pwc-archive/papers-with-abstracts huggingface dataset:
Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm
Authors: Vinay Chakravarthi Gogineni, Mrityunjoy Chakraborty
Date: 2015-07-29 00:00:00
Tasks: Diversity

Distributed adaptive networks achieve better estimation performance by
exploiting temporal and as well spatial diversity while consuming few
resources. Recent works have studied the single task distributed estimation
problem, in which the nodes estimate a single optimum parameter vector
collaboratively. However, there are many important applications where the
multiple vectors have to estimated simultaneously, in a collaborative manner.
This paper presents multi-task diffusion strategies based on the Affine
Projection Algorithm (APA), usage of APA makes the algorithm robust against the
correlated input. The performance analysis of the proposed multi-task diffusion
APA algorithm is studied in mean and mean square sense. And also a modified
multi-task diffusion strategy is proposed that improves the performance in
terms of convergence rate and steady state EMSE as well. Simulations are
conducted to verify the analytical results.
Disk storage management for LHCb based on Data Popularity estimator
Authors: Mikhail Hushchyn, Philippe Charpentier, Andrey Ustyuzhanin
Date: 2015-10-01 00:00:00
Tasks: Management, Time Series, Time Series Analysis

This paper presents an algorithm providing recommendations for optimizing the
LHCb data storage. The LHCb data storage system is a hybrid system. All
datasets are kept as archives on magnetic tapes. The most popular datasets are
kept on disks. The algorithm takes the dataset usage history and metadata
(size, type, configuration etc.) to generate a recommendation report. This
article presents how we use machine learning algorithms to predict future data
popularity. Using these predictions it is possible to estimate which datasets
should be removed from disk. We use regression algorithms and time series
analysis to find the optimal number of replicas for datasets that are kept on
disk. Based on the data popularity and the number of replicas optimization, the
algorithm minimizes a loss function to find the optimal data distribution. The
loss function represents all requirements for data distribution in the data
storage system. We demonstrate how our algorithm helps to save disk space and
to reduce waiting times for jobs using this data.
Multi-objective Differential Evolution with Helper Functions for Constrained Optimization
Authors: Tao Xu, Jun He
Date: 2015-09-30 00:00:00
Tasks: Evolutionary Algorithms

Solving constrained optimization problems by multi-objective evolutionary
algorithms has scored tremendous achievements in the last decade. Standard
multi-objective schemes usually aim at minimizing the objective function and
also the degree of constraint violation simultaneously. This paper proposes a
new multi-objective method for solving constrained optimization problems. The
new method keeps two standard objectives: the original objective function and
the sum of degrees of constraint violation. But besides them, four more
objectives are added. One is based on the feasible rule. The other three come
from the penalty functions. This paper conducts an initial experimental study
on thirteen benchmark functions. A simplified version of CMODE is applied to
solving multi-objective optimization problems. Our initial experimental results
confirm our expectation that adding more helper functions could be useful. The
performance of SMODE with more helper functions (four or six) is better than
that with only two helper functions.
Amodal Completion and Size Constancy in Natural Scenes
Authors: Abhishek Kar, Shubham Tulsiani, João Carreira, Jitendra Malik
Date: 2015-09-27 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, object-detection, Object Detection, Object Recognition, Scene Recognition

We consider the problem of enriching current object detection systems with
veridical object sizes and relative depth estimates from a single image. There
are several technical challenges to this, such as occlusions, lack of
calibration data and the scale ambiguity between object size and distance.
These have not been addressed in full generality in previous work. Here we
propose to tackle these issues by building upon advances in object recognition
and using recently created large-scale datasets. We first introduce the task of
amodal bounding box completion, which aims to infer the the full extent of the
object instances in the image. We then propose a probabilistic framework for
learning category-specific object size distributions from available annotations
and leverage these in conjunction with amodal completion to infer veridical
sizes in novel images. Finally, we introduce a focal length prediction approach
that exploits scene recognition to overcome inherent scaling ambiguities and we
demonstrate qualitative results on challenging real-world scenes.
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
Authors: Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis
Date: 2015-02-13 00:00:00
Tasks: Denoising, Speech Denoising, Speech Separation

