pwc-archive/papers-with-abstracts huggingface dataset:
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
Authors: Fabio Cuzzolin, Diana Mateus, Radu Horaud
Date: 2014-05-26 00:00:00

In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model construction
Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning
Authors: Dan Stowell, Mark D. Plumbley
Date: 2014-05-26 00:00:00
Tasks: Classification, General Classification

Automatic species classification of birds from their sound is a computational
tool of increasing importance in ecology, conservation monitoring and vocal
communication studies. To make classification useful in practice, it is crucial
to improve its accuracy while ensuring that it can run at big data scales. Many
approaches use acoustic measures based on spectrogram-type data, such as the
Mel-frequency cepstral coefficient (MFCC) features which represent a
manually-designed summary of spectral information. However, recent work in
machine learning has demonstrated that features learnt automatically from data
can often outperform manually-designed feature transforms. Feature learning can
be performed at large scale and "unsupervised", meaning it requires no manual
data labelling, yet it can improve performance on "supervised" tasks such as
classification. In this work we introduce a technique for feature learning from
large volumes of bird sound recordings, inspired by techniques that have proven
useful in other domains. We experimentally compare twelve different feature
representations derived from the Mel spectrum (of which six use this
technique), using four large and diverse databases of bird vocalisations, with
a random forest classifier. We demonstrate that MFCCs are of limited power in
this context, leading to worse performance than the raw Mel spectral data.
Conversely, we demonstrate that unsupervised feature learning provides a
substantial boost over MFCCs and Mel spectra without adding computational
complexity after the model has been trained. The boost is particularly notable
for single-label classification tasks at large scale. The spectro-temporal
activations learned through our procedure resemble spectro-temporal receptive
fields calculated from avian primary auditory forebrain.
Fast and Robust Archetypal Analysis for Representation Learning
Authors: Yuansi Chen, Julien Mairal, Zaid Harchaoui
Date: 2014-05-26 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, Representation Learning

We revisit a pioneer unsupervised learning technique called archetypal
analysis, which is related to successful data analysis methods such as sparse
coding and non-negative matrix factorization. Since it was proposed, archetypal
analysis did not gain a lot of popularity even though it produces more
interpretable models than other alternatives. Because no efficient
implementation has ever been made publicly available, its application to
important scientific problems may have been severely limited. Our goal is to
bring back into favour archetypal analysis. We propose a fast optimization
scheme using an active-set strategy, and provide an efficient open-source
implementation interfaced with Matlab, R, and Python. Then, we demonstrate the
usefulness of archetypal analysis for computer vision tasks, such as codebook
learning, signal classification, and large image collection visualization.
Optimal interval clustering: Application to Bregman clustering and statistical mixture learning
Authors: Frank Nielsen, Richard Nock
Date: 2014-03-11 00:00:00
Tasks: Clustering, Model Selection

We present a generic dynamic programming method to compute the optimal
clustering of $n$ scalar elements into $k$ pairwise disjoint intervals. This
case includes 1D Euclidean $k$-means, $k$-medoids, $k$-medians, $k$-centers,
etc. We extend the method to incorporate cluster size constraints and show how
to choose the appropriate $k$ by model selection. Finally, we illustrate and
refine the method on two case studies: Bregman clustering and statistical
mixture learning maximizing the complete likelihood.
The role of dimensionality reduction in linear classification
Authors: Weiran Wang, Miguel Á. Carreira-Perpiñán
Date: 2014-05-26 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification

Dimensionality reduction (DR) is often used as a preprocessing step in
classification, but usually one first fixes the DR mapping, possibly using
label information, and then learns a classifier (a filter approach). Best
performance would be obtained by optimizing the classification error jointly
over DR mapping and classifier (a wrapper approach), but this is a difficult
nonconvex problem, particularly with nonlinear DR. Using the method of
auxiliary coordinates, we give a simple, efficient algorithm to train a
combination of nonlinear DR and a classifier, and apply it to a RBF mapping
with a linear SVM. This alternates steps where we train the RBF mapping and a
linear SVM as usual regression and classification, respectively, with a
closed-form step that coordinates both. The resulting nonlinear low-dimensional
classifier achieves classification errors competitive with the state-of-the-art
but is fast at training and testing, and allows the user to trade off runtime
for classification accuracy easily. We then study the role of nonlinear DR in
linear classification, and the interplay between the DR mapping, the number of
latent dimensions and the number of classes. When trained jointly, the DR
mapping takes an extreme role in eliminating variation: it tends to collapse
classes in latent space, erasing all manifold structure, and lay out class
centroids so they are linearly separable with maximum margin.
TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations
Authors: Naushad UzZaman, Hector Llorens, James Allen, Leon Derczynski, Marc Verhagen, James Pustejovsky
Date: 2012-06-22 00:00:00
Tasks: Relation, Relation Extraction, Temporal Relation Extraction

