pwc-archive/papers-with-abstracts huggingface dataset:
LAF-Fabric: a data analysis tool for Linguistic Annotation Framework with an application to the Hebrew Bible
Authors: Dirk Roorda, Gino Kalkman, Martijn Naaijer, Andreas van Cranenburgh
Date: 2014-10-01 00:00:00

The Linguistic Annotation Framework (LAF) provides a general, extensible
stand-off markup system for corpora. This paper discusses LAF-Fabric, a new
tool to analyse LAF resources in general with an extension to process the
Hebrew Bible in particular. We first walk through the history of the Hebrew
Bible as text database in decennium-wide steps. Then we describe how LAF-Fabric
may serve as an analysis tool for this corpus. Finally, we describe three
analytic projects/workflows that benefit from the new LAF representation:
  1) the study of linguistic variation: extract cooccurrence data of common
nouns between the books of the Bible (Martijn Naaijer); 2) the study of the
grammar of Hebrew poetry in the Psalms: extract clause typology (Gino Kalkman);
3) construction of a parser of classical Hebrew by Data Oriented Parsing:
generate tree structures from the database (Andreas van Cranenburgh).
Pattern Encoding on the Poincare Sphere
Authors: Aleksandra Pizurica
Date: 2014-10-01 00:00:00
Tasks: Clustering

This paper presents a convenient graphical tool for encoding visual patterns
(such as image patches and image atoms) as point constellations in a space
spanned by perceptual features and with a clear geometrical interpretation.
General theory and a practical pattern encoding scheme are presented, inspired
by encoding polarization states of a light wave on the Poincare sphere. This
new pattern encoding scheme can be useful for many applications in image
processing and computer vision. Here, three possible applications are
illustrated, in clustering perceptually similar patterns, visualizing
properties of learned dictionaries of image atoms and generating new
dictionaries of image atoms from spherical codes.
Quantum POMDPs
Authors: Jennifer Barry, Daniel T. Barry, Scott Aaronson
Date: 2014-06-11 00:00:00

We present quantum observable Markov decision processes (QOMDPs), the quantum
analogues of partially observable Markov decision processes (POMDPs). In a
QOMDP, an agent's state is represented as a quantum state and the agent can
choose a superoperator to apply. This is similar to the POMDP belief state,
which is a probability distribution over world states and evolves via a
stochastic matrix. We show that the existence of a policy of at least a certain
value has the same complexity for QOMDPs and POMDPs in the polynomial and
infinite horizon cases. However, we also prove that the existence of a policy
that can reach a goal state is decidable for goal POMDPs and undecidable for
goal QOMDPs.
Reservoir Computing using Cellular Automata
Authors: Ozgur Yilmaz
Date: 2014-10-01 00:00:00

We introduce a novel framework of reservoir computing. Cellular automaton is
used as the reservoir of dynamical systems. Input is randomly projected onto
the initial conditions of automaton cells and nonlinear computation is
performed on the input via application of a rule in the automaton for a period
of time. The evolution of the automaton creates a space-time volume of the
automaton state space, and it is used as the reservoir. The proposed framework
is capable of long short-term memory and it requires orders of magnitude less
computation compared to Echo State Networks. Also, for additive cellular
automaton rules, reservoir features can be combined using Boolean operations,
which provides a direct way for concept building and symbolic processing, and
it is much more efficient compared to state-of-the-art approaches.
Deep Tempering
Authors: Guillaume Desjardins, Heng Luo, Aaron Courville, Yoshua Bengio
Date: 2014-10-01 00:00:00

Restricted Boltzmann Machines (RBMs) are one of the fundamental building
blocks of deep learning. Approximate maximum likelihood training of RBMs
typically necessitates sampling from these models. In many training scenarios,
computationally efficient Gibbs sampling procedures are crippled by poor
mixing. In this work we propose a novel method of sampling from Boltzmann
machines that demonstrates a computationally efficient way to promote mixing.
Our approach leverages an under-appreciated property of deep generative models
such as the Deep Belief Network (DBN), where Gibbs sampling from deeper levels
of the latent variable hierarchy results in dramatically increased ergodicity.
Our approach is thus to train an auxiliary latent hierarchical model, based on
the DBN. When used in conjunction with parallel-tempering, the method is
asymptotically guaranteed to simulate samples from the target RBM. Experimental
results confirm the effectiveness of this sampling strategy in the context of
RBM training.
Riemannian Multi-Manifold Modeling
Authors: Xu Wang, Konstantinos Slavakis, Gilad Lerman
Date: 2014-10-01 00:00:00
Tasks: Clustering