Monaural source separation is important for many real world applications. It
is challenging because, with only a single channel of information available,
without any constraints, an infinite number of solutions are possible. In this
paper, we explore joint optimization of masking functions and deep recurrent
neural networks for monaural source separation tasks, including monaural speech
separation, monaural singing voice separation, and speech denoising. The joint
optimization of the deep recurrent neural networks with an extra masking layer
enforces a reconstruction constraint. Moreover, we explore a discriminative
criterion for training neural networks to further enhance the separation
performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT
datasets for speech separation, singing voice separation, and speech denoising
tasks, respectively. Our approaches achieve 2.30--4.98 dB SDR gain compared to
NMF models in the speech separation task, 2.30--2.48 dB GNSDR gain and
4.32--5.42 dB GSIR gain compared to existing models in the singing voice
separation task, and outperform NMF and DNN baselines in the speech denoising
task.
Supporting Regularized Logistic Regression Privately and Efficiently
Authors: Wenfa Li, Hongzhe Liu, Peng Yang, Wei Xie
Date: 2015-10-01 00:00:00
Tasks: BIG-bench Machine Learning, Epidemiology, regression

As one of the most popular statistical and machine learning models, logistic
regression with regularization has found wide adoption in biomedicine, social
sciences, information technology, and so on. These domains often involve data
of human subjects that are contingent upon strict privacy regulations.
Increasing concerns over data privacy make it more and more difficult to
coordinate and conduct large-scale collaborative studies, which typically rely
on cross-institution data sharing and joint analysis. Our work here focuses on
safeguarding regularized logistic regression, a widely-used machine learning
model in various disciplines while at the same time has not been investigated
from a data security and privacy perspective. We consider a common use scenario
of multi-institution collaborative studies, such as in the form of research
consortia or networks as widely seen in genetics, epidemiology, social
sciences, etc. To make our privacy-enhancing solution practical, we demonstrate
a non-conventional and computationally efficient method leveraging distributing
computing and strong cryptography to provide comprehensive protection over
individual-level and summary data. Extensive empirical evaluation on several
studies validated the privacy guarantees, efficiency and scalability of our
proposal. We also discuss the practical implications of our solution for
large-scale studies and applications from various disciplines, including
genetic and biomedical studies, smart grid, network analysis, etc.
Clamping Improves TRW and Mean Field Approximations
Authors: Adrian Weller, Justin Domke
Date: 2015-10-01 00:00:00

We examine the effect of clamping variables for approximate inference in
undirected graphical models with pairwise relationships and discrete variables.
For any number of variable labels, we demonstrate that clamping and summing
approximate sub-partition functions can lead only to a decrease in the
partition function estimate for TRW, and an increase for the naive mean field
method, in each case guaranteeing an improvement in the approximation and
bound. We next focus on binary variables, add the Bethe approximation to
consideration and examine ways to choose good variables to clamp, introducing
new methods. We show the importance of identifying highly frustrated cycles,
and of checking the singleton entropy of a variable. We explore the value of
our methods by empirical analysis and draw lessons to guide practitioners.
Describing Videos by Exploiting Temporal Structure
Authors: Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville
Date: 2015-02-27 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Image Description, Temporal Action Localization, Video Description

Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural language description. In this context, we propose an
approach that successfully takes into account both the local and global
temporal structure of videos to produce descriptions. First, our approach
incorporates a spatial temporal 3-D convolutional neural network (3-D CNN)
representation of the short temporal dynamics. The 3-D CNN representation is
trained on video action recognition tasks, so as to produce a representation
that is tuned to human motion and behavior. Second we propose a temporal
attention mechanism that allows to go beyond local temporal modeling and learns
to automatically select the most relevant temporal segments given the
text-generating RNN. Our approach exceeds the current state-of-art for both
BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on
a new, larger and more challenging dataset of paired video and natural language
descriptions.
Hierarchy of Scales in Language Dynamics
Authors: Richard A. Blythe
Date: 2015-05-01 00:00:00

Methods and insights from statistical physics are finding an increasing
variety of applications where one seeks to understand the emergent properties
of a complex interacting system. One such area concerns the dynamics of
language at a variety of levels of description, from the behaviour of
individual agents learning simple artificial languages from each other, up to
changes in the structure of languages shared by large groups of speakers over
historical timescales. In this Colloquium, we survey a hierarchy of scales at
which language and linguistic behaviour can be described, along with the main
progress in understanding that has been made at each of them---much of which
has come from the statistical physics community. We argue that future
developments may arise by linking the different levels of the hierarchy
together in a more coherent fashion, in particular where this allows more
effective use of rich empirical data sets.
General Dynamic Scene Reconstruction from Multiple View Video
Authors: Armin Mustafa, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton
Date: 2015-09-30 00:00:00
Proceeding: ICCV 2015 12
Tasks: Scene Segmentation, Segmentation

