizing the speckle in
US images. Thus, we propose a GGD-Potts model defined by a label map coupling
US image segmentation and deconvolution. The Bayesian estimators of the unknown
model parameters, including the US image, the label map and all the
hyperparameters are difficult to be expressed in closed form. Thus, we
investigate a Gibbs sampler to generate samples distributed according to the
posterior of interest. These generated samples are finally used to compute the
Bayesian estimators of the unknown parameters. The performance of the proposed
Bayesian model is compared with existing approaches via several experiments
conducted on realistic synthetic data and in vivo US images.
NetVLAD: CNN architecture for weakly supervised place recognition
Authors: Relja Arandjelović, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivic
Date: 2015-11-23 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Retrieval, Retrieval, Visual Place Recognition

We tackle the problem of large scale visual place recognition, where the task
is to quickly and accurately recognize the location of a given query
photograph. We present the following three principal contributions. First, we
develop a convolutional neural network (CNN) architecture that is trainable in
an end-to-end manner directly for the place recognition task. The main
component of this architecture, NetVLAD, is a new generalized VLAD layer,
inspired by the "Vector of Locally Aggregated Descriptors" image representation
commonly used in image retrieval. The layer is readily pluggable into any CNN
architecture and amenable to training via backpropagation. Second, we develop a
training procedure, based on a new weakly supervised ranking loss, to learn
parameters of the architecture in an end-to-end manner from images depicting
the same places over time downloaded from Google Street View Time Machine.
Finally, we show that the proposed architecture significantly outperforms
non-learnt image representations and off-the-shelf CNN descriptors on two
challenging place recognition benchmarks, and improves over current
state-of-the-art compact image representations on standard image retrieval
benchmarks.
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Authors: Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi, Andreas Krause
Date: 2016-05-02 00:00:00
Tasks: Clustering, Navigate

Faced with massive data, is it possible to trade off (statistical) risk, and
(computational) space and time? This challenge lies at the heart of large-scale
machine learning. Using k-means clustering as a prototypical unsupervised
learning problem, we show how we can strategically summarize the data (control
space) in order to trade off risk and time when data is generated by a
probabilistic model. Our summarization is based on coreset constructions from
computational geometry. We also develop an algorithm, TRAM, to navigate the
space/time/data/risk tradeoff in practice. In particular, we show that for a
fixed risk (or data size), as the data size increases (resp. risk increases)
the running time of TRAM decreases. Our extensive experiments on real data sets
demonstrate the existence and practical utility of such tradeoffs, not only for
k-means but also for Gaussian Mixture Models.
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition
Authors: Rohit Prabhavalkar, Ouais Alsharif, Antoine Bruguier, Ian McGraw
Date: 2016-03-25 00:00:00
Tasks: Model Compression, speech-recognition, Speech Recognition

We study the problem of compressing recurrent neural networks (RNNs). In
particular, we focus on the compression of RNN acoustic models, which are
motivated by the goal of building compact and accurate speech recognition
systems which can be run efficiently on mobile devices. In this work, we
present a technique for general recurrent model compression that jointly
compresses both recurrent and non-recurrent inter-layer weight matrices. We
find that the proposed technique allows us to reduce the size of our Long
Short-Term Memory (LSTM) acoustic model to a third of its original size with
negligible loss in accuracy.
Linear-time Outlier Detection via Sensitivity
Authors: Mario Lucic, Olivier Bachem, Andreas Krause
Date: 2016-05-02 00:00:00
Tasks: Clustering, Outlier Detection, Sensitivity

Outliers are ubiquitous in modern data sets. Distance-based techniques are a
popular non-parametric approach to outlier detection as they require no prior
assumptions on the data generating distribution and are simple to implement.
Scaling these techniques to massive data sets without sacrificing accuracy is a
challenging task. We propose a novel algorithm based on the intuition that
outliers have a significant influence on the quality of divergence-based
clustering solutions. We propose sensitivity - the worst-case impact of a data
point on the clustering objective - as a measure of outlierness. We then prove
that influence, a (non-trivial) upper-bound on the sensitivity, can be computed
by a simple linear time algorithm. To scale beyond a single machine, we propose
a communication efficient distributed algorithm. In an extensive experimental
evaluation, we demonstrate the effectiveness and establish the statistical
significance of the proposed approach. In particular, it outperforms the most
popular distance-based approaches while being several orders of magnitude
faster.
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
Authors: Mario Lucic, Olivier Bachem, Andreas Krause
Date: 2015-08-21 00:00:00
Tasks: Clustering

Coresets are efficient representations of data sets such that models trained
on the coreset are provably competitive with models trained on the original
data set. As such, they have been successfully used to scale up clustering
models such as K-Means and Gaussian mixture models to massive data sets.
However, until now, the algorithms and the corresponding theory were usually
specific to each clustering problem.
  We propose a single, practical algorithm to construct strong coresets for a
large class of hard and soft clustering problems based on Bregman divergences.
This class includes hard clustering with popular distortion measures such as
the Squared Euclidean distance, the Mahalanobis distance, KL-divergence and
Itakura-Saito distance. The corresponding soft clustering problems are directly
related to popular mixture models due to a dual relationship between Bregman
divergences and Exponential family distributions. Our theoretical results
further imply a randomized polynomial-time approximation scheme for hard
clustering. We demonstrate the practicality of the proposed algorithm in an
empirical evaluation.
Fuzzy clustering of distribution-valued data using adaptive L2 Wasserstein distances
Authors: Antonio Irpino, Francisco De Carvalho, Rosanna Verde
Date: 2016-05-02 00:00:00
Tasks: Clustering, Variable Selection