This paper advocates a novel framework for segmenting a dataset in a
Riemannian manifold $M$ into clusters lying around low-dimensional submanifolds
of $M$. Important examples of $M$, for which the proposed clustering algorithm
is computationally efficient, are the sphere, the set of positive definite
matrices, and the Grassmannian. The clustering problem with these examples of
$M$ is already useful for numerous application domains such as action
identification in video sequences, dynamic texture clustering, brain fiber
segmentation in medical imaging, and clustering of deformed images. The
proposed clustering algorithm constructs a data-affinity matrix by thoroughly
exploiting the intrinsic geometry and then applies spectral clustering. The
intrinsic local geometry is encoded by local sparse coding and more importantly
by directional information of local tangent spaces and geodesics. Theoretical
guarantees are established for a simplified variant of the algorithm even when
the clusters intersect. To avoid complication, these guarantees assume that the
underlying submanifolds are geodesic. Extensive validation on synthetic and
real data demonstrates the resiliency of the proposed method against deviations
from the theoretical model as well as its superior performance over
state-of-the-art techniques.
Integrating active sensing into reactive synthesis with temporal logic constraints under partial observations
Authors: Jie Fu, Ufuk Topcu
Date: 2014-10-01 00:00:00

We introduce the notion of online reactive planning with sensing actions for
systems with temporal logic constraints in partially observable and dynamic
environments. With incomplete information on the dynamic environment, reactive
controller synthesis amounts to solving a two-player game with partial
observations, which has impractically computational complexity. To alleviate
the high computational burden, online replanning via sensing actions avoids
solving the strategy in the reactive system under partial observations.
Instead, we only solve for a strategy that ensures a given temporal logic
specification can be satisfied had the system have complete observations of its
environment. Such a strategy is then transformed into one which makes control
decisions based on the observed sequence of states (of the interacting system
and its environment). When the system encounters a belief---a set including all
possible hypotheses the system has for the current state---for which the
observation-based strategy is undefined, a sequence of sensing actions are
triggered, chosen by an active sensing strategy, to reduce the uncertainty in
the system's belief. We show that by alternating between the observation-based
strategy and the active sensing strategy, under a mild technical assumption of
the set of sensors in the system, the given temporal logic specification can be
satisfied with probability 1.
Unsupervised Bump Hunting Using Principal Components
Authors: Daniel A Díaz-Pachón, Jean-Eudes Dazard, J. Sunil Rao
Date: 2014-09-30 00:00:00
Tasks: Dimensionality Reduction

Principal Components Analysis is a widely used technique for dimension
reduction and characterization of variability in multivariate populations. Our
interest lies in studying when and why the rotation to principal components can
be used effectively within a response-predictor set relationship in the context
of mode hunting. Specifically focusing on the Patient Rule Induction Method
(PRIM), we first develop a fast version of this algorithm (fastPRIM) under
normality which facilitates the theoretical studies to follow. Using basic
geometrical arguments, we then demonstrate how the PC rotation of the predictor
space alone can in fact generate improved mode estimators. Simulation results
are used to illustrate our findings.
A Study of Image Analysis with Tangent Distance
Authors: Elif Vural, Pascal Frossard
Date: 2014-01-11 00:00:00
Tasks: image-classification, Image Classification, Image Registration

The computation of the geometric transformation between a reference and a
target image, known as registration or alignment, corresponds to the projection
of the target image onto the transformation manifold of the reference image
(the set of images generated by its geometric transformations). It, however,
often takes a nontrivial form such that the exact computation of projections on
the manifold is difficult. The tangent distance method is an effective
algorithm to solve this problem by exploiting a linear approximation of the
manifold. As theoretical studies about the tangent distance algorithm have been
largely overlooked, we present in this work a detailed performance analysis of
this useful algorithm, which can eventually help its implementation. We
consider a popular image registration setting using a multiscale pyramid of
lowpass filtered versions of the (possibly noisy) reference and target images,
which is particularly useful for recovering large transformations. We first
show that the alignment error has a nonmonotonic variation with the filter
size, due to the opposing effects of filtering on both manifold nonlinearity
and image noise. We then study the convergence of the multiscale tangent
distance method to the optimal solution. We finally examine the performance of
the tangent distance method in image classification applications. Our
theoretical findings are confirmed by experiments on image transformation
models involving translations, rotations and scalings. Our study is the first
detailed study of the tangent distance algorithm that leads to a better
understanding of its efficacy and to the proper selection of its design
parameters.
Distributed Detection : Finite-time Analysis and Impact of Network Topology
Authors: Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie
Date: 2014-09-30 00:00:00

