pwc-archive/papers-with-abstracts huggingface dataset:
Study of the Influence of the Number Normalization Scheme Used in Two Chaotic Pseudo Random Number Generators Used as the Source of Randomness in Differential Evolution
Authors: Lenka Skanderova, Tomas Fabian
Date: 2014-12-01 00:00:00
Tasks: Evolutionary Algorithms

In many publications, authors showed that chaotic pseudo random number
generators (PRNGs) may improve performance of the evolutionary algorithms. In
this paper, we use two chaotic maps Gingerbread man and Tinkerbell as the
chaotic PRNGs instead of the classical PRNG in the differential evolution.
Numbers generated by this maps are normalized to the unit interval by three
different methods -- operation modulo, straightforward number normalization
where we know minimal and maximal generated number and arctangent of the two
variables $x$ and $y$, where numbers $x$ and $y$ are generated by the
Gingerbread man map and Tinkerbell map. The first goal of this paper is to show
whether the differential evolution convergence speed might be affected by the
way how we normalize number generated by the chaotic map. The second goal is to
find out the influence of the probability distribution function of the selected
chaotic PRNGs. The results mentioned below showed that the selected
normalization method may improve differential evolution convergence speed,
especially in the case of arctangent and straightforward number normalization,
where we know the minimal and maximal generated numbers.
CAM: Causal additive models, high-dimensional order search and penalized regression
Authors: Peter Bühlmann, Jonas Peters, Jan Ernest
Date: 2013-10-06 00:00:00
Tasks: Additive models, regression, Vocal Bursts Intensity Prediction

We develop estimation for potentially high-dimensional additive structural
equation models. A key component of our approach is to decouple order search
among the variables from feature or edge selection in a directed acyclic graph
encoding the causal structure. We show that the former can be done with
nonregularized (restricted) maximum likelihood estimation while the latter can
be efficiently addressed using sparse regression techniques. Thus, we
substantially simplify the problem of structure search and estimation for an
important class of causal models. We establish consistency of the (restricted)
maximum likelihood estimator for low- and high-dimensional scenarios, and we
also allow for misspecification of the error distribution. Furthermore, we
develop an efficient computational algorithm which can deal with many
variables, and the new method's accuracy and performance is illustrated on
simulated and real data.
A CSP implementation of the bigraph embedding problem
Authors: Marino Miculan, Marco Peressotti
Date: 2014-12-01 00:00:00

A crucial problem for many results and tools about bigraphs and bigraphical reactive systems is bigraph embedding. An embedding is more informative than a bigraph matching, since it keeps track of the correspondence between the various components of the redex (guest) within the agent (host). In this paper, we present an algorithm for computing embeddings based on a reduction to a constraint satisfaction problem. This algorithm, that we prove to be sound and complete, has been successfully implemented in LibBig, a library for manipulating bigraphical reactive systems. This library can be used for implementing a wide range of tools, and it can be adapted to various extensions of bigraphs.
Chases and Escapes, and Optimization Problems
Authors: Toru Ohira
Date: 2014-12-01 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

We propose a new approach for solving combinatorial optimization problem by
utilizing the mechanism of chases and escapes, which has a long history in
mathematics. In addition to the well-used steepest descent and neighboring
search, we perform a chase and escape game on the "landscape" of the cost
function. We have created a concrete algorithm for the Traveling Salesman
Problem. Our preliminary test indicates a possibility that this new fusion of
chases and escapes problem into combinatorial optimization search is fruitful.
Efficiently learning Ising models on arbitrary graphs
Authors: Guy Bresler
Date: 2014-11-22 00:00:00