Distributional (or distribution-valued) data are a new type of data arising
from several sources and are considered as realizations of distributional
variables. A new set of fuzzy c-means algorithms for data described by
distributional variables is proposed.
  The algorithms use the $L2$ Wasserstein distance between distributions as
dissimilarity measures. Beside the extension of the fuzzy c-means algorithm for
distributional data, and considering a decomposition of the squared $L2$
Wasserstein distance, we propose a set of algorithms using different automatic
way to compute the weights associated with the variables as well as with their
components, globally or cluster-wise. The relevance weights are computed in the
clustering process introducing product-to-one constraints.
  The relevance weights induce adaptive distances expressing the importance of
each variable or of each component in the clustering process, acting also as a
variable selection method in clustering. We have tested the proposed algorithms
on artificial and real-world data. Results confirm that the proposed methods
are able to better take into account the cluster structure of the data with
respect to the standard fuzzy c-means, with non-adaptive distances.
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
Authors: Enrico Ampellio, Luca Vassio
Date: 2014-02-24 00:00:00

In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.
Methods for Sparse and Low-Rank Recovery under Simplex Constraints
Authors: Ping Li, Syama Sundar Rangapuram, Martin Slawski
Date: 2016-05-02 00:00:00
Tasks: compressed sensing, Density Estimation, Portfolio Optimization, Quantum State Tomography

The de-facto standard approach of promoting sparsity by means of
$\ell_1$-regularization becomes ineffective in the presence of simplex
constraints, i.e.,~the target is known to have non-negative entries summing up
to a given constant. The situation is analogous for the use of nuclear norm
regularization for low-rank recovery of Hermitian positive semidefinite
matrices with given trace. In the present paper, we discuss several strategies
to deal with this situation, from simple to more complex. As a starting point,
we consider empirical risk minimization (ERM). It follows from existing theory
that ERM enjoys better theoretical properties w.r.t.~prediction and
$\ell_2$-estimation error than $\ell_1$-regularization. In light of this, we
argue that ERM combined with a subsequent sparsification step like thresholding
is superior to the heuristic of using $\ell_1$-regularization after dropping
the sum constraint and subsequent normalization.
  At the next level, we show that any sparsity-promoting regularizer under
simplex constraints cannot be convex. A novel sparsity-promoting regularization
scheme based on the inverse or negative of the squared $\ell_2$-norm is
proposed, which avoids shortcomings of various alternative methods from the
literature. Our approach naturally extends to Hermitian positive semidefinite
matrices with given trace. Numerical studies concerning compressed sensing,
sparse mixture density estimation, portfolio optimization and quantum state
tomography are used to illustrate the key points of the paper.
Fast Single Image Super-Resolution
Authors: Ningning Zhao, Qi Wei, Adrian Basarab, Nicolas Dobigeon, Denis Kouame, Jean-Yves Tourneret
Date: 2015-10-01 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

This paper addresses the problem of single image super-resolution (SR), which
consists of recovering a high resolution image from its blurred, decimated and
noisy version. The existing algorithms for single image SR use different
strategies to handle the decimation and blurring operators. In addition to the
traditional first-order gradient methods, recent techniques investigate
splitting-based methods dividing the SR problem into up-sampling and
deconvolution steps that can be easily solved. Instead of following this
splitting strategy, we propose to deal with the decimation and blurring
operators simultaneously by taking advantage of their particular properties in
the frequency domain, leading to a new fast SR approach. Specifically, an
analytical solution can be obtained and implemented efficiently for the
Gaussian prior or any other regularization that can be formulated into an
$\ell_2$-regularized quadratic model, i.e., an $\ell_2$-$\ell_2$ optimization
problem. Furthermore, the flexibility of the proposed SR scheme is shown
through the use of various priors/regularizations, ranging from generic image
priors to learning-based approaches. In the case of non-Gaussian priors, we
show how the analytical solution derived from the Gaussian case can be embedded
intotraditional splitting frameworks, allowing the computation cost of existing
algorithms to be decreased significantly. Simulation results conducted on
several images with different priors illustrate the effectiveness of our fast
SR approach compared with the existing techniques.
Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry
Authors: Yuchao Dai, Hongdong Li, Laurent Kneip
Date: 2016-05-02 00:00:00
Proceeding: CVPR 2016 6

The vast majority of modern consumer-grade cameras employ a rolling shutter
mechanism. In dynamic geometric computer vision applications such as visual
SLAM, the so-called rolling shutter effect therefore needs to be properly taken
into account. A dedicated relative pose solver appears to be the first problem
to solve, as it is of eminent importance to bootstrap any derivation of
multi-view geometry. However, despite its significance, it has received
inadequate attention to date.
  This paper presents a detailed investigation of the geometry of the rolling
shutter relative pose problem. We introduce the rolling shutter essential
matrix, and establish its link to existing models such as the push-broom
cameras, summarized in a clean hierarchy of multi-perspective cameras. The
generalization of well-established concepts from epipolar geometry is completed
by a definition of the Sampson distance in the rolling shutter case. The work
is concluded with a careful investigation of the introduced epipolar geometry
for rolling shutter cameras on several dedicated benchmarks.