pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Decision Forest for Data Clustering and Density Estimation
Authors: Hayder Albehadili, Naz Islam
Date: 2015-07-15 00:00:00
Tasks: Clustering, Density Estimation

An algorithm to improve performance parameter for unsupervised decision
forest clustering and density estimation is presented. Specifically, a dual
assignment parameter is introduced as a density estimator by combining Random
Forest and Gaussian Mixture Model. The Random Forest method has been
specifically applied to construct a robust affinity graph that provides
information on the underlying structure of data objects used in clustering. The
proposed algorithm differs from the commonly used spectral clustering methods
where the computed distance metric is used to find similarities between data
points. Experiments were conducted using five datasets. A comparison with six
other state-of-the-art methods shows that our model is superior to existing
approaches. Efficiency of the proposed model is in capturing the underlying
structure for a given set of data points. The proposed method is also robust,
and can discriminate between the complex features of data points among
different clusters.
Online Transfer Learning in Reinforcement Learning Domains
Authors: Yusen Zhan, Matthew E. Taylor
Date: 2015-07-02 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

This paper proposes an online transfer framework to capture the interaction
among agents and shows that current transfer learning in reinforcement learning
is a special case of online transfer. Furthermore, this paper re-characterizes
existing agents-teaching-agents methods as online transfer and analyze one such
teaching method in three ways. First, the convergence of Q-learning and Sarsa
with tabular representation with a finite budget is proven. Second, the
convergence of Q-learning and Sarsa with linear function approximation is
established. Third, the we show the asymptotic performance cannot be hurt
through teaching. Additionally, all theoretical results are empirically
validated.
Training artificial neural networks to learn a nondeterministic game
Authors: Thomas E. Portegys
Date: 2015-07-14 00:00:00

It is well known that artificial neural networks (ANNs) can learn
deterministic automata. Learning nondeterministic automata is another matter.
This is important because much of the world is nondeterministic, taking the
form of unpredictable or probabilistic events that must be acted upon. If ANNs
are to engage such phenomena, then they must be able to learn how to deal with
nondeterminism. In this project the game of Pong poses a nondeterministic
environment. The learner is given an incomplete view of the game state and
underlying deterministic physics, resulting in a nondeterministic game. Three
models were trained and tested on the game: Mona, Elman, and Numenta's NuPIC.
Feature Normalisation for Robust Speech Recognition
Authors: D. S. Pavan Kumar
Date: 2015-07-14 00:00:00
Tasks: Robust Speech Recognition, speech-recognition, Speech Recognition

Speech recognition system performance degrades in noisy environments. If the
acoustic models are built using features of clean utterances, the features of a
noisy test utterance would be acoustically mismatched with the trained model.
This gives poor likelihoods and poor recognition accuracy. Model adaptation and
feature normalisation are two broad areas that address this problem. While the
former often gives better performance, the latter involves estimation of lesser
number of parameters, making the system feasible for practical implementations.
  This research focuses on the efficacies of various subspace, statistical and
stereo based feature normalisation techniques. A subspace projection based
method has been investigated as a standalone and adjunct technique involving
reconstruction of noisy speech features from a precomputed set of clean speech
building-blocks. The building blocks are learned using non-negative matrix
factorisation (NMF) on log-Mel filter bank coefficients, which form a basis for
the clean speech subspace. The work provides a detailed study on how the method
can be incorporated into the extraction process of Mel-frequency cepstral
coefficients. Experimental results show that the new features are robust to
noise, and achieve better results when combined with the existing techniques.
  The work also proposes a modification to the training process of SPLICE
algorithm for noise robust speech recognition. It is based on feature
correlations, and enables this stereo-based algorithm to improve the
performance in all noise conditions, especially in unseen cases. Further, the
modified framework is extended to work for non-stereo datasets where clean and
noisy training utterances, but not stereo counterparts, are required. An
MLLR-based computationally efficient run-time noise adaptation method in SPLICE
framework has been proposed.
Structure and inference in annotated networks
Authors: M. E. J. Newman, Aaron Clauset
Date: 2015-07-14 00:00:00
Tasks: Community Detection