We describe the TempEval-3 task which is currently in preparation for the
SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on
temporal information processing. TempEval-3 follows on from previous TempEval
events, incorporating: a three-part task structure covering event, temporal
expression and temporal relation extraction; a larger dataset; and single
overall task quality scores.
Parallelizing MCMC via Weierstrass Sampler
Authors: Xiangyu Wang, David B. Dunson
Date: 2013-12-17 00:00:00
Tasks: Computational Efficiency

With the rapidly growing scales of statistical problems, subset based
communication-free parallel MCMC methods are a promising future for large scale
Bayesian analysis. In this article, we propose a new Weierstrass sampler for
parallel MCMC based on independent subsets. The new sampler approximates the
full data posterior samples via combining the posterior draws from independent
subset MCMC chains, and thus enjoys a higher computational efficiency. We show
that the approximation error for the Weierstrass sampler is bounded by some
tuning parameters and provide suggestions for choice of the values. Simulation
study shows the Weierstrass sampler is very competitive compared to other
methods for combining MCMC chains generated for subsets, including averaging
and kernel smoothing.
Sparse Estimation From Noisy Observations of an Overdetermined Linear System
Authors: Liang Dai, Kristiaan Pelckmans
Date: 2014-02-12 00:00:00

This note studies a method for the efficient estimation of a finite number of
unknown parameters from linear equations, which are perturbed by Gaussian
noise.
  In case the unknown parameters have only few nonzero entries, the proposed
estimator performs more efficiently than a traditional approach.
  The method consists of three steps:
  (1) a classical Least Squares Estimate (LSE),
  (2) the support is recovered through a Linear Programming (LP) optimization
problem which can be computed using a soft-thresholding step,
  (3) a de-biasing step using a LSE on the estimated support set.
  The main contribution of this note is a formal derivation of an associated
ORACLE property of the final estimate.
  That is, when the number of samples is large enough, the estimate is shown to
equal the LSE based on the support of the {\em true} parameters.
Learning the Irreducible Representations of Commutative Lie Groups
Authors: Taco Cohen, Max Welling
Date: 2014-02-18 00:00:00
Tasks: General Classification, Translation

We present a new probabilistic model of compact commutative Lie groups that
produces invariant-equivariant and disentangled representations of data. To
define the notion of disentangling, we borrow a fundamental principle from
physics that is used to derive the elementary particles of a system from its
symmetries. Our model employs a newfound Bayesian conjugacy relation that
enables fully tractable probabilistic inference over compact commutative Lie
groups -- a class that includes the groups that describe the rotation and
cyclic translation of images. We train the model on pairs of transformed image
patches, and show that the learned invariant representation is highly effective
for classification.
Volumetric Spanners: an Efficient Exploration Basis for Learning
Authors: Elad Hazan, Zohar Karnin, Raghu Mehka
Date: 2013-12-21 00:00:00
Tasks: BIG-bench Machine Learning, Efficient Exploration

Numerous machine learning problems require an exploration basis - a mechanism
to explore the action space. We define a novel geometric notion of exploration
basis with low variance, called volumetric spanners, and give efficient
algorithms to construct such a basis.
  We show how efficient volumetric spanners give rise to the first efficient
and optimal regret algorithm for bandit linear optimization over general convex
sets. Previously such results were known only for specific convex sets, or
under special conditions such as the existence of an efficient self-concordant
barrier for the underlying set.
HEPGAME and the Simplification of Expressions
Authors: Ben Ruijl, Jos Vermaseren, Aske Plaat, Jaap van den Herik
Date: 2014-05-25 00:00:00
Tasks: Numerical Integration