We consider the problem of reconstructing the graph underlying an Ising model
from i.i.d. samples. Over the last fifteen years this problem has been of
significant interest in the statistics, machine learning, and statistical
physics communities, and much of the effort has been directed towards finding
algorithms with low computational cost for various restricted classes of
models. Nevertheless, for learning Ising models on general graphs with $p$
nodes of degree at most $d$, it is not known whether or not it is possible to
improve upon the $p^{d}$ computation needed to exhaustively search over all
possible neighborhoods for each node.
  In this paper we show that a simple greedy procedure allows to learn the
structure of an Ising model on an arbitrary bounded-degree graph in time on the
order of $p^2$. We make no assumptions on the parameters except what is
necessary for identifiability of the model, and in particular the results hold
at low-temperatures as well as for highly non-uniform models. The proof rests
on a new structural property of Ising models: we show that for any node there
exists at least one neighbor with which it has a high mutual information. This
structural property may be of independent interest.
Lifted Probabilistic Inference for Asymmetric Graphical Models
Authors: Guy Van den Broeck, Mathias Niepert
Date: 2014-12-01 00:00:00

Lifted probabilistic inference algorithms have been successfully applied to a
large number of symmetric graphical models. Unfortunately, the majority of
real-world graphical models is asymmetric. This is even the case for relational
representations when evidence is given. Therefore, more recent work in the
community moved to making the models symmetric and then applying existing
lifted inference algorithms. However, this approach has two shortcomings.
First, all existing over-symmetric approximations require a relational
representation such as Markov logic networks. Second, the induced symmetries
often change the distribution significantly, making the computed probabilities
highly biased. We present a framework for probabilistic sampling-based
inference that only uses the induced approximate symmetries to propose steps in
a Metropolis-Hastings style Markov chain. The framework, therefore, leads to
improved probability estimates while remaining unbiased. Experiments
demonstrate that the approach outperforms existing MCMC algorithms.
Seeding the Initial Population of Multi-Objective Evolutionary Algorithms: A Computational Study
Authors: Tobias Friedrich, Markus Wagner
Date: 2014-11-30 00:00:00
Tasks: Evolutionary Algorithms

Most experimental studies initialize the population of evolutionary
algorithms with random genotypes. In practice, however, optimizers are
typically seeded with good candidate solutions either previously known or
created according to some problem-specific method. This "seeding" has been
studied extensively for single-objective problems. For multi-objective
problems, however, very little literature is available on the approaches to
seeding and their individual benefits and disadvantages. In this article, we
are trying to narrow this gap via a comprehensive computational study on common
real-valued test functions. We investigate the effect of two seeding techniques
for five algorithms on 48 optimization problems with 2, 3, 4, 6, and 8
objectives. We observe that some functions (e.g., DTLZ4 and the LZ family)
benefit significantly from seeding, while others (e.g., WFG) profit less. The
advantage of seeding also depends on the examined algorithm.
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection
Authors: George Papandreou, Iasonas Kokkinos, Pierre-André Savalle
Date: 2014-11-30 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multiple Instance Learning, object-detection, Object Detection, Translation

Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling'
(MP) layers to extract deformation-invariant features, but we argue in favor of
a more refined treatment. First, we introduce epitomic convolution as a
building block alternative to the common convolution-MP cascade of DCNNs; while
having identical complexity to MP, Epitomic Convolution allows for parameter
sharing across different filters, resulting in faster convergence and better
generalization. Second, we introduce a Multiple Instance Learning approach to
explicitly accommodate global translation and scaling when training a DCNN
exclusively with class labels. For this we rely on a `patchwork' data structure
that efficiently lays out all image scales and positions as candidates to a
DCNN. Factoring global and local deformations allows a DCNN to `focus its
resources' on the treatment of non-rigid deformations and yields a substantial
classification accuracy improvement. Third, further pursuing this idea, we
develop an efficient DCNN sliding window object detector that employs explicit
search over position, scale, and aspect ratio. We provide competitive image
classification and localization results on the ImageNet dataset and object
detection results on the Pascal VOC 2007 benchmark.
A Clearer Picture of Blind Deconvolution
Authors: Daniele Perrone, Paolo Favaro
Date: 2014-11-30 00:00:00

