pwc-archive/papers-with-abstracts huggingface dataset:
Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking
Authors: Liming Zhao, Xi Li, Jun Xiao, Fei Wu, Yueting Zhuang
Date: 2014-12-04 00:00:00
Tasks: Metric Learning, Object Tracking

As an important and challenging problem in computer vision and graphics,
keypoint-based object tracking is typically formulated in a spatio-temporal
statistical learning framework. However, most existing keypoint trackers are
incapable of effectively modeling and balancing the following three aspects in
a simultaneous manner: temporal model coherence across frames, spatial model
consistency within frames, and discriminative feature construction. To address
this issue, we propose a robust keypoint tracker based on spatio-temporal
multi-task structured output optimization driven by discriminative metric
learning. Consequently, temporal model coherence is characterized by multi-task
structured keypoint model learning over several adjacent frames, while spatial
model consistency is modeled by solving a geometric verification based
structured learning problem. Discriminative feature construction is enabled by
metric learning to ensure the intra-class compactness and inter-class
separability. Finally, the above three modules are simultaneously optimized in
a joint learning scheme. Experimental results have demonstrated the
effectiveness of our tracker.
On the String Kernel Pre-Image Problem with Applications in Drug Discovery
Authors: Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand
Date: 2014-12-03 00:00:00
Tasks: Combinatorial Optimization, Drug Discovery, valid

The pre-image problem has to be solved during inference by most structured
output predictors. For string kernels, this problem corresponds to finding the
string associated to a given input. An algorithm capable of solving or finding
good approximations to this problem would have many applications in
computational biology and other fields. This work uses a recent result on
combinatorial optimization of linear predictors based on string kernels to
develop, for the pre-image, a low complexity upper bound valid for many string
kernels. This upper bound is used with success in a branch and bound searching
algorithm. Applications and results in the discovery of druggable peptides are
presented and discussed.
Detection of cheating by decimation algorithm
Authors: Shogo Yamanaka, Masayuki Ohzeki, Aurelien Decelle
Date: 2014-10-14 00:00:00
Tasks: BIG-bench Machine Learning

We expand the item response theory to study the case of "cheating students"
for a set of exams, trying to detect them by applying a greedy algorithm of
inference. This extended model is closely related to the Boltzmann machine
learning. In this paper we aim to infer the correct biases and interactions of
our model by considering a relatively small number of sets of training data.
Nevertheless, the greedy algorithm that we employed in the present study
exhibits good performance with a few number of training data. The key point is
the sparseness of the interactions in our problem in the context of the
Boltzmann machine learning: the existence of cheating students is expected to
be very rare (possibly even in real world). We compare a standard approach to
infer the sparse interactions in the Boltzmann machine learning to our greedy
algorithm and we find the latter to be superior in several aspects.
Probability Theory without Bayes' Rule
Authors: Samuel G. Rodriques
Date: 2014-11-28 00:00:00

Within the Kolmogorov theory of probability, Bayes' rule allows one to
perform statistical inference by relating conditional probabilities to
unconditional probabilities. As we show here, however, there is a continuous
set of alternative inference rules that yield the same results, and that may
have computational or practical advantages for certain problems. We formulate
generalized axioms for probability theory, according to which the reverse
conditional probability distribution P(B|A) is not specified by the forward
conditional probability distribution P(A|B) and the marginals P(A) and P(B).
Thus, in order to perform statistical inference, one must specify an additional
"inference axiom," which relates P(B|A) to P(A|B), P(A), and P(B). We show that
when Bayes' rule is chosen as the inference axiom, the axioms are equivalent to
the classical Kolmogorov axioms. We then derive consistency conditions on the
inference axiom, and thereby characterize the set of all possible rules for
inference. The set of "first-order" inference axioms, defined as the set of
axioms in which P(B|A) depends on the first power of P(A|B), is found to be a
1-simplex, with Bayes' rule at one of the extreme points. The other extreme
point, the "inversion rule," is studied in depth.
Textural Approach for Mass Abnormality Segmentation in Mammographic Images
Authors: Khamsa Djaroudib, Abdelmalik Taleb Ahmed, Abdelmadjid Zidani
Date: 2014-12-03 00:00:00
Tasks: Diagnostic, Segmentation

Mass abnormality segmentation is a vital step for the medical diagnostic
process and is attracting more and more the interest of many research groups.
Currently, most of the works achieved in this area have used the Gray Level
Co-occurrence Matrix (GLCM) as texture features with a region-based approach.
These features come in previous phase for segmentation stage or are using as
inputs to classification stage. The work discussed in this paper attempts to
experiment the GLCM method under a contour-based approach. Besides, we
experiment the proposed approach on various tissues densities to bring more
significant results. At this end, we explored some challenging breast images
from BIRADS medical Data Base. Our first experimentations showed promising
results with regard to the edges mass segmentation methods. This paper
discusses first the main works achieved in this area. Sections 2 and 3 present
materials and our methodology. The main results are showed and evaluated before
concluding our paper.
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Authors: Nikos Komodakis, Jean-Christophe Pesquet
Date: 2014-06-20 00:00:00

Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness.
Structure learning of antiferromagnetic Ising models
Authors: Guy Bresler, David Gamarnik, Devavrat Shah
Date: 2014-12-03 00:00:00
Proceeding: NeurIPS 2014 12

In this paper we investigate the computational complexity of learning the
graph structure underlying a discrete undirected graphical model from i.i.d.
samples. We first observe that the notoriously difficult problem of learning
parities with noise can be captured as a special case of learning graphical
models. This leads to an unconditional computational lower bound of $\Omega
(p^{d/2})$ for learning general graphical models on $p$ nodes of maximum degree
$d$, for the class of so-called statistical algorithms recently introduced by
Feldman et al (2013). The lower bound suggests that the $O(p^d)$ runtime
required to exhaustively search over neighborhoods cannot be significantly
improved without restricting the class of models.
  Aside from structural assumptions on the graph such as it being a tree,
hypertree, tree-like, etc., many recent papers on structure learning assume
that the model has the correlation decay property. Indeed, focusing on
ferromagnetic Ising models, Bento and Montanari (2009) showed that all known
low-complexity algorithms fail to learn simple graphs when the interaction
strength exceeds a number related to the correlation decay threshold. Our
second set of results gives a class of repelling (antiferromagnetic) models
that have the opposite behavior: very strong interaction allows efficient
learning in time $O(p^2)$. We provide an algorithm whose performance
interpolates between $O(p^2)$ and $O(p^{d+2})$ depending on the strength of the
repulsion.
Memory Bounded Deep Convolutional Networks
Authors: Maxwell D. Collins, Pushmeet Kohli
Date: 2014-12-03 00:00:00

In this work, we investigate the use of sparsity-inducing regularizers during
training of Convolution Neural Networks (CNNs). These regularizers encourage
that fewer connections in the convolution and fully connected layers take
non-zero values and in effect result in sparse connectivity between hidden
units in the deep network. This in turn reduces the memory and runtime cost
involved in deploying the learned CNNs. We show that training with such
regularization can still be performed using stochastic gradient descent
implying that it can be used easily in existing codebases. Experimental
evaluation of our approach on MNIST, CIFAR, and ImageNet datasets shows that
our regularizers can result in dramatic reductions in memory requirements. For
instance, when applied on AlexNet, our method can reduce the memory consumption
by a factor of four with minimal loss in accuracy.
A note relating ridge regression and OLS p-values to preconditioned sparse penalized regression
Authors: Karl Rohe
Date: 2014-11-26 00:00:00
Tasks: regression

When the design matrix has orthonormal columns, "soft thresholding" the
ordinary least squares (OLS) solution produces the Lasso solution [Tibshirani,
1996]. If one uses the Puffer preconditioned Lasso [Jia and Rohe, 2012], then
this result generalizes from orthonormal designs to full rank designs (Theorem
1). Theorem 2 refines the Puffer preconditioner to make the Lasso select the
same model as removing the elements of the OLS solution with the largest
p-values. Using a generalized Puffer preconditioner, Theorem 3 relates ridge
regression to the preconditioned Lasso; this result is for the high dimensional
setting, p > n. Where the standard Lasso is akin to forward selection [Efron et
al., 2004], Theorems 1, 2, and 3 suggest that the preconditioned Lasso is more
akin to backward elimination. These results hold for sparse penalties beyond
l1; for a broad class of sparse and non-convex techniques (e.g. SCAD and MC+),
the results hold for all local minima.
Nested Variational Compression in Deep Gaussian Processes
Authors: James Hensman, Neil D. Lawrence
Date: 2014-12-03 00:00:00
Tasks: Gaussian Processes, Variational Inference

Deep Gaussian processes provide a flexible approach to probabilistic
modelling of data using either supervised or unsupervised learning. For
tractable inference approximations to the marginal likelihood of the model must
be made. The original approach to approximate inference in these models used
variational compression to allow for approximate variational marginalization of
the hidden variables leading to a lower bound on the marginal likelihood of the
model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a
nested variational compression. The resulting lower bound on the likelihood can
be easily parallelized or adapted for stochastic variational inference.
Curriculum Learning of Multiple Tasks
Authors: Anastasia Pentina, Viktoriia Sharmanska, Christoph H. Lampert
Date: 2014-12-03 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multi-Task Learning