This paper introduces a general approach to dynamic scene reconstruction from
multiple moving cameras without prior knowledge or limiting constraints on the
scene structure, appearance, or illumination. Existing techniques for dynamic
scene reconstruction from multiple wide-baseline camera views primarily focus
on accurate reconstruction in controlled environments, where the cameras are
fixed and calibrated and background is known. These approaches are not robust
for general dynamic scenes captured with sparse moving cameras. Previous
approaches for outdoor dynamic scene reconstruction assume prior knowledge of
the static background appearance and structure. The primary contributions of
this paper are twofold: an automatic method for initial coarse dynamic scene
segmentation and reconstruction without prior knowledge of background
appearance or structure; and a general robust approach for joint segmentation
refinement and dense reconstruction of dynamic scenes from multiple
wide-baseline static or moving cameras. Evaluation is performed on a variety of
indoor and outdoor scenes with cluttered backgrounds and multiple dynamic
non-rigid objects such as people. Comparison with state-of-the-art approaches
demonstrates improved accuracy in both multiple view segmentation and dense
reconstruction. The proposed approach also eliminates the requirement for prior
knowledge of scene structure and appearance.
Extended Formulations for Online Linear Bandit Optimization
Authors: Shaona Ghosh, Adam Prugel-Bennett
Date: 2013-11-20 00:00:00
Tasks: Efficient Exploration

On-line linear optimization on combinatorial action sets (d-dimensional
actions) with bandit feedback, is known to have complexity in the order of the
dimension of the problem. The exponential weighted strategy achieves the best
known regret bound that is of the order of $d^{2}\sqrt{n}$ (where $d$ is the
dimension of the problem, $n$ is the time horizon). However, such strategies
are provably suboptimal or computationally inefficient. The complexity is
attributed to the combinatorial structure of the action set and the dearth of
efficient exploration strategies of the set. Mirror descent with entropic
regularization function comes close to solving this problem by enforcing a
meticulous projection of weights with an inherent boundary condition. Entropic
regularization in mirror descent is the only known way of achieving a
logarithmic dependence on the dimension. Here, we argue otherwise and recover
the original intuition of exponential weighting by borrowing a technique from
discrete optimization and approximation algorithms called `extended
formulation'. Such formulations appeal to the underlying geometry of the set
with a guaranteed logarithmic dependence on the dimension underpinned by an
information theoretic entropic analysis.
A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation
Authors: Yuan Zhou, Anand Rangarajan, Paul Gader
Date: 2015-09-30 00:00:00
Tasks: Hyperspectral Unmixing

The normal compositional model (NCM) has been extensively used in
hyperspectral unmixing. However, most of the previous research has focused on
estimation of endmembers and/or their variability. Also, little work has
employed spatial information in NCM. In this paper, we show that NCM can be
used for calculating the uncertainty of the estimated endmembers with spatial
priors incorporated for better unmixing. This results in a spatial
compositional model (SCM) which features (i) spatial priors that force
neighboring abundances to be similar based on their pixel similarity and (ii) a
posterior that is obtained from a likelihood model which does not assume pixel
independence. The resulting algorithm turns out to be easy to implement and
efficient to run. We compared SCM with current state-of-the-art algorithms on
synthetic and real images. The results show that SCM can in the main provide
more accurate endmembers and abundances. Moreover, the estimated uncertainty
can serve as a prediction of endmember error under certain conditions.
Fault Tolerance in Distributed Neural Computing
Authors: Anton Kulakov, Mark Zwolinski, Jeff Reeve
Date: 2015-09-30 00:00:00
Tasks: Management

With the increasing complexity of computing systems, complete hardware
reliability can no longer be guaranteed. We need, however, to ensure overall
system reliability. One of the most important features of artificial neural
networks is their intrinsic fault-tolerance. The aim of this work is to
investigate whether such networks have features that can be applied to wider
computational systems. This paper presents an analysis, in both the learning
and operational phases, of a distributed feed-forward neural network with
decentralised event-driven time management, which is insensitive to
intermittent faults caused by unreliable communication or faulty hardware
components. The learning rules used in the model are local in space and time,
which allows efficient scalable distributed implementation. We investigate the
overhead caused by injected faults and analyse the sensitivity to limited
failures in the computational hardware in different areas of the network.
Deep Haar Scattering Networks
Authors: Xiuyuan Cheng, Xu Chen, Stephane Mallat
Date: 2015-09-30 00:00:00
Tasks: Classification, General Classification

An orthogonal Haar scattering transform is a deep network, computed with a
hierarchy of additions, subtractions and absolute values, over pairs of
coefficients. It provides a simple mathematical model for unsupervised deep
network learning. It implements non-linear contractions, which are optimized
for classification, with an unsupervised pair matching algorithm, of polynomial
complexity. A structured Haar scattering over graph data computes permutation
invariant representations of groups of connected points in the graph. If the
graph connectivity is unknown, unsupervised Haar pair learning can provide a
consistent estimation of connected dyadic groups of points. Classification
results are given on image data bases, defined on regular grids or graphs, with
a connectivity which may be known or unknown.
Towards Trainable Media: Using Waves for Neural Network-Style Training
Authors: Michiel Hermans, Thomas Van Vaerenbergh
Date: 2015-09-30 00:00:00