Blind deconvolution is the problem of recovering a sharp image and a blur
kernel from a noisy blurry image. Recently, there has been a significant effort
on understanding the basic mechanisms to solve blind deconvolution. While this
effort resulted in the deployment of effective algorithms, the theoretical
findings generated contrasting views on why these approaches worked. On the one
hand, one could observe experimentally that alternating energy minimization
algorithms converge to the desired solution. On the other hand, it has been
shown that such alternating minimization algorithms should fail to converge and
one should instead use a so-called Variational Bayes approach. To clarify this
conundrum, recent work showed that a good image and blur prior is instead what
makes a blind deconvolution algorithm work. Unfortunately, this analysis did
not apply to algorithms based on total variation regularization. In this
manuscript, we provide both analysis and experiments to get a clearer picture
of blind deconvolution. Our analysis reveals the very reason why an algorithm
based on total variation works. We also introduce an implementation of this
algorithm and show that, in spite of its extreme simplicity, it is very robust
and achieves a performance comparable to the state of the art.
Simple pairs of points in digital spaces. Topology-preserving transformations of digital spaces by contracting simple pairs of points
Authors: Alexander V. Evako
Date: 2014-11-30 00:00:00

Transformations of digital spaces preserving local and global topology play
an important role in thinning, skeletonization and simplification of digital
images. In the present paper, we introduce and study contractions of simple
pair of points based on the notions of a digital contractible space and
contractible transformations of digital spaces. We show that the contraction of
a simple pair of points preserves local and global topology of a digital space.
Relying on the obtained results, we study properties if digital manifolds. In
particular, we show that a digital n-manifold can be transformed to its
compressed form with the minimal number of points by sequential contractions of
simple pairs.
  Key Words: Graph, digital space, contraction, splitting, simple pair,
homotopy, thinning
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods
Authors: Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli
Date: 2013-11-09 00:00:00
Tasks: Computational Efficiency

We present an algorithm for minimizing a sum of functions that combines the
computational efficiency of stochastic gradient descent (SGD) with the second
order curvature information leveraged by quasi-Newton methods. We unify these
disparate approaches by maintaining an independent Hessian approximation for
each contributing function in the sum. We maintain computational tractability
and limit memory requirements even for high dimensional optimization problems
by storing and manipulating these quadratic approximations in a shared, time
evolving, low dimensional subspace. Each update step requires only a single
contributing function or minibatch evaluation (as in SGD), and each step is
scaled using an approximate inverse Hessian and little to no adjustment of
hyperparameters is required (as is typical for quasi-Newton methods). This
algorithm contrasts with earlier stochastic second order techniques that treat
the Hessian of each contributing function as a noisy approximation to the full
Hessian, rather than as a target for direct estimation. We experimentally
demonstrate improved convergence on seven diverse optimization problems. The
algorithm is released as open source Python and MATLAB packages.
Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and Optimal Sampling Distributions
Authors: Alexandre Défossez, Francis Bach
Date: 2014-11-29 00:00:00

We consider the least-squares regression problem and provide a detailed
asymptotic analysis of the performance of averaged constant-step-size
stochastic gradient descent (a.k.a. least-mean-squares). In the strongly-convex
case, we provide an asymptotic expansion up to explicit exponentially decaying
terms. Our analysis leads to new insights into stochastic approximation
algorithms: (a) it gives a tighter bound on the allowed step-size; (b) the
generalization error may be divided into a variance term which is decaying as
O(1/n), independently of the step-size $\gamma$, and a bias term that decays as
O(1/$\gamma$ 2 n 2); (c) when allowing non-uniform sampling, the choice of a
good sampling density depends on whether the variance or bias terms dominate.
In particular, when the variance term dominates, optimal sampling densities do
not lead to much gain, while when the bias term dominates, we can choose larger
step-sizes that leads to significant improvements.
Latent semantics of action verbs reflect phonetic parameters of intensity and emotional content
Authors: Michael Kai Petersen
Date: 2014-05-06 00:00:00
Tasks: Clustering