Sharing information between multiple tasks enables algorithms to achieve good
generalization performance even from small amounts of training data. However,
in a realistic scenario of multi-task learning not all tasks are equally
related to each other, hence it could be advantageous to transfer information
only between the most related tasks. In this work we propose an approach that
processes multiple tasks in a sequence with sharing between subsequent tasks
instead of solving all tasks jointly. Subsequently, we address the question of
curriculum learning of tasks, i.e. finding the best order of tasks to be
learned. Our approach is based on a generalization bound criterion for choosing
the task order that optimizes the average expected classification performance
over all tasks. Our experimental results show that learning multiple related
tasks sequentially can be more effective than learning them jointly, the order
in which tasks are being solved affects the overall performance, and that our
model is able to automatically discover the favourable order of tasks.
A perspective on the advancement of natural language processing tasks via topological analysis of complex networks
Authors: Diego R. Amancio
Date: 2014-12-03 00:00:00

Comment on "Approaching human language with complex networks" by Cong and Liu
(Physics of Life Reviews, Volume 11, Issue 4, December 2014, Pages 598-618).
Deeply learned face representations are sparse, selective, and robust
Authors: Yi Sun, Xiaogang Wang, Xiaoou Tang
Date: 2014-12-03 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Recognition

This paper designs a high-performance deep convolutional network (DeepID2+) for face recognition. It is learned with the identification-verification supervisory signal. By increasing the dimension of hidden representations and adding supervision to early convolutional layers, DeepID2+ achieves new state-of-the-art on LFW and YouTube Faces benchmarks. Through empirical studies, we have discovered three properties of its deep neural activations critical for the high performance: sparsity, selectiveness and robustness. (1) It is observed that neural activations are moderately sparse. Moderate sparsity maximizes the discriminative power of the deep net as well as the distance between images. It is surprising that DeepID2+ still can achieve high recognition accuracy even after the neural responses are binarized. (2) Its neurons in higher layers are highly selective to identities and identity-related attributes. We can identify different subsets of neurons which are either constantly excited or inhibited when different identities or attributes are present. Although DeepID2+ is not taught to distinguish attributes during training, it has implicitly learned such high-level concepts. (3) It is much more robust to occlusions, although occlusion patterns are not included in the training set.
Colorisation et texturation temps réel d'environnements urbains par système mobile avec scanner laser et caméra fish-eye
Authors: Jean-Emmanuel Deschaud, Xavier Brun, François Goulette
Date: 2014-12-03 00:00:00
Tasks: Colorization

We present here a real time mobile mapping system mounted on a vehicle. The
terrestrial acquisition system is based on a geolocation system and two
sensors, namely, a laser scanner and a camera with a fish-eye lens. We produce
3D colored points cloud and textured models of the environment. Once the system
has been calibrated, the data acquisition and processing are done "on the way".
This article mainly presents our methods of colorization of point cloud,
triangulation and texture mapping.
Simple Two-Dimensional Object Tracking based on a Graph Algorithm
Authors: Alexandra Heidsieck
Date: 2014-12-03 00:00:00
Tasks: Object Tracking, Vocal Bursts Valence Prediction

The visual observation and tracking of cells and other micrometer-sized
objects has many different biomedical applications. The automation of those
tasks based on computer methods helps in the evaluation of such measurements.
In this work, we present a general purpose algorithm that excels at evaluating
deterministic behavior of micrometer-sized objects. Our concrete application is
the tracking of fast moving objects over large distances along deterministic
trajectories in a microscopic video. Thereby, we are able to determine
characteristic properties of the objects. For this purpose, we use a set of
basic algorithms, including blob recognition, feature-based shape recognition
and a graph algorithm, and combined them in a novel way. An evaluation of the
algorithms performance shows a high accuracy in the recognition of objects as
well as of complete trajectories. Moreover, a direct comparison to a similar
algorithm shows superior recognition rates.
Mary Astell's words in A Serious Proposal to the Ladies (part I), a lexicographic inquiry with NooJ
Authors: Hélène Pignot, Odile Piton
Date: 2014-12-03 00:00:00
Tasks: Articles