This paper addresses the problem of distributed detection in multi-agent
networks. Agents receive private signals about an unknown state of the world.
The underlying state is globally identifiable, yet informative signals may be
dispersed throughout the network. Using an optimization-based framework, we
develop an iterative local strategy for updating individual beliefs. In
contrast to the existing literature which focuses on asymptotic learning, we
provide a finite-time analysis. Furthermore, we introduce a Kullback-Leibler
cost to compare the efficiency of the algorithm to its centralized counterpart.
Our bounds on the cost are expressed in terms of network size, spectral gap,
centrality of each agent and relative entropy of agents' signal structures. A
key observation is that distributing more informative signals to central agents
results in a faster learning rate. Furthermore, optimizing the weights, we can
speed up learning by improving the spectral gap. We also quantify the effect of
link failures on learning speed in symmetric networks. We finally provide
numerical simulations which verify our theoretical results.
Data Imputation through the Identification of Local Anomalies
Authors: Huseyin Ozkan, Ozgun S. Pelvan, Suleyman S. Kozat
Date: 2014-09-30 00:00:00
Tasks: Imputation

We introduce a comprehensive and statistical framework in a model free
setting for a complete treatment of localized data corruptions due to severe
noise sources, e.g., an occluder in the case of a visual recording. Within this
framework, we propose i) a novel algorithm to efficiently separate, i.e.,
detect and localize, possible corruptions from a given suspicious data instance
and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As
a generalization to Euclidean distance, we also propose a novel distance
measure, which is based on the ranked deviations among the data attributes and
empirically shown to be superior in separating the corruptions. Our algorithm
first splits the suspicious instance into parts through a binary partitioning
tree in the space of data attributes and iteratively tests those parts to
detect local anomalies using the nominal statistics extracted from an
uncorrupted (clean) reference data set. Once each part is labeled as anomalous
vs normal, the corresponding binary patterns over this tree that characterize
corruptions are identified and the affected attributes are imputed. Under a
certain conditional independency structure assumed for the binary patterns, we
analytically show that the false alarm rate of the introduced algorithm in
detecting the corruptions is independent of the data and can be directly set
without any parameter tuning. The proposed framework is tested over several
well-known machine learning data sets with synthetically generated corruptions;
and experimentally shown to produce remarkable improvements in terms of
classification purposes with strong corruption separation capabilities. Our
experiments also indicate that the proposed algorithms outperform the typical
approaches and are robust to varying training phase conditions.
Efficient multivariate sequence classification
Authors: Pavel P. Kuksa
Date: 2014-09-29 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Music Classification, Quantization, Text Categorization, Time Series Analysis

Kernel-based approaches for sequence classification have been successfully
applied to a variety of domains, including the text categorization, image
classification, speech analysis, biological sequence analysis, time series and
music classification, where they show some of the most accurate results.
  Typical kernel functions for sequences in these domains (e.g., bag-of-words,
mismatch, or subsequence kernels) are restricted to {\em discrete univariate}
(i.e. one-dimensional) string data, such as sequences of words in the text
analysis, codeword sequences in the image analysis, or nucleotide or amino acid
sequences in the DNA and protein sequence analysis. However, original sequence
data are often of real-valued multivariate nature, i.e. are not univariate and
discrete as required by typical $k$-mer based sequence kernel functions.
  In this work, we consider the problem of the {\em multivariate} sequence
classification such as classification of multivariate music sequences, or
multidimensional protein sequence representations. To this end, we extend {\em
univariate} kernel functions typically used in sequence analysis and propose
efficient {\em multivariate} similarity kernel method (MVDFQ-SK) based on (1) a
direct feature quantization (DFQ) of each sequence dimension in the original
{\em real-valued} multivariate sequences and (2) applying novel multivariate
discrete kernel measures on these multivariate discrete DFQ sequence
representations to more accurately capture similarity relationships among
sequences and improve classification performance.
  Experiments using the proposed MVDFQ-SK kernel method show excellent
classification performance on three challenging music classification tasks as
well as protein sequence classification with significant 25-40% improvements
over univariate kernel methods and existing state-of-the-art sequence
classification methods.
A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis
Authors: Prasanna Kumar Muthukumar, Alan W. black
Date: 2014-09-30 00:00:00
Tasks: Denoising, Speech Synthesis

Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral
coefficients as the vocal tract parameterization of the speech signal. Mel
Cepstral coefficients were never intended to work in a parametric speech
synthesis framework, but as yet, there has been little success in creating a
better parameterization that is more suited to synthesis. In this paper, we use
deep learning algorithms to investigate a data-driven parameterization
technique that is designed for the specific requirements of synthesis. We
create an invertible, low-dimensional, noise-robust encoding of the Mel Log
Spectrum by training a tapered Stacked Denoising Autoencoder (SDA). This SDA is
then unwrapped and used as the initialization for a Multi-Layer Perceptron
(MLP). The MLP is fine-tuned by training it to reconstruct the input at the
output layer. This MLP is then split down the middle to form encoding and
decoding networks. These networks produce a parameterization of the Mel Log
Spectrum that is intended to better fulfill the requirements of synthesis.
Results are reported for experiments conducted using this resulting
parameterization with the ClusterGen speech synthesizer.
Predicting missing links via correlation between nodes
Authors: Hao Liao, An Zeng, Yi-Cheng Zhang
Date: 2014-09-30 00:00:00
Tasks: Link Prediction, Prediction

As a fundamental problem in many different fields, link prediction aims to
estimate the likelihood of an existing link between two nodes based on the
observed information. Since this problem is related to many applications
ranging from uncovering missing data to predicting the evolution of networks,
link prediction has been intensively investigated recently and many methods
have been proposed so far. The essential challenge of link prediction is to
estimate the similarity between nodes. Most of the existing methods are based
on the common neighbor index and its variants. In this paper, we propose to
calculate the similarity between nodes by the correlation coefficient. This
method is found to be very effective when applied to calculate similarity based
on high order paths. We finally fuse the correlation-based method with the
resource allocation method, and find that the combined method can substantially
outperform the existing methods, especially in sparse networks.
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Authors: Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana
Date: 2014-09-30 00:00:00
Tasks: Authorship Attribution, Information Retrieval, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.
Interference Effects in Quantum Belief Networks
Authors: Catarina Moreira, Andreas Wichert
Date: 2014-09-30 00:00:00
Tasks: Decision Making

Probabilistic graphical models such as Bayesian Networks are one of the most
powerful structures known by the Computer Science community for deriving
probabilistic inferences. However, modern cognitive psychology has revealed
that human decisions could not follow the rules of classical probability
theory, because humans cannot process large amounts of data in order to make
judgements. Consequently, the inferences performed are based on limited data
coupled with several heuristics, leading to violations of the law of total
probability. This means that probabilistic graphical models based on classical
probability theory are too limited to fully simulate and explain various
aspects of human decision making.
  Quantum probability theory was developed in order to accommodate the
paradoxical findings that the classical theory could not explain. Recent
findings in cognitive psychology revealed that quantum probability can fully
describe human decisions in an elegant framework. Their findings suggest that,
before taking a decision, human thoughts are seen as superposed waves that can
interfere with each other, influencing the final decision.
  In this work, we propose a new Bayesian Network based on the psychological
findings of cognitive scientists. We made experiments with two very well known
Bayesian Networks from the literature. The results obtained revealed that the
quantum like Bayesian Network can affect drastically the probabilistic
inferences, specially when the levels of uncertainty of the network are very
high (no pieces of evidence observed). When the levels of uncertainty are very
low, then the proposed quantum like network collapses to its classical
counterpart.
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback
Authors: Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Shie Mannor, Yishay Mansour, Ohad Shamir
Date: 2014-09-30 00:00:00
Tasks: Multi-Armed Bandits

We present and study a partial-information model of online learning, where a
decision maker repeatedly chooses from a finite set of actions, and observes
some subset of the associated losses. This naturally models several situations
where the losses of different actions are related, and knowing the loss of one
action provides information on the loss of other actions. Moreover, it
generalizes and interpolates between the well studied full-information setting
(where all losses are revealed) and the bandit setting (where only the loss of
the action chosen by the player is revealed). We provide several algorithms
addressing different variants of our setting, and provide tight regret bounds
depending on combinatorial properties of the information feedback structure.
Variational inference of latent state sequences using Recurrent Networks
Authors: Justin Bayer, Christian Osendorfer
Date: 2014-06-06 00:00:00
Tasks: Denoising, Imputation, Time Series, Time Series Analysis, Variational Inference

Recent advances in the estimation of deep directed graphical models and
recurrent networks let us contribute to the removal of a blind spot in the area
of probabilistc modelling of time series. The proposed methods i) can infer
distributed latent state-space trajectories with nonlinear transitions, ii)
scale to large data sets thanks to the use of a stochastic objective and fast,
approximate inference, iii) enable the design of rich emission models which iv)
will naturally lead to structured outputs. Two different paths of introducing
latent state sequences are pursued, leading to the variational recurrent auto
encoder (VRAE) and the variational one step predictor (VOSP). The use of
independent Wiener processes as priors on the latent state sequence is a viable
compromise between efficient computation of the Kullback-Leibler divergence
from the variational approximation of the posterior and maintaining a
reasonable belief in the dynamics. We verify our methods empirically, obtaining
results close or superior to the state of the art. We also show qualitative
results for denoising and missing value imputation.