Conjuring up our thoughts, language reflects statistical patterns of word
co-occurrences which in turn come to describe how we perceive the world.
Whether counting how frequently nouns and verbs combine in Google search
queries, or extracting eigenvectors from term document matrices made up of
Wikipedia lines and Shakespeare plots, the resulting latent semantics capture
not only the associative links which form concepts, but also spatial dimensions
embedded within the surface structure of language. As both the shape and
movements of objects have been found to be associated with phonetic contrasts
already in toddlers, this study explores whether articulatory and acoustic
parameters may likewise differentiate the latent semantics of action verbs.
Selecting 3 x 20 emotion, face, and hand related verbs known to activate
premotor areas in the brain, their mutual cosine similarities were computed
using latent semantic analysis LSA, and the resulting adjacency matrices were
compared based on two different large scale text corpora; HAWIK and TASA.
Applying hierarchical clustering to identify common structures across the two
text corpora, the verbs largely divide into combined mouth and hand movements
versus emotional expressions. Transforming the verbs into their constituent
phonemes, the clustered small and large size movements appear differentiated by
front versus back vowels corresponding to increasing levels of arousal. Whereas
the clustered emotional verbs seem characterized by sequences of close versus
open jaw produced phonemes, generating up- or downwards shifts in formant
frequencies that may influence their perceived valence. Suggesting, that the
latent semantics of action verbs reflect parameters of intensity and emotional
polarity that appear correlated with the articulatory contrasts and acoustic
characteristics of phonemes
Color image quality assessment measure using multivariate generalized Gaussian distribution
Authors: Mounir Omari, Abdelkaher Ait Abdelouahad, Mohammed El Hassouni, Hocine Cherifi
Date: 2014-11-29 00:00:00
Tasks: Image Quality Assessment

This paper deals with color image quality assessment in the reduced-reference
framework based on natural scenes statistics. In this context, we propose to
model the statistics of the steerable pyramid coefficients by a Multivariate
Generalized Gaussian distribution (MGGD). This model allows taking into account
the high correlation between the components of the RGB color space. For each
selected scale and orientation, we extract a parameter matrix from the three
color components subbands. In order to quantify the visual degradation, we use
a closed-form of Kullback-Leibler Divergence (KLD) between two MGGDs. Using
"TID 2008" benchmark, the proposed measure has been compared with the most
influential methods according to the FRTV1 VQEG framework. Results demonstrates
its effectiveness for a great variety of distortion type. Among other benefits
this measure uses only very little information about the original image.
Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images
Authors: Stefan Mathe, Cristian Sminchisescu
Date: 2014-11-29 00:00:00
Tasks: Action Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Weakly supervised segmentation

State-of-the-art visual recognition and detection systems increasingly rely
on large amounts of training data and complex classifiers. Therefore it becomes
increasingly expensive both to manually annotate datasets and to keep running
times at levels acceptable for practical applications. In this paper, we
propose two solutions to address these issues. First, we introduce a weakly
supervised, segmentation-based approach to learn accurate detectors and image
classifiers from weak supervisory signals that provide only approximate
constraints on target localization. We illustrate our system on the problem of
action detection in static images (Pascal VOC Actions 2012), using human visual
search patterns as our training signal. Second, inspired from the
saccade-and-fixate operating principle of the human visual system, we use
reinforcement learning techniques to train efficient search models for
detection. Our sequential method is weakly supervised and general (it does not
require eye movements), finds optimal search strategies for any given detection
confidence function and achieves performance similar to exhaustive sliding
window search at a fraction of its computational cost.
Multi-frame denoising of high speed optical coherence tomography data using inter-frame and intra-frame priors
Authors: Liheng Bian, Jinli Suo, Feng Chen, Qionghai Dai
Date: 2013-12-06 00:00:00
Tasks: Denoising, Diagnostic