In the following article we elected to study with NooJ the lexis of a 17 th
century text, Mary Astell's seminal essay, A Serious Proposal to the Ladies,
part I, published in 1694. We first focused on the semantics to see how Astell
builds her vindication of the female sex, which words she uses to sensitise
women to their alienated condition and promote their education. Then we studied
the morphology of the lexemes (which is different from contemporary English)
used by the author, thanks to the NooJ tools we have devised for this purpose.
NooJ has great functionalities for lexicographic work. Its commands and graphs
prove to be most efficient in the spotting of archaic words or variants in
spelling. Introduction In our previous articles, we have studied the
singularities of 17 th century English within the framework of a diachronic
analysis thanks to syntactical and morphological graphs and thanks to the
dictionaries we have compiled from a corpus that may be expanded overtime. Our
early work was based on a limited corpus of English travel literature to Greece
in the 17 th century. This article deals with a late seventeenth century text
written by a woman philosopher and essayist, Mary Astell (1666--1731),
considered as one of the first English feminists. Astell wrote her essay at a
time in English history when women were "the weaker vessel" and their main
business in life was to charm and please men by their looks and submissiveness.
In this essay we will see how NooJ can help us analyse Astell's rhetoric (what
point of view does she adopt, does she speak in her own name, in the name of
all women, what is her representation of men and women and their relationships
in the text, what are the goals of education?). Then we will turn our attention
to the morphology of words in the text and use NooJ commands and graphs to
carry out a lexicographic inquiry into Astell's lexemes.
Gradient Boundary Histograms for Action Recognition
Authors: Feng Shi, Robert Laganiere, Emil Petriu
Date: 2014-12-03 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper introduces a high efficient local spatiotemporal descriptor,
called gradient boundary histograms (GBH). The proposed GBH descriptor is built
on simple spatio-temporal gradients, which are fast to compute. We demonstrate
that it can better represent local structure and motion than other
gradient-based descriptors, and significantly outperforms them on large
realistic datasets. A comprehensive evaluation shows that the recognition
accuracy is preserved while the spatial resolution is greatly reduced, which
yields both high efficiency and low memory usage.
Highly comparative fetal heart rate analysis
Authors: B. D. Fulcher, A. E. Georgieva, C. W. G. Redman, Nick S. Jones
Date: 2014-12-03 00:00:00
Tasks: Time Series, Time Series Analysis

A database of fetal heart rate (FHR) time series measured from 7221 patients
during labor is analyzed with the aim of learning the types of features of
these recordings that are informative of low cord pH. Our 'highly comparative'
analysis involves extracting over 9000 time-series analysis features from each
FHR time series, including measures of autocorrelation, entropy, distribution,
and various model fits. This diverse collection of features was developed in
previous work, and is publicly available. We describe five features that most
accurately classify a balanced training set of 59 'low pH' and 59 'normal pH'
FHR recordings. We then describe five of the features with the strongest linear
correlation to cord pH across the full dataset of FHR time series. The features
identified in this work may be used as part of a system for guiding
intervention during labor in future. This work successfully demonstrates the
utility of comparing across a large, interdisciplinary literature on
time-series analysis to automatically contribute new scientific results for
specific biomedical signal processing challenges.
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning
Authors: Judy Hoffman, Deepak Pathak, Trevor Darrell, Kate Saenko
Date: 2014-12-02 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multiple Instance Learning, Representation Learning, Transfer Learning

We develop methods for detector learning which exploit joint training over
both weak and strong labels and which transfer learned perceptual
representations from strongly-labeled auxiliary tasks. Previous methods for
weak-label learning often learn detector models independently using latent
variable optimization, but fail to share deep representation knowledge across
classes and usually require strong initialization. Other previous methods
transfer deep representations from domains with strong labels to those with
only weak labels, but do not optimize over individual latent boxes, and thus
may miss specific salient structures for a particular category. We propose a
model that subsumes these previous approaches, and simultaneously trains a
representation and detectors for categories with either weak or strong labels
present. We provide a novel formulation of a joint multiple instance learning
method that includes examples from classification-style data when available,
and also performs domain transfer learning to improve the underlying detector
representation. Our model outperforms known methods on ImageNet-200 detection
with weak labels.
Spread Unary Coding
Authors: Subhash Kak
Date: 2014-12-02 00:00:00
Tasks: Form

Unary coding is useful but it is redundant in its standard form. Unary coding
can also be seen as spatial coding where the value of the number is determined
by its place in an array. Motivated by biological finding that several neurons
in the vicinity represent the same number, we propose a variant of unary
numeration in its spatial form, where each number is represented by several 1s.
We call this spread unary coding where the number of 1s used is the spread of
the code. Spread unary coding is associated with saturation of the Hamming
distance between code words.
Easy Hyperparameter Search Using Optunity
Authors: Marc Claesen, Jaak Simm, Dusan Popovic, Yves Moreau, Bart De Moor
Date: 2014-12-02 00:00:00
Tasks: BIG-bench Machine Learning, Hyperparameter Optimization

Optunity is a free software package dedicated to hyperparameter optimization.
It contains various types of solvers, ranging from undirected methods to direct
search, particle swarm and evolutionary optimization. The design focuses on
ease of use, flexibility, code clarity and interoperability with existing
software in all machine learning environments. Optunity is written in Python
and contains interfaces to environments such as R and MATLAB. Optunity uses a
BSD license and is freely available online at http://www.optunity.net.