Advances in high energy physics have created the need to increase
computational capacity. Project HEPGAME was composed to address this challenge.
One of the issues is that numerical integration of expressions of current
interest have millions of terms and takes weeks to compute. We have
investigated ways to simplify these expressions, using Horner schemes and
common subexpression elimination. Our approach applies MCTS, a search procedure
that has been successful in AI. We use it to find near-optimal Horner schemes.
Although MCTS finds better solutions, this approach gives rise to two further
challenges. (1) MCTS (with UCT) introduces a constant, $C_p$ that governs the
balance between exploration and exploitation. This constant has to be tuned
manually. (2) There should be more guided exploration at the bottom of the
tree, since the current approach reduces the quality of the solution towards
the end of the expression. We investigate NMCS (Nested Monte Carlo Search) to
address both issues, but find that NMCS is computationally unfeasible for our
problem. Then, we modify the MCTS formula by introducing a dynamic
exploration-exploitation parameter $T$ that decreases linearly with the
iteration number. Consequently, we provide a performance analysis. We observe
that a variable $C_p$ solves our domain: it yields more exploration at the
bottom and as a result the tuning problem has been simplified. The region in
$C_p$ for which good values are found is increased by more than a tenfold. This
result encourages us to continue our research to solve other prominent problems
in High Energy Physics.
Active causation and the origin of meaning
Authors: J. H. van Hateren
Date: 2013-10-08 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Purpose and meaning are necessary concepts for understanding mind and
culture, but appear to be absent from the physical world and are not part of
the explanatory framework of the natural sciences. Understanding how meaning
(in the broad sense of the term) could arise from a physical world has proven
to be a tough problem. The basic scheme of Darwinian evolution produces
adaptations that only represent apparent ("as if") goals and meaning. Here I
use evolutionary models to show that a slight, evolvable extension of the basic
scheme is sufficient to produce genuine goals. The extension, targeted
modulation of mutation rate, is known to be generally present in biological
cells, and gives rise to two phenomena that are absent from the non-living
world: intrinsic meaning and the ability to initiate goal-directed chains of
causation (active causation). The extended scheme accomplishes this by
utilizing randomness modulated by a feedback loop that is itself regulated by
evolutionary pressure. The mechanism can be extended to behavioural variability
as well, and thus shows how freedom of behaviour is possible. A further
extension to communication suggests that the active exchange of intrinsic
meaning between organisms may be the origin of consciousness, which in
combination with active causation can provide a physical basis for the
phenomenon of free will.
A Novel Stochastic Decoding of LDPC Codes with Quantitative Guarantees
Authors: Nima Noorshams, Aravind Iyengar
Date: 2014-05-25 00:00:00

Low-density parity-check codes, a class of capacity-approaching linear codes,
are particularly recognized for their efficient decoding scheme. The decoding
scheme, known as the sum-product, is an iterative algorithm consisting of
passing messages between variable and check nodes of the factor graph. The
sum-product algorithm is fully parallelizable, owing to the fact that all
messages can be update concurrently. However, since it requires extensive
number of highly interconnected wires, the fully-parallel implementation of the
sum-product on chips is exceedingly challenging. Stochastic decoding
algorithms, which exchange binary messages, are of great interest for
mitigating this challenge and have been the focus of extensive research over
the past decade. They significantly reduce the required wiring and
computational complexity of the message-passing algorithm. Even though
stochastic decoders have been shown extremely effective in practice, the
theoretical aspect and understanding of such algorithms remains limited at
large. Our main objective in this paper is to address this issue. We first
propose a novel algorithm referred to as the Markov based stochastic decoding.
Then, we provide concrete quantitative guarantees on its performance for
tree-structured as well as general factor graphs. More specifically, we provide
upper-bounds on the first and second moments of the error, illustrating that
the proposed algorithm is an asymptotically consistent estimate of the
sum-product algorithm. We also validate our theoretical predictions with
experimental results, showing we achieve comparable performance to other
practical stochastic decoders.
Efficient Model Learning for Human-Robot Collaborative Tasks
Authors: Stefanos Nikolaidis, Keren Gu, Ramya Ramakrishnan, Julie Shah
Date: 2014-05-24 00:00:00
Tasks: Clustering, Reinforcement Learning

We present a framework for learning human user models from joint-action
demonstrations that enables the robot to compute a robust policy for a
collaborative task with a human. The learning takes place completely
automatically, without any human intervention. First, we describe the
clustering of demonstrated action sequences into different human types using an
unsupervised learning algorithm. These demonstrated sequences are also used by
the robot to learn a reward function that is representative for each type,
through the employment of an inverse reinforcement learning algorithm. The
learned model is then used as part of a Mixed Observability Markov Decision
Process formulation, wherein the human type is a partially observable variable.
With this framework, we can infer, either offline or online, the human type of
a new user that was not included in the training set, and can compute a policy
for the robot that will be aligned to the preference of this new user and will
be robust to deviations of the human actions from prior demonstrations. Finally
we validate the approach using data collected in human subject experiments, and
conduct proof-of-concept demonstrations in which a person performs a
collaborative task with a small industrial robot.
Four Classes of Morphogenetic Collective Systems
Authors: Hiroki Sayama
Date: 2014-05-24 00:00:00