For many networks of scientific interest we know both the connections of the
network and information about the network nodes, such as the age or gender of
individuals in a social network, geographic location of nodes in the Internet,
or cellular function of nodes in a gene regulatory network. Here we demonstrate
how this "metadata" can be used to improve our analysis and understanding of
network structure. We focus in particular on the problem of community detection
in networks and develop a mathematically principled approach that combines a
network and its metadata to detect communities more accurately than can be done
with either alone. Crucially, the method does not assume that the metadata are
correlated with the communities we are trying to find. Instead the method
learns whether a correlation exists and correctly uses or ignores the metadata
depending on whether they contain useful information. The learned correlations
are also of interest in their own right, allowing us to make predictions about
the community membership of nodes whose network connections are unknown. We
demonstrate our method on synthetic networks with known structure and on
real-world networks, large and small, drawn from social, biological, and
technological domains.
Planning as Tabled Logic Programming
Authors: Neng-Fa Zhou, Roman Bartak, Agostino Dovier
Date: 2015-07-14 00:00:00

This paper describes Picat's planner, its implementation, and planning models
for several domains used in International Planning Competition (IPC) 2014.
Picat's planner is implemented by use of tabling. During search, every state
encountered is tabled, and tabled states are used to effectively perform
resource-bounded search. In Picat, structured data can be used to avoid
enumerating all possible permutations of objects, and term sharing is used to
avoid duplication of common state data. This paper presents several modeling
techniques through the example models, ranging from designing state
representations to facilitate data sharing and symmetry breaking, encoding
actions with operations for efficient precondition checking and state updating,
to incorporating domain knowledge and heuristics. Broadly, this paper
demonstrates the effectiveness of tabled logic programming for planning, and
argues the importance of modeling despite recent significant progress in
domain-independent PDDL planners.
Compressive Imaging and Characterization of Sparse Light Deflection Maps
Authors: Prasad Sudhakar, Laurent Jacques, Xavier Dubois, Philippe Antoine, Luc Joannes
Date: 2014-06-25 00:00:00
Tasks: compressed sensing, Object

Light rays incident on a transparent object of uniform refractive index
undergo deflections, which uniquely characterize the surface geometry of the
object. Associated with each point on the surface is a deflection map (or
spectrum) which describes the pattern of deflections in various directions.
This article presents a novel method to efficiently acquire and reconstruct
sparse deflection spectra induced by smooth object surfaces. To this end, we
leverage the framework of Compressed Sensing (CS) in a particular
implementation of a schlieren deflectometer, i.e., an optical system providing
linear measurements of deflection spectra with programmable spatial light
modulation patterns. We design those modulation patterns on the principle of
spread spectrum CS for reducing the number of observations. The ability of our
device to simultaneously observe the deflection spectra on a dense
discretization of the object surface is related to a Multiple Measurement
Vector (MMV) model. This scheme allows us to estimate both the noise power and
the instrumental point spread function.
  We formulate the spectrum reconstruction task as the solving of a linear
inverse problem regularized by an analysis sparsity prior using a translation
invariant wavelet frame. Our results demonstrate the capability and advantages
of using a CS based approach for deflectometric imaging both on simulated data
and experimental deflectometric data.
  Finally, the paper presents an extension of our method showing how we can
extract the main deflection direction in each point of the object surface from
a few compressive measurements, without needing any costly reconstruction
procedures. This compressive characterization is then confirmed with
experimental results on simple plano-convex and multifocal intra-ocular lenses
studying the evolution of the main deflection as a function of the object point
location.
Rewriting recursive aggregates in answer set programming: back to monotonicity
Authors: Mario Alviano, Wolfgang Faber, Martin Gebser
Date: 2015-07-14 00:00:00
Tasks: Translation

Aggregation functions are widely used in answer set programming for
representing and reasoning on knowledge involving sets of objects collectively.
Current implementations simplify the structure of programs in order to optimize
the overall performance. In particular, aggregates are rewritten into simpler
forms known as monotone aggregates. Since the evaluation of normal programs
with monotone aggregates is in general on a lower complexity level than the
evaluation of normal programs with arbitrary aggregates, any faithful
translation function must introduce disjunction in rule heads in some cases.
However, no function of this kind is known. The paper closes this gap by
introducing a polynomial, faithful, and modular translation for rewriting
common aggregation functions into the simpler form accepted by current solvers.
A prototype system allows for experimenting with arbitrary recursive
aggregates, which are also supported in the recent version 4.5 of the grounder
\textsc{gringo}, using the methods presented in this paper.
  To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of
ICLP 2015.
Complexity and Compilation of GZ-Aggregates in Answer Set Programming
Authors: Mario Alviano, Nicola Leone
Date: 2015-07-14 00:00:00