In this paper we study the concept of using the interaction between waves and
a trainable medium in order to construct a matrix-vector multiplier. In
particular we study such a device in the context of the backpropagation
algorithm, which is commonly used for training neural networks. Here, the
weights of the connections between neurons are trained by multiplying a
`forward' signal with a backwards propagating `error' signal. We show that this
concept can be extended to trainable media, where the gradient for the local
wave number is given by multiplying signal waves and error waves. We provide a
numerical example of such a system with waves traveling freely in a trainable
medium, and we discuss a potential way to build such a device in an integrated
photonics chip.
Supporting interoperability of collaborative networks through engineering of a service-based Mediation Information System (MISE 2.0)
Authors: Frederick Benaben, Wenxin Mu, Nicolas Boissel-Dallier, Anne-Marie Barthe-Delanoë, Sarah Zribi, Herve Pingaud
Date: 2015-09-30 00:00:00
Tasks: Management

The Mediation Information System Engineering project is currently finishing
its second iteration (MISE 2.0). The main objective of this scientific project
is to provide any emerging collaborative situation with methods and tools to
deploy a Mediation Information System (MIS). MISE 2.0 aims at defining and
designing a service-based platform, dedicated to initiating and supporting the
interoperability of collaborative situations among potential partners. This
MISE 2.0 platform implements a model-driven engineering approach to the design
of a service-oriented MIS dedicated to supporting the collaborative situation.
This approach is structured in three layers, each providing their own key
innovative points: (i) the gathering of individual and collaborative knowledge
to provide appropriate collaborative business behaviour (key point: knowledge
management, including semantics, exploitation and capitalization), (ii)
deployment of a mediation information system able to computerize the previously
deduced collaborative processes (key point: the automatic generation of
collaborative workflows, including connection with existing devices or
services) (iii) the management of the agility of the obtained collaborative
network of organizations (key point: supervision of collaborative situations
and relevant exploitation of the gathered data). MISE covers business issues
(through BPM), technical issues (through an SOA) and agility issues of
collaborative situations (through EDA).
Knowledge-based system for collaborative process specification
Authors: Frederick Benaben, Vatcharaphun Rajsiri, Jean-Pierre Lorré, Hervé Pingaud
Date: 2015-09-30 00:00:00

This paper presents an ontology-based approach for the design of a
collaborative business process model (CBP). This CBP is considered as a
specification of needs in order to build a collaboration information system
(CIS) for a network of organisations. The study is a part of a model driven
engineering approach of the CIS in a specific enterprise interoperability
framework that will be summarised. An adaptation of the Business Process
Modeling Notation (BPMN) is used to represent the CBP model. We develop a
knowledge-based system (KbS) which is composed of three main parts: knowledge
gathering, knowledge representation and reasoning, and collaborative business
process modelling. The first part starts from a high abstraction level where
knowledge from business partners is captured. A collaboration ontology is
defined in order to provide a structure to store and use the knowledge
captured. In parallel, we try to reuse generic existing knowledge about
business processes from the MIT Process Handbook repository. This results in a
collaboration process ontology that is also described. A set of rules is
defined in order to extract knowledge about fragments of the CBP model from the
two previous ontologies. These fragments are finally assembled in the third
part of the KbS. A prototype of the KbS has been developed in order to
implement and support this approach. The prototype is a computer-aided design
tool of the CBP. In this paper, we will present the theoretical aspects of each
part of this KbS as well as the tools that we developed and used in order to
support its functionalities.
Learning From Missing Data Using Selection Bias in Movie Recommendation
Authors: Claire Vernade, Olivier Cappé
Date: 2015-09-30 00:00:00
Tasks: Collaborative Filtering, Movie Recommendation, Selection bias

Recommending items to users is a challenging task due to the large amount of
missing information. In many cases, the data solely consist of ratings or tags
voluntarily contributed by each user on a very limited subset of the available
items, so that most of the data of potential interest is actually missing.
Current approaches to recommendation usually assume that the unobserved data is
missing at random. In this contribution, we provide statistical evidence that
existing movie recommendation datasets reveal a significant positive
association between the rating of items and the propensity to select these
items. We propose a computationally efficient variational approach that makes
it possible to exploit this selection bias so as to improve the estimation of
ratings from small populations of users. Results obtained with this approach
applied to neighborhood-based collaborative filtering illustrate its potential
for improving the reliability of the recommendation.