pwc-archive/papers-with-abstracts huggingface dataset:
Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images
Authors: Mete Ozay
Date: 2015-02-17 00:00:00
Tasks: Segmentation

A Semi-supervised Segmentation Fusion algorithm is proposed using consensus
and distributed learning. The aim of Unsupervised Segmentation Fusion (USF) is
to achieve a consensus among different segmentation outputs obtained from
different segmentation algorithms by computing an approximate solution to the
NP problem with less computational complexity. Semi-supervision is incorporated
in USF using a new algorithm called Semi-supervised Segmentation Fusion (SSSF).
In SSSF, side information about the co-occurrence of pixels in the same or
different segments is formulated as the constraints of a convex optimization
problem. The results of the experiments employed on artificial and real-world
benchmark multi-spectral and aerial images show that the proposed algorithms
perform better than the individual state-of-the art segmentation algorithms.
Fracking Deep Convolutional Image Descriptors
Authors: Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Francesc Moreno-Noguer
Date: 2014-12-19 00:00:00

In this paper we propose a novel framework for learning local image
descriptors in a discriminative manner. For this purpose we explore a siamese
architecture of Deep Convolutional Neural Networks (CNN), with a Hinge
embedding loss on the L2 distance between descriptors. Since a siamese
architecture uses pairs rather than single image patches to train, there exist
a large number of positive samples and an exponential number of negative
samples. We propose to explore this space with a stochastic sampling of the
training set, in combination with an aggressive mining strategy over both the
positive and negative samples which we denote as "fracking". We perform a
thorough evaluation of the architecture hyper-parameters, and demonstrate large
performance gains compared to both standard CNN learning strategies,
hand-crafted image descriptors like SIFT, and the state-of-the-art on learned
descriptors: up to 2.5x vs SIFT and 1.5x vs the state-of-the-art in terms of
the area under the curve (AUC) of the Precision-Recall curve.
Competing with the Empirical Risk Minimizer in a Single Pass
Authors: Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
Date: 2014-12-20 00:00:00

In many estimation problems, e.g. linear and logistic regression, we wish to
minimize an unknown objective given only unbiased samples of the objective
function. Furthermore, we aim to achieve this using as few samples as possible.
In the absence of computational constraints, the minimizer of a sample average
of observed data -- commonly referred to as either the empirical risk minimizer
(ERM) or the $M$-estimator -- is widely regarded as the estimation strategy of
choice due to its desirable statistical convergence properties. Our goal in
this work is to perform as well as the ERM, on every problem, while minimizing
the use of computational resources such as running time and space usage.
  We provide a simple streaming algorithm which, under standard regularity
assumptions on the underlying problem, enjoys the following properties:
  * The algorithm can be implemented in linear time with a single pass of the
observed data, using space linear in the size of a single sample.
  * The algorithm achieves the same statistical rate of convergence as the
empirical risk minimizer on every problem, even considering constant factors.
  * The algorithm's performance depends on the initial error at a rate that
decreases super-polynomially.
  * The algorithm is easily parallelizable.
  Moreover, we quantify the (finite-sample) rate at which the algorithm becomes
competitive with the ERM.
Constraint-based sequence mining using constraint programming
Authors: Benjamin Negrevergne, Tias Guns
Date: 2015-01-06 00:00:00

The goal of constraint-based sequence mining is to find sequences of symbols
that are included in a large number of input sequences and that satisfy some
constraints specified by the user. Many constraints have been proposed in the
literature, but a general framework is still missing. We investigate the use of
constraint programming as general framework for this task. We first identify
four categories of constraints that are applicable to sequence mining. We then
propose two constraint programming formulations. The first formulation
introduces a new global constraint called exists-embedding. This formulation is
the most efficient but does not support one type of constraint. To support such
constraints, we develop a second formulation that is more general but incurs
more overhead. Both formulations can use the projected database technique used
in specialised algorithms. Experiments demonstrate the flexibility towards
constraint-based settings and compare the approach to existing methods.
Online Pairwise Learning Algorithms with Kernels
Authors: Yiming Ying, Ding-Xuan Zhou
Date: 2015-02-25 00:00:00
Tasks: Metric Learning