Gelfond and Zhang recently proposed a new stable model semantics based on
Vicious Circle Principle in order to improve the interpretation of logic
programs with aggregates. The paper focuses on this proposal, and analyzes the
complexity of both coherence testing and cautious reasoning under the new
semantics. Some surprising results highlight similarities and differences
versus mainstream stable model semantics for aggregates. Moreover, the paper
reports on the design of compilation techniques for implementing the new
semantics on top of existing ASP solvers, which eventually lead to realize a
prototype system that allows for experimenting with Gelfond-Zhang's aggregates.
  To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of
ICLP 2015.
Fuzzy Answer Set Computation via Satisfiability Modulo Theories
Authors: Mario Alviano, Rafael Penaloza
Date: 2015-07-14 00:00:00

Fuzzy answer set programming (FASP) combines two declarative frameworks,
answer set programming and fuzzy logic, in order to model reasoning by default
over imprecise information. Several connectives are available to combine
different expressions; in particular the \Godel and \Luka fuzzy connectives are
usually considered, due to their properties. Although the \Godel conjunction
can be easily eliminated from rule heads, we show through complexity arguments
that such a simplification is infeasible in general for all other connectives.
%, even if bodies are restricted to \Luka or \Godel conjunctions. The paper
analyzes a translation of FASP programs into satisfiability modulo
theories~(SMT), which in general produces quantified formulas because of the
minimality of the semantics. Structural properties of many FASP programs allow
to eliminate the quantification, or to sensibly reduce the number of quantified
variables. Indeed, integrality constraints can replace recursive rules commonly
used to force Boolean interpretations, and completion subformulas can guarantee
minimality for acyclic programs with atomic heads. Moreover, head cycle free
rules can be replaced by shifted subprograms, whose structure depends on the
eliminated head connective, so that ordered completion may replace the
minimality check if also \Luka disjunction in rule bodies is acyclic. The paper
also presents and evaluates a prototype system implementing these translations.
  To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of
ICLP 2015.
Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis
Authors: Katya Scheinberg, Xiaocheng Tang
Date: 2013-11-26 00:00:00

Recently several methods were proposed for sparse optimization which make
careful use of second-order information [10, 28, 16, 3] to improve local
convergence rates. These methods construct a composite quadratic approximation
using Hessian information, optimize this approximation using a first-order
method, such as coordinate descent and employ a line search to ensure
sufficient descent. Here we propose a general framework, which includes
slightly modified versions of existing algorithms and also a new algorithm,
which uses limited memory BFGS Hessian approximations, and provide a novel
global convergence rate analysis, which covers methods that solve subproblems
via coordinate descent.
An SVM-like Approach for Expectile Regression
Authors: Muhammad Farooq, Ingo Steinwart
Date: 2015-07-14 00:00:00
Tasks: regression

Expectile regression is a nice tool for investigating conditional
distributions beyond the conditional mean. It is well-known that expectiles can
be described with the help of the asymmetric least square loss function, and
this link makes it possible to estimate expectiles in a non-parametric
framework by a support vector machine like approach. In this work we develop an
efficient sequential-minimal-optimization-based solver for the underlying
optimization problem. The behavior of the solver is investigated by conducting
various experiments and the results are compared with the recent R-package
ER-Boost.
Rich Component Analysis
Authors: Rong Ge, James Zou
Date: 2015-07-14 00:00:00

In many settings, we have multiple data sets (also called views) that capture
different and overlapping aspects of the same phenomenon. We are often
interested in finding patterns that are unique to one or to a subset of the
views. For example, we might have one set of molecular observations and one set
of physiological observations on the same group of individuals, and we want to
quantify molecular patterns that are uncorrelated with physiology. Despite
being a common problem, this is highly challenging when the correlations come
from complex distributions. In this paper, we develop the general framework of
Rich Component Analysis (RCA) to model settings where the observations from
different views are driven by different sets of latent components, and each
component can be a complex, high-dimensional distribution. We introduce
algorithms based on cumulant extraction that provably learn each of the
components without having to model the other components. We show how to
integrate RCA with stochastic gradient descent into a meta-algorithm for
learning general models, and demonstrate substantial improvement in accuracy on
several synthetic and real datasets in both supervised and unsupervised tasks.
Our method makes it possible to learn latent variable models when we don't have
samples from the true model but only samples after complex perturbations.
Ensemble of Hankel Matrices for Face Emotion Recognition
Authors: Liliana Lo Presti, Marco La Cascia
Date: 2015-07-14 00:00:00
Tasks: Emotion Classification, Emotion Recognition, General Classification, Time Series, Time Series Analysis

