pwc-archive/papers-with-abstracts huggingface dataset:
Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval
Authors: Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang
Date: 2017-01-18 00:00:00
Tasks: Collaborative Filtering, Retrieval

Given a query photo issued by a user (q-user), the landmark retrieval is to
return a set of photos with their landmarks similar to those of the query,
while the existing studies on the landmark retrieval focus on exploiting
geometries of landmarks for similarity matches between candidate photos and a
query photo. We observe that the same landmarks provided by different users
over social media community may convey different geometry information depending
on the viewpoints and/or angles, and may subsequently yield very different
results. In fact, dealing with the landmarks with \illshapes caused by the
photography of q-users is often nontrivial and has seldom been studied. In this
paper we propose a novel framework, namely multi-query expansions, to retrieve
semantically robust landmarks by two steps. Firstly, we identify the top-$k$
photos regarding the latent topics of a query landmark to construct multi-query
set so as to remedy its possible \illshape. For this purpose, we significantly
extend the techniques of Latent Dirichlet Allocation. Then, motivated by the
typical \emph{collaborative filtering} methods, we propose to learn a
\emph{collaborative} deep networks based semantically, nonlinear and high-level
features over the latent factor for landmark photo as the training set, which
is formed by matrix factorization over \emph{collaborative} user-photo matrix
regarding the multi-query set. The learned deep network is further applied to
generate the features for all the other photos, meanwhile resulting into a
compact multi-query set within such space. Extensive experiments are conducted
on real-world social media data with both landmark photos together with their
user information to show the superior performance over the existing methods.
Stochastic Online Shortest Path Routing: The Value of Feedback
Authors: M. Sadegh Talebi, Zhenhua Zou, Richard Combes, Alexandre Proutiere, Mikael Johansson
Date: 2013-09-27 00:00:00

This paper studies online shortest path routing over multi-hop networks. Link
costs or delays are time-varying and modeled by independent and identically
distributed random processes, whose parameters are initially unknown. The
parameters, and hence the optimal path, can only be estimated by routing
packets through the network and observing the realized delays. Our aim is to
find a routing policy that minimizes the regret (the cumulative difference of
expected delay) between the path chosen by the policy and the unknown optimal
path. We formulate the problem as a combinatorial bandit optimization problem
and consider several scenarios that differ in where routing decisions are made
and in the information available when making the decisions. For each scenario,
we derive a tight asymptotic lower bound on the regret that has to be satisfied
by any online routing policy. These bounds help us to understand the
performance improvements we can expect when (i) taking routing decisions at
each hop rather than at the source only, and (ii) observing per-link delays
rather than end-to-end path delays. In particular, we show that (i) is of no
use while (ii) can have a spectacular impact. Three algorithms, with a
trade-off between computational complexity and performance, are proposed. The
regret upper bounds of these algorithms improve over those of the existing
algorithms, and they significantly outperform state-of-the-art algorithms in
numerical experiments.
Measurement Bounds for Sparse Signal Reconstruction with Multiple Side Information
Authors: Huynh Van Luong, Jurgen Seiler, Andre Kaup, Soren Forchhammer, Nikos Deligiannis
Date: 2016-05-10 00:00:00
Tasks: compressed sensing, Object Recognition

In the context of compressed sensing (CS), this paper considers the problem
of reconstructing sparse signals with the aid of other given correlated sources
as multiple side information. To address this problem, we theoretically study a
generic \textcolor{black}{weighted $n$-$\ell_{1}$ minimization} framework and
propose a reconstruction algorithm that leverages multiple side information
signals (RAMSI). The proposed RAMSI algorithm computes adaptively optimal
weights among the side information signals at every reconstruction iteration.
In addition, we establish theoretical bounds on the number of measurements that
are required to successfully reconstruct the sparse source by using
\textcolor{black}{weighted $n$-$\ell_{1}$ minimization}. The analysis of the
established bounds reveal that \textcolor{black}{weighted $n$-$\ell_{1}$
minimization} can achieve sharper bounds and significant performance
improvements compared to classical CS. We evaluate experimentally the proposed
RAMSI algorithm and the established bounds using synthetic sparse signals as
well as correlated feature histograms, extracted from a multiview image
database for object recognition. The obtained results show clearly that the
proposed algorithm outperforms state-of-the-art
algorithms---\textcolor{black}{including classical CS, $\ell_1\text{-}\ell_1$
minimization, Modified-CS, regularized Modified-CS, and weighted $\ell_1$
minimization}---in terms of both the theoretical bounds and the practical
performance.
A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams
Authors: Manuele Leonelli, Eva Riccomagno, Jim Q. Smith
Date: 2016-07-28 00:00:00