Pairwise learning usually refers to a learning task which involves a loss
function depending on pairs of examples, among which most notable ones include
ranking, metric learning and AUC maximization. In this paper, we study an
online algorithm for pairwise learning with a least-square loss function in an
unconstrained setting of a reproducing kernel Hilbert space (RKHS), which we
refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to
existing works \cite{Kar,Wang} which require that the iterates are restricted
to a bounded domain or the loss function is strongly-convex, OPERA is
associated with a non-strongly convex objective function and learns the target
function in an unconstrained RKHS. Specifically, we establish a general theorem
which guarantees the almost surely convergence for the last iterate of OPERA
without any assumptions on the underlying distribution. Explicit convergence
rates are derived under the condition of polynomially decaying step sizes. We
also establish an interesting property for a family of widely-used kernels in
the setting of pairwise learning and illustrate the above convergence results
using such kernels. Our methodology mainly depends on the characterization of
RKHSs using its associated integral operators and probability inequalities for
random variables with values in a Hilbert space.
Toggling a Genetic Switch Using Reinforcement Learning
Authors: Aivar Sootla, Natalja Strelkowa, Damien Ernst, Mauricio Barahona, Guy-Bart Stan
Date: 2013-03-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we consider the problem of optimal exogenous control of gene
regulatory networks. Our approach consists in adapting an established
reinforcement learning algorithm called the fitted Q iteration. This algorithm
infers the control law directly from the measurements of the system's response
to external control inputs without the use of a mathematical model of the
system. The measurement data set can either be collected from wet-lab
experiments or artificially created by computer simulations of dynamical models
of the system. The algorithm is applicable to a wide range of biological
systems due to its ability to deal with nonlinear and stochastic system
dynamics. To illustrate the application of the algorithm to a gene regulatory
network, the regulation of the toggle switch system is considered. The control
objective of this problem is to drive the concentrations of two specific
proteins to a target region in the state space.
The VC-Dimension of Similarity Hypotheses Spaces
Authors: Mark Herbster, Paul Rubenstein, James Townsend
Date: 2015-02-25 00:00:00
Tasks: PAC learning

Given a set $X$ and a function $h:X\longrightarrow\{0,1\}$ which labels each
element of $X$ with either $0$ or $1$, we may define a function $h^{(s)}$ to
measure the similarity of pairs of points in $X$ according to $h$.
Specifically, for $h\in \{0,1\}^X$ we define $h^{(s)}\in \{0,1\}^{X\times X}$
by $h^{(s)}(w,x):= \mathbb{1}[h(w) = h(x)]$. This idea can be extended to a set
of functions, or hypothesis space $\mathcal{H} \subseteq \{0,1\}^X$ by defining
a similarity hypothesis space $\mathcal{H}^{(s)}:=\{h^{(s)}:h\in\mathcal{H}\}$.
We show that ${{vc-dimension}}(\mathcal{H}^{(s)}) \in
\Theta({{vc-dimension}}(\mathcal{H}))$.
Cross-Modality Hashing with Partial Correspondence
Authors: Yun Gu, Haoyang Xue, Jie Yang
Date: 2015-02-18 00:00:00

Learning a hashing function for cross-media search is very desirable due to
its low storage cost and fast query speed. However, the data crawled from
Internet cannot always guarantee good correspondence among different modalities
which affects the learning for hashing function. In this paper, we focus on
cross-modal hashing with partially corresponded data. The data without full
correspondence are made in use to enhance the hashing performance. The
experiments on Wiki and NUS-WIDE datasets demonstrates that the proposed method
outperforms some state-of-the-art hashing approaches with fewer correspondence
information.
A Note on the Kullback-Leibler Divergence for the von Mises-Fisher distribution
Authors: Tom Diethe
Date: 2015-02-25 00:00:00

We present a derivation of the Kullback Leibler (KL)-Divergence (also known
as Relative Entropy) for the von Mises Fisher (VMF) Distribution in
$d$-dimensions.
On aggregation for heavy-tailed classes
Authors: Shahar Mendelson
Date: 2015-02-25 00:00:00
Tasks: Learning Theory

We introduce an alternative to the notion of `fast rate' in Learning Theory,
which coincides with the optimal error rate when the given class happens to be
convex and regular in some sense. While it is well known that such a rate
cannot always be attained by a learning procedure (i.e., a procedure that
selects a function in the given class), we introduce an aggregation procedure
that attains that rate under rather minimal assumptions -- for example, that
the $L_q$ and $L_2$ norms are equivalent on the linear span of the class for
some $q>2$, and the target random variable is square-integrable.
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
Authors: Adam W. Harley, Alex Ufkes, Konstantinos G. Derpanis
Date: 2015-02-25 00:00:00
Tasks: Descriptive, document-image-classification, Document Image Classification, General Classification, image-classification, Image Classification, Retrieval