We studied the roles of morphogenetic principles---heterogeneity of
components, dynamic differentiation/re-differentiation of components, and local
information sharing among components---in the self-organization of
morphogenetic collective systems. By incrementally introducing these principles
to collectives, we defined four distinct classes of morphogenetic collective
systems. Monte Carlo simulations were conducted using an extended version of
the Swarm Chemistry model that was equipped with dynamic
differentiation/re-differentiation and local information sharing capabilities.
Self-organization of swarms was characterized by several kinetic and
topological measurements, the latter of which were facilitated by a newly
developed network-based method. Results of simulations revealed that, while
heterogeneity of components had a strong impact on the structure and behavior
of the swarms, dynamic differentiation/re-differentiation of components and
local information sharing helped the swarms maintain spatially adjacent,
coherent organization.
Cross-Language Personal Name Mapping
Authors: Ahmed H. Yousef
Date: 2014-05-24 00:00:00

Name matching between multiple natural languages is an important step in
cross-enterprise integration applications and data mining. It is difficult to
decide whether or not two syntactic values (names) from two heterogeneous data
sources are alternative designation of the same semantic entity (person), this
process becomes more difficult with Arabic language due to several factors
including spelling and pronunciation variation, dialects and special vowel and
consonant distinction and other linguistic characteristics. This paper proposes
a new framework for name matching between the Arabic language and other
languages. The framework uses a dictionary based on a new proposed version of
the Soundex algorithm to encapsulate the recognition of special features of
Arabic names. The framework proposes a new proximity matching algorithm to suit
the high importance of order sensitivity in Arabic name matching. New
performance evaluation metrics are proposed as well. The framework is
implemented and verified empirically in several case studies demonstrating
substantial improvements compared to other well-known techniques found in
literature.
Online Learning with Predictable Sequences
Authors: Alexander Rakhlin, Karthik Sridharan
Date: 2012-08-18 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis, Time Series Prediction

We present methods for online linear optimization that take advantage of
benign (as opposed to worst-case) sequences. Specifically if the sequence
encountered by the learner is described well by a known "predictable process",
the algorithms presented enjoy tighter bounds as compared to the typical worst
case bounds. Additionally, the methods achieve the usual worst-case regret
bounds if the sequence is not benign. Our approach can be seen as a way of
adding prior knowledge about the sequence within the paradigm of online
learning. The setting is shown to encompass partial and side information.
Variance and path-length bounds can be seen as particular examples of online
learning with simple predictable sequences.
  We further extend our methods and results to include competing with a set of
possible predictable processes (models), that is "learning" the predictable
process itself concurrently with using it to obtain better regret guarantees.
We show that such model selection is possible under various assumptions on the
available feedback. Our results suggest a promising direction of further
research with potential applications to stock market and time series
prediction.
Kernel Estimation from Salient Structure for Robust Motion Deblurring
Authors: Jinshan Pan, Risheng Liu, Zhixun Su, Xianfeng GU
Date: 2012-12-05 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Denoising, Image Deblurring, Image Denoising, Image Restoration

Blind image deblurring algorithms have been improving steadily in the past
years. Most state-of-the-art algorithms, however, still cannot perform
perfectly in challenging cases, especially in large blur setting. In this
paper, we focus on how to estimate a good kernel estimate from a single blurred
image based on the image structure. We found that image details caused by
blurring could adversely affect the kernel estimation, especially when the blur
kernel is large. One effective way to eliminate these details is to apply image
denoising model based on the Total Variation (TV). First, we developed a novel
method for computing image structures based on TV model, such that the
structures undermining the kernel estimation will be removed. Second, to
mitigate the possible adverse effect of salient edges and improve the
robustness of kernel estimation, we applied a gradient selection method. Third,
we proposed a novel kernel estimation method, which is capable of preserving
the continuity and sparsity of the kernel and reducing the noises. Finally, we
developed an adaptive weighted spatial prior, for the purpose of preserving
sharp edges in latent image restoration. The effectiveness of our method is
demonstrated by experiments on various kinds of challenging examples.