In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.
The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures
Authors: Benjamin Eltzner, Carina Wollnik, Carsten Gottschlich, Stephan Huckemann, Florian Rehfeldt
Date: 2014-08-18 00:00:00

A reliable extraction of filament data from microscopic images is of high
interest in the analysis of acto-myosin structures as early morphological
markers in mechanically guided differentiation of human mesenchymal stem cells
and the understanding of the underlying fiber arrangement processes. In this
paper, we propose the filament sensor (FS), a fast and robust processing
sequence which detects and records location, orientation, length and width for
each single filament of an image, and thus allows for the above described
analysis. The extraction of these features has previously not been possible
with existing methods. We evaluate the performance of the proposed FS in terms
of accuracy and speed in comparison to three existing methods with respect to
their limited output. Further, we provide a benchmark dataset of real cell
images along with filaments manually marked by a human expert as well as
simulated benchmark images. The FS clearly outperforms existing methods in
terms of computational runtime and filament extraction accuracy. The
implementation of the FS and the benchmark database are available as open
source.
Closed Curves and Elementary Visual Object Identification
Authors: Manfred Harringer
Date: 2015-07-14 00:00:00
Tasks: Object

For two closed curves on a plane (discrete version) and local criteria for
similarity of points on the curves one gets a potential, which describes the
similarity between curve points. This is the base for a global similarity
measure of closed curves (Fr\'echet distance). I use borderlines of handwritten
digits to demonstrate an area of application. I imagine, measuring the
similarity of closed curves is an essential and elementary task performed by a
visual system. This approach to similarity measures may be used by visual
systems.
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing
Authors: Jian-Feng Cai, Suhui Liu, Weiyu Xu
Date: 2015-07-14 00:00:00
Tasks: compressed sensing, Matrix Completion

This paper considers reconstructing a spectrally sparse signal from a small
number of randomly observed time-domain samples. The signal of interest is a
linear combination of complex sinusoids at $R$ distinct frequencies. The
frequencies can assume any continuous values in the normalized frequency domain
$[0,1)$. After converting the spectrally sparse signal recovery into a low rank
structured matrix completion problem, we propose an efficient feasible point
approach, named projected Wirtinger gradient descent (PWGD) algorithm, to
efficiently solve this structured matrix completion problem. We further
accelerate our proposed algorithm by a scheme inspired by FISTA. We give the
convergence analysis of our proposed algorithms. Extensive numerical
experiments are provided to illustrate the efficiency of our proposed
algorithm. Different from earlier approaches, our algorithm can solve problems
of very large dimensions very efficiently.
Towards Understanding Egyptian Arabic Dialogues
Authors: AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith
Date: 2015-07-14 00:00:00
Tasks: General Classification

Labelling of user's utterances to understanding his attends which called
Dialogue Act (DA) classification, it is considered the key player for dialogue
language understanding layer in automatic dialogue systems. In this paper, we
proposed a novel approach to user's utterances labeling for Egyptian
spontaneous dialogues and Instant Messages using Machine Learning (ML) approach
without relying on any special lexicons, cues, or rules. Due to the lack of
Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus
includes 4725 utterances for three domains, which are collected and annotated
manually from Egyptian call-centers. The system achieves F1 scores of 70. 36%
overall domains.
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
Authors: Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier
Date: 2014-12-22 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Word Embeddings

An important class of problems involves training deep neural networks with
sparse prediction targets of very high dimension D. These occur naturally in
e.g. neural language models or the learning of word-embeddings, often posed as
predicting the probability of next words among a vocabulary of size D (e.g. 200
000). Computing the equally large, but typically non-sparse D-dimensional
output vector from a last hidden layer of reasonable dimension d (e.g. 500)
incurs a prohibitive O(Dd) computational cost for each example, as does
updating the D x d output weight matrix and computing the gradient needed for
backpropagation to previous layers. While efficient handling of large sparse
network inputs is trivial, the case of large sparse targets is not, and has
thus so far been sidestepped with approximate alternatives such as hierarchical
softmax or sampling-based approximations during training. In this work we
develop an original algorithmic approach which, for a family of loss functions
that includes squared error and spherical softmax, can compute the exact loss,
gradient update for the output weights, and gradient for backpropagation, all
in O(d^2) per example instead of O(Dd), remarkably without ever computing the
D-dimensional output. The proposed algorithm yields a speedup of D/4d , i.e.
two orders of magnitude for typical sizes, for that critical part of the
computations that often dominates the training time in this kind of network
architecture.