This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis.
Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey
Authors: Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Seungmin Rho, Sung Wook Baik
Date: 2015-02-25 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Visual media has always been the most enjoyed way of communication. From the
advent of television to the modern day hand held computers, we have witnessed
the exponential growth of images around us. Undoubtedly it's a fact that they
carry a lot of information in them which needs be utilized in an effective
manner. Hence intense need has been felt to efficiently index and store large
image collections for effective and on- demand retrieval. For this purpose
low-level features extracted from the image contents like color, texture and
shape has been used. Content based image retrieval systems employing these
features has proven very successful. Image retrieval has promising applications
in numerous fields and hence has motivated researchers all over the world. New
and improved ways to represent visual content are being developed each day.
Tremendous amount of research has been carried out in the last decade. In this
paper we will present a detailed overview of some of the powerful color,
texture and shape descriptors for content based image retrieval. A comparative
analysis will also be carried out for providing an insight into outstanding
challenges in this field.
Web-scale Surface and Syntactic n-gram Features for Dependency Parsing
Authors: Dominick Ng, Mohit Bansal, James R. Curran
Date: 2015-02-25 00:00:00
Tasks: Dependency Parsing

We develop novel first- and second-order features for dependency parsing
based on the Google Syntactic Ngrams corpus, a collection of subtree counts of
parsed sentences from scanned books. We also extend previous work on surface
$n$-gram features from Web1T to the Google Books corpus and from first-order to
second-order, comparing and analysing performance over newswire and web
treebanks.
  Surface and syntactic $n$-grams both produce substantial and complementary
gains in parsing accuracy across domains. Our best system combines the two
feature sets, achieving up to 0.8% absolute UAS improvements on newswire and
1.4% on web text.
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Authors: Shreyansh Daftry, Christof Hoppe, Horst Bischof
Date: 2015-02-25 00:00:00
Tasks: 3D Reconstruction

Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.
On Convolutional Approximations to Linear Dimensionality Reduction Operators for Large Scale Data Processing
Authors: Swayambhoo Jain, Jarvis Haupt
Date: 2015-02-25 00:00:00
Tasks: Dimensionality Reduction

In this paper, we examine the problem of approximating a general linear
dimensionality reduction (LDR) operator, represented as a matrix $A \in
\mathbb{R}^{m \times n}$ with $m < n$, by a partial circulant matrix with rows
related by circular shifts. Partial circulant matrices admit fast
implementations via Fourier transform methods and subsampling operations; our
investigation here is motivated by a desire to leverage these potential
computational improvements in large-scale data processing tasks. We establish a
fundamental result, that most large LDR matrices (whose row spaces are
uniformly distributed) in fact cannot be well approximated by partial circulant
matrices. Then, we propose a natural generalization of the partial circulant
approximation framework that entails approximating the range space of a given
LDR operator $A$ over a restricted domain of inputs, using a matrix formed as a
product of a partial circulant matrix having $m '> m$ rows and a $m \times k$
'post processing' matrix. We introduce a novel algorithmic technique, based on
sparse matrix factorization, for identifying the factors comprising such
approximations, and provide preliminary evidence to demonstrate the potential
of this approach.
Classification approach based on association rules mining for unbalanced data
Authors: Cheikh Ndour, Aliou Diop, Simplice Dossou-Gbété
Date: 2012-02-24 00:00:00
Tasks: Binary Classification, Classification, General Classification

This paper deals with the binary classification task when the target class
has the lower probability of occurrence. In such situation, it is not possible
to build a powerful classifier by using standard methods such as logistic
regression, classification tree, discriminant analysis, etc. To overcome this
short-coming of these methods which yield classifiers with low sensibility, we
tackled the classification problem here through an approach based on the
association rules learning. This approach has the advantage of allowing the
identification of the patterns that are well correlated with the target class.
Association rules learning is a well known method in the area of data-mining.
It is used when dealing with large database for unsupervised discovery of local
patterns that expresses hidden relationships between input variables. In
considering association rules from a supervised learning point of view, a
relevant set of weak classifiers is obtained from which one derives a
classifier that performs well.
Personalising Mobile Advertising Based on Users Installed Apps
Authors: Jenna Reps, Uwe Aickelin, Jonathan Garibaldi, Chris Damski
Date: 2015-02-24 00:00:00
Tasks: Clustering, Dimensionality Reduction