Influence diagrams provide a compact graphical representation of decision
problems. Several algorithms for the quick computation of their associated
expected utilities are available in the literature. However, often they rely on
a full quantification of both probabilistic uncertainties and utility values.
For problems where all random variables and decision spaces are finite and
discrete, here we develop a symbolic way to calculate the expected utilities of
influence diagrams that does not require a full numerical representation.
Within this approach expected utilities correspond to families of polynomials.
After characterizing their polynomial structure, we develop an efficient
symbolic algorithm for the propagation of expected utilities through the
diagram and provide an implementation of this algorithm using a computer
algebra system. We then characterize many of the standard manipulations of
influence diagrams as transformations of polynomials. We also generalize the
decision analytic framework of these diagrams by defining asymmetries as
operations over the expected utility polynomials.
Multilayer Perceptron Algebra
Authors: Zhao Peng
Date: 2017-01-18 00:00:00

Artificial Neural Networks(ANN) has been phenomenally successful on various
pattern recognition tasks. However, the design of neural networks rely heavily
on the experience and intuitions of individual developers. In this article, the
author introduces a mathematical structure called MLP algebra on the set of all
Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding
principle to build MLPs accommodating to the particular data sets, and to build
complex MLPs from simpler ones.
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe
Authors: Volodymyr Turchenko, Eric Chalmers, Artur Luczak
Date: 2017-01-18 00:00:00
Tasks: Clustering, Decoder, Dimensionality Reduction

This paper presents the development of several models of a deep convolutional
auto-encoder in the Caffe deep learning framework and their experimental
evaluation on the example of MNIST dataset. We have created five models of a
convolutional auto-encoder which differ architecturally by the presence or
absence of pooling and unpooling layers in the auto-encoder's encoder and
decoder parts. Our results show that the developed models provide very good
results in dimensionality reduction and unsupervised clustering tasks, and
small classification errors when we used the learned internal code as an input
of a supervised linear classifier and multi-layer perceptron. The best results
were provided by a model where the encoder part contains convolutional and
pooling layers, followed by an analogous decoder part with deconvolution and
unpooling layers without the use of switch variables in the decoder part. The
paper also discusses practical details of the creation of a deep convolutional
auto-encoder in the very popular Caffe deep learning framework. We believe that
our approach and results presented in this paper could help other researchers
to build efficient deep neural network architectures in the future.
Learning Bound for Parameter Transfer Learning
Authors: Wataru Kumagai
Date: 2016-10-27 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Transfer Learning

We consider a transfer-learning problem by using the parameter transfer
approach, where a suitable parameter of feature mapping is learned through one
task and applied to another objective task. Then, we introduce the notion of
the local stability and parameter transfer learnability of parametric feature
mapping,and thereby derive a learning bound for parameter transfer algorithms.
As an application of parameter transfer learning, we discuss the performance of
sparse coding in self-taught learning. Although self-taught learning algorithms
with plentiful unlabeled data often show excellent empirical performance, their
theoretical analysis has not been studied. In this paper, we also provide the
first theoretical learning bound for self-taught learning.
Triplet Probabilistic Embedding for Face Verification and Clustering
Authors: Swami Sankaranarayanan, Azadeh Alavi, Carlos Castillo, Rama Chellappa
Date: 2016-04-19 00:00:00
Tasks: Clustering, Face Verification, Triplet

Despite significant progress made over the past twenty five years,
unconstrained face verification remains a challenging problem. This paper
proposes an approach that couples a deep CNN-based approach with a
low-dimensional discriminative embedding learned using triplet probability
constraints to solve the unconstrained face verification problem. Aside from
yielding performance improvements, this embedding provides significant
advantages in terms of memory and for post-processing operations like subject
specific clustering. Experiments on the challenging IJB-A dataset show that the
proposed algorithm performs comparably or better than the state of the art
methods in verification and identification metrics, while requiring much less
training data and training time. The superior performance of the proposed
method on the CFP dataset shows that the representation learned by our deep CNN
is robust to extreme pose variation. Furthermore, we demonstrate the robustness
of the deep features to challenges including age, pose, blur and clutter by
performing simple clustering experiments on both IJB-A and LFW datasets.
Bringing Impressionism to Life with Neural Style Transfer in Come Swim
Authors: Bhautik Joshi, Kristen Stewart, David Shapiro
Date: 2017-01-18 00:00:00
Tasks: Style Transfer