Optical coherence tomography (OCT) is an important interferometric diagnostic
technique which provides cross-sectional views of the subsurface microstructure
of biological tissues. However, the imaging quality of high-speed OCT is
limited due to the large speckle noise. To address this problem, this paper
proposes a multi-frame algorithmic method to denoise OCT volume.
Mathematically, we build an optimization model which forces the temporally
registered frames to be low rank, and the gradient in each frame to be sparse,
under logarithmic image formation and noise variance constraints. Besides, a
convex optimization algorithm based on the augmented Lagrangian method is
derived to solve the above model. The results reveal that our approach
outperforms the other methods in terms of both speckle noise suppression and
crucial detail preservation.
Pedestrian Detection aided by Deep Learning Semantic Tasks
Authors: Yonglong Tian, Ping Luo, Xiaogang Wang, Xiaoou Tang
Date: 2014-11-29 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deep Learning, Pedestrian Detection, Scene Segmentation

Deep learning methods have achieved great success in pedestrian detection,
owing to its ability to learn features from raw pixels. However, they mainly
capture middle-level representations, such as pose of pedestrian, but confuse
positive with hard negative samples, which have large ambiguity, e.g. the shape
and appearance of `tree trunk' or `wire pole' are similar to pedestrian in
certain viewpoint. This ambiguity can be distinguished by high-level
representation. To this end, this work jointly optimizes pedestrian detection
with semantic tasks, including pedestrian attributes (e.g. `carrying backpack')
and scene attributes (e.g. `road', `tree', and `horizontal'). Rather than
expensively annotating scene attributes, we transfer attributes information
from existing scene segmentation datasets to the pedestrian dataset, by
proposing a novel deep model to learn high-level features from multiple tasks
and multiple data sources. Since distinct tasks have distinct convergence rates
and data from different datasets have different distributions, a multi-task
objective function is carefully designed to coordinate tasks and reduce
discrepancies among datasets. The importance coefficients of tasks and network
parameters in this objective function can be iteratively estimated. Extensive
evaluations show that the proposed approach outperforms the state-of-the-art on
the challenging Caltech and ETH datasets, where it reduces the miss rates of
previous deep models by 17 and 5.5 percent, respectively.
3D Hand Pose Detection in Egocentric RGB-D Images
Authors: Gregory Rogez, James S. Supancic III, Maryam Khademi, Jose Maria Martinez Montiel, Deva Ramanan
Date: 2014-11-29 00:00:00
Tasks: Hand Detection, Hand Pose Estimation, Pose Estimation

We focus on the task of everyday hand pose estimation from egocentric
viewpoints. For this task, we show that depth sensors are particularly
informative for extracting near-field interactions of the camera wearer with
his/her environment. Despite the recent advances in full-body pose estimation
using Kinect-like sensors, reliable monocular hand pose estimation in RGB-D
images is still an unsolved problem. The problem is considerably exacerbated
when analyzing hands performing daily activities from a first-person viewpoint,
due to severe occlusions arising from object manipulations and a limited
field-of-view. Our system addresses these difficulties by exploiting strong
priors over viewpoint and pose in a discriminative tracking-by-detection
framework. Our priors are operationalized through a photorealistic synthetic
model of egocentric scenes, which is used to generate training data for
learning depth-based pose classifiers. We evaluate our approach on an annotated
dataset of real egocentric object manipulation scenes and compare to both
commercial and academic approaches. Our method provides state-of-the-art
performance for both hand detection and pose estimation in egocentric RGB-D
images.
A Bayesian Framework for Sparse Representation-Based 3D Human Pose Estimation
Authors: Behnam Babagholami-Mohamadabadi, Amin Jourabloo, Ali Zarghami, Shohreh Kasaei
Date: 2014-11-29 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

A Bayesian framework for 3D human pose estimation from monocular images based
on sparse representation (SR) is introduced. Our probabilistic approach aims at
simultaneously learning two overcomplete dictionaries (one for the visual input
space and the other for the pose space) with a shared sparse representation.
Existing SR-based pose estimation approaches only offer a point estimation of
the dictionary and the sparse codes. Therefore, they might be unreliable when
the number of training examples is small. Our Bayesian framework estimates a
posterior distribution for the sparse codes and the dictionaries from labeled
training data. Hence, it is robust to overfitting on small-size training data.
Experimental results on various human activities show that the proposed method
is superior to the state of-the-art pose estimation algorithms.