Mobile advertising is a billion pound industry that is rapidly expanding. The
success of an advert is measured based on how users interact with it. In this
paper we investigate whether the application of unsupervised learning and
association rule mining could be used to enable personalised targeting of
mobile adverts with the aim of increasing the interaction rate. Over May and
June 2014 we recorded advert interactions such as tapping the advert or
watching the whole advert video along with the set of apps a user has installed
at the time of the interaction. Based on the apps that the users have installed
we applied k-means clustering to profile the users into one of ten classes. Due
to the large number of apps considered we implemented dimension reduction to
reduced the app feature space by mapping the apps to their iTunes category and
clustered users based on the percentage of their apps that correspond to each
iTunes app category. The clustering was externally validated by investigating
differences between the way the ten profiles interact with the various adverts
genres (lifestyle, finance and entertainment adverts). In addition association
rule mining was performed to find whether the time of the day that the advert
is served and the number of apps a user has installed makes certain profiles
more likely to interact with the advert genres. The results showed there were
clear differences in the way the profiles interact with the different advert
genres and the results of this paper suggest that mobile advert targeting would
improve the frequency that users interact with an advert.
Scalable Variational Inference in Log-supermodular Models
Authors: Josip Djolonga, Andreas Krause
Date: 2015-02-23 00:00:00
Tasks: Bayesian Inference, Image Segmentation, Semantic Segmentation, Variational Inference

We consider the problem of approximate Bayesian inference in log-supermodular
models. These models encompass regular pairwise MRFs with binary variables, but
allow to capture high-order interactions, which are intractable for existing
approximate inference techniques such as belief propagation, mean field, and
variants. We show that a recently proposed variational approach to inference in
log-supermodular models -L-FIELD- reduces to the widely-studied minimum norm
problem for submodular minimization. This insight allows to leverage powerful
existing tools, and hence to solve the variational problem orders of magnitude
more efficiently than previously possible. We then provide another natural
interpretation of L-FIELD, demonstrating that it exactly minimizes a specific
type of R\'enyi divergence measure. This insight sheds light on the nature of
the variational approximations produced by L-FIELD. Furthermore, we show how to
perform parallel inference as message passing in a suitable factor graph at a
linear convergence rate, without having to sum up over all the configurations
of the factor. Finally, we apply our approach to a challenging image
segmentation task. Our experiments confirm scalability of our approach, high
quality of the marginals, and the benefit of incorporating higher-order
potentials.
Tensor SimRank for Heterogeneous Information Networks
Authors: Ben Usman, Ivan Oseledets
Date: 2015-02-24 00:00:00

We propose a generalization of SimRank similarity measure for heterogeneous
information networks. Given the information network, the intraclass similarity
score s(a, b) is high if the set of objects that are related with a and the set
of objects that are related with b are pair-wise similar according to all
imposed relations.
A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design
Authors: Ngoc Q. K. Duong, Hien-Thanh Duong
Date: 2015-02-24 00:00:00
Tasks: Audio Signal Processing, Survey

Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio signature, the second step usually requires knowledge about
database and quick-search algorithms. Though this technique offers a wide range
of real-world applications, to the best of the authors' knowledge, a
comprehensive survey of existing algorithms appeared more than eight years ago.
Thus, in this paper, we present a more up-to-date review and, for emphasizing
on the audio signal processing aspect, we focus our state-of-the-art survey on
the fingerprint design step for which various audio features and their
tractable statistical models are discussed.
New HSL Distance Based Colour Clustering Algorithm
Authors: Vasile Patrascu
Date: 2015-02-24 00:00:00
Tasks: Clustering

In this paper, we define a distance for the HSL colour system. Next, the
proposed distance is used for a fuzzy colour clustering algorithm construction.
The presented algorithm is related to the well-known fuzzy c-means algorithm.
Finally, the clustering algorithm is used as colour reduction method. The
obtained experimental results are presented to demonstrate the effectiveness of
our approach.