Neural Style Transfer is a striking, recently-developed technique that uses
neural networks to artistically redraw an image in the style of a source style
image. This paper explores the use of this technique in a production setting,
applying Neural Style Transfer to redraw key scenes in 'Come Swim' in the style
of the impressionistic painting that inspired the film. We document how the
technique can be driven within the framework of an iterative creative process
to achieve a desired look, and propose a mapping of the broad parameter space
to a key set of creative controls. We hope that this mapping can provide
insights into priorities for future research.
Action Recognition: From Static Datasets to Moving Robots
Authors: Fahimeh Rezazadegan, Sareh Shirazi, Ben Upcroft, Michael Milford
Date: 2017-01-18 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Deep learning models have achieved state-of-the- art performance in
recognizing human activities, but often rely on utilizing background cues
present in typical computer vision datasets that predominantly have a
stationary camera. If these models are to be employed by autonomous robots in
real world environments, they must be adapted to perform independently of
background cues and camera motion effects. To address these challenges, we
propose a new method that firstly generates generic action region proposals
with good potential to locate one human action in unconstrained videos
regardless of camera motion and then uses action proposals to extract and
classify effective shape and motion features by a ConvNet framework. In a range
of experiments, we demonstrate that by actively proposing action regions during
both training and testing, state-of-the-art or better performance is achieved
on benchmarks. We show the outperformance of our approach compared to the
state-of-the-art in two new datasets; one emphasizes on irrelevant background,
the other highlights the camera motion. We also validate our action recognition
method in an abnormal behavior detection scenario to improve workplace safety.
The results verify a higher success rate for our method due to the ability of
our system to recognize human actions regardless of environment and camera
motion.
Compression of Deep Neural Networks for Image Instance Retrieval
Authors: Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso Poggio
Date: 2017-01-18 00:00:00
Tasks: Image Instance Retrieval, Model Compression, Quantization, Retrieval

Image instance retrieval is the problem of retrieving images from a database
which contain the same object. Convolutional Neural Network (CNN) based
descriptors are becoming the dominant approach for generating {\it global image
descriptors} for the instance retrieval problem. One major drawback of
CNN-based {\it global descriptors} is that uncompressed deep neural network
models require hundreds of megabytes of storage making them inconvenient to
deploy in mobile applications or in custom hardware. In this work, we study the
problem of neural network model compression focusing on the image instance
retrieval task. We study quantization, coding, pruning and weight sharing
techniques for reducing model size for the instance retrieval problem. We
provide extensive experimental results on the trade-off between retrieval
performance and model size for different types of networks on several data sets
providing the most comprehensive study on this topic. We compress models to the
order of a few MBs: two orders of magnitude smaller than the uncompressed
models while achieving negligible loss in retrieval performance.
An MM Algorithm for Split Feasibility Problems
Authors: Jason Xu, Eric C. Chi, Meng Yang, Kenneth Lange
Date: 2016-12-16 00:00:00

The classical multi-set split feasibility problem seeks a point in the
intersection of finitely many closed convex domain constraints, whose image
under a linear mapping also lies in the intersection of finitely many closed
convex range constraints. Split feasibility generalizes important inverse
problems including convex feasibility, linear complementarity, and regression
with constraint sets. When a feasible point does not exist, solution methods
that proceed by minimizing a proximity function can be used to obtain optimal
approximate solutions to the problem. We present an extension of the proximity
function approach that generalizes the linear split feasibility problem to
allow for non-linear mappings. Our algorithm is based on the principle of
majorization-minimization, is amenable to quasi-Newton acceleration, and comes
complete with convergence guarantees under mild assumptions. Furthermore, we
show that the Euclidean norm appearing in the proximity function of the
non-linear split feasibility problem can be replaced by arbitrary Bregman
divergences. We explore several examples illustrating the merits of non-linear
formulations over the linear case, with a focus on optimization for
intensity-modulated radiation therapy.
Unknowable Manipulators: Social Network Curator Algorithms
Authors: Samuel Albanie, Hillary Shakespeare, Tom Gunter
Date: 2017-01-17 00:00:00
Tasks: Decision Making, Reinforcement Learning

For a social networking service to acquire and retain users, it must find
ways to keep them engaged. By accurately gauging their preferences, it is able
to serve them with the subset of available content that maximises revenue for
the site. Without the constraints of an appropriate regulatory framework, we
argue that a sufficiently sophisticated curator algorithm tasked with
performing this process may choose to explore curation strategies that are
detrimental to users. In particular, we suggest that such an algorithm is
capable of learning to manipulate its users, for several qualitative reasons:
1. Access to vast quantities of user data combined with ongoing breakthroughs
in the field of machine learning are leading to powerful but uninterpretable
strategies for decision making at scale. 2. The availability of an effective
feedback mechanism for assessing the short and long term user responses to
curation strategies. 3. Techniques from reinforcement learning have allowed
machines to learn automated and highly successful strategies at an abstract
level, often resulting in non-intuitive yet nonetheless highly appropriate
action selection. In this work, we consider the form that these strategies for
user manipulation might take and scrutinise the role that regulation should
play in the design of such systems.
Towards Principled Methods for Training Generative Adversarial Networks
Authors: Martin Arjovsky, Léon Bottou
Date: 2017-01-17 00:00:00

The goal of this paper is not to introduce a single algorithm or method, but
to make theoretical steps towards fully understanding the training dynamics of
generative adversarial networks. In order to substantiate our theoretical
analysis, we perform targeted experiments to verify our assumptions, illustrate
our claims, and quantify the phenomena. This paper is divided into three
sections. The first section introduces the problem at hand. The second section
is dedicated to studying and proving rigorously the problems including
instability and saturation that arize when training generative adversarial
networks. The third section examines a practical and theoretically grounded
direction towards solving these problems, while introducing new tools to study
them.
Full-reference image quality assessment-based B-mode ultrasound image similarity measure
Authors: Kele Xu, Xi Liu, Hengxing Cai, Zhifeng Gao
Date: 2017-01-10 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment

During the last decades, the number of new full-reference image quality
assessment algorithms has been increasing drastically. Yet, despite of the
remarkable progress that has been made, the medical ultrasound image similarity
measurement remains largely unsolved due to a high level of speckle noise
contamination. Potential applications of the ultrasound image similarity
measurement seem evident in several aspects. To name a few, ultrasound imaging
quality assessment, abnormal function region detection, etc. In this paper, a
comparative study was made on full-reference image quality assessment methods
for ultrasound image visual structural similarity measure. Moreover, based on
the image similarity index, a generic ultrasound motion tracking
re-initialization framework is given in this work. The experiments are
conducted on synthetic data and real-ultrasound liver data and the results
demonstrate that, with proposed similarity-based tracking re-initialization,
the mean error of landmarks tracking can be decreased from 2 mm to about 1.5 mm
in the ultrasound liver sequence.
Sampling Requirements for Stable Autoregressive Estimation
Authors: Abbas Kazemipour, Sina Miran, Piya Pal, Behtash Babadi, Min Wu
Date: 2016-05-04 00:00:00
Tasks: Model Selection

We consider the problem of estimating the parameters of a linear univariate
autoregressive model with sub-Gaussian innovations from a limited sequence of
consecutive observations. Assuming that the parameters are compressible, we
analyze the performance of the $\ell_1$-regularized least squares as well as a
greedy estimator of the parameters and characterize the sampling trade-offs
required for stable recovery in the non-asymptotic regime. In particular, we
show that for a fixed sparsity level, stable recovery of AR parameters is
possible when the number of samples scale sub-linearly with the AR order. Our
results improve over existing sampling complexity requirements in AR estimation
using the LASSO, when the sparsity level scales faster than the square root of
the model order. We further derive sufficient conditions on the sparsity level
that guarantee the minimax optimality of the $\ell_1$-regularized least squares
estimate. Applying these techniques to simulated data as well as real-world
datasets from crude oil prices and traffic speed data confirm our predicted
theoretical performance gains in terms of estimation accuracy and model
selection.
Semi-Supervised Radio Signal Identification
Authors: Timothy J. O'Shea, Nathan West, Matthew Vondal, T. Charles Clancy
Date: 2016-11-01 00:00:00
Tasks: Clustering

Radio emitter recognition in dense multi-user environments is an important
tool for optimizing spectrum utilization, identifying and minimizing
interference, and enforcing spectrum policy. Radio data is readily available
and easy to obtain from an antenna, but labeled and curated data is often
scarce making supervised learning strategies difficult and time consuming in
practice. We demonstrate that semi-supervised learning techniques can be used
to scale learning beyond supervised datasets, allowing for discerning and
recalling new radio signals by using sparse signal representations based on
both unsupervised and supervised methods for nonlinear feature learning and
clustering methods.
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
Authors: Lei Zheng, Vahid Noroozi, Philip S. Yu
Date: 2017-01-17 00:00:00
Tasks: Recommendation Systems

A large amount of information exists in reviews written by users. This source
of information has been ignored by most of the current recommender systems
while it can potentially alleviate the sparsity problem and improve the quality
of recommendations. In this paper, we present a deep model to learn item
properties and user behaviors jointly from review text. The proposed model,
named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel
neural networks coupled in the last layers. One of the networks focuses on
learning user behaviors exploiting reviews written by the user, and the other
one learns item properties from the reviews written for the item. A shared
layer is introduced on the top to couple these two networks together. The
shared layer enables latent factors learned for users and items to interact
with each other in a manner similar to factorization machine techniques.
Experimental results demonstrate that DeepCoNN significantly outperforms all
baseline recommender systems on a variety of datasets.