pwc-archive/papers-with-abstracts huggingface dataset:
Avoiding Confusion between Predictors and Inhibitors in Value Function Approximation
Authors: Patrick C. Connor, Thomas P. Trappenberg
Date: 2013-12-19 00:00:00
Tasks: Decision Making, Reinforcement Learning, Value prediction

In reinforcement learning, the goal is to seek rewards and avoid punishments.
A simple scalar captures the value of a state or of taking an action, where
expected future rewards increase and punishments decrease this quantity.
Naturally an agent should learn to predict this quantity to take beneficial
actions, and many value function approximators exist for this purpose. In the
present work, however, we show how value function approximators can cause
confusion between predictors of an outcome of one valence (e.g., a signal of
reward) and the inhibitor of the opposite valence (e.g., a signal canceling
expectation of punishment). We show this to be a problem for both linear and
non-linear value function approximators, especially when the amount of data (or
experience) is limited. We propose and evaluate a simple resolution: to instead
predict reward and punishment values separately, and rectify and add them to
get the value needed for decision making. We evaluate several function
approximators in this slightly different value function approximation
architecture and show that this approach is able to circumvent the confusion
and thereby achieve lower value-prediction errors.
NEFI: Network Extraction From Images
Authors: Michael Dirnberger, Adrian Neumann, Tim Kehl
Date: 2015-02-18 00:00:00

Networks and network-like structures are amongst the central building blocks
of many technological and biological systems. Given a mathematical graph
representation of a network, methods from graph theory enable a precise
investigation of its properties. Software for the analysis of graphs is widely
available and has been applied to graphs describing large scale networks such
as social networks, protein-interaction networks, etc. In these applications,
graph acquisition, i.e., the extraction of a mathematical graph from a network,
is relatively simple. However, for many network-like structures, e.g. leaf
venations, slime molds and mud cracks, data collection relies on images where
graph extraction requires domain-specific solutions or even manual. Here we
introduce Network Extraction From Images, NEFI, a software tool that
automatically extracts accurate graphs from images of a wide range of networks
originating in various domains. While there is previous work on graph
extraction from images, theoretical results are fully accessible only to an
expert audience and ready-to-use implementations for non-experts are rarely
available or insufficiently documented. NEFI provides a novel platform allowing
practitioners from many disciplines to easily extract graph representations
from images by supplying flexible tools from image processing, computer vision
and graph theory bundled in a convenient package. Thus, NEFI constitutes a
scalable alternative to tedious and error-prone manual graph extraction and
special purpose tools. We anticipate NEFI to enable the collection of larger
datasets by reducing the time spent on graph extraction. The analysis of these
new datasets may open up the possibility to gain new insights into the
structure and function of various types of networks. NEFI is open source and
available http://nefi.mpi-inf.mpg.de.
F0 Modeling In Hmm-Based Speech Synthesis System Using Deep Belief Network
Authors: Sankar Mukherjee, Shyamal Kumar Das Mandal
Date: 2015-02-18 00:00:00
Tasks: Clustering, Speaker Verification, speech-recognition, Speech Recognition, Speech Synthesis

In recent years multilayer perceptrons (MLPs) with many hid- den layers Deep
Neural Network (DNN) has performed sur- prisingly well in many speech tasks,
i.e. speech recognition, speaker verification, speech synthesis etc. Although
in the context of F0 modeling these techniques has not been ex- ploited
properly. In this paper, Deep Belief Network (DBN), a class of DNN family has
been employed and applied to model the F0 contour of synthesized speech which
was generated by HMM-based speech synthesis system. The experiment was done on
Bengali language. Several DBN-DNN architectures ranging from four to seven
hidden layers and up to 200 hid- den units per hidden layer was presented and
evaluated. The results were compared against clustering tree techniques pop-
ularly found in statistical parametric speech synthesis. We show that from
textual inputs DBN-DNN learns a high level structure which in turn improves F0
contour in terms of ob- jective and subjective tests.
IAT - Image Annotation Tool: Manual
Authors: Gianluigi Ciocca, Paolo Napoletano, Raimondo Schettini
Date: 2015-02-18 00:00:00
Tasks: Information Retrieval, Object, Retrieval

The annotation of image and video data of large datasets is a fundamental
task in multimedia information retrieval and computer vision applications. In
order to support the users during the image and video annotation process,
several software tools have been developed to provide them with a graphical
environment which helps drawing object contours, handling tracking information
and specifying object metadata. Here we introduce a preliminary version of the
image annotation tools developed at the Imaging and Vision Laboratory.
A Stochastic Quasi-Newton Method for Large-Scale Optimization
Authors: R. H. Byrd, S. L. Hansen, J. Nocedal, Y. Singer
Date: 2014-01-27 00:00:00

The question of how to incorporate curvature information in stochastic
approximation methods is challenging. The direct application of classical
quasi- Newton updating techniques for deterministic optimization leads to noisy
curvature estimates that have harmful effects on the robustness of the
iteration. In this paper, we propose a stochastic quasi-Newton method that is
efficient, robust and scalable. It employs the classical BFGS update formula in
its limited memory form, and is based on the observation that it is beneficial
to collect curvature information pointwise, and at regular intervals, through
(sub-sampled) Hessian-vector products. This technique differs from the
classical approach that would compute differences of gradients, and where
controlling the quality of the curvature estimates can be difficult. We present
numerical results on problems arising in machine learning that suggest that the
proposed method shows much promise.
Dengue disease prediction using weka data mining tool
Authors: Kashish Ara Shakil, Shadma Anis, Mansaf Alam
Date: 2015-02-18 00:00:00
Tasks: Disease Prediction, Prediction

Dengue is a life threatening disease prevalent in several developed as well
as developing countries like India.In this paper we discuss various algorithm
approaches of data mining that have been utilized for dengue disease
prediction. Data mining is a well known technique used by health organizations
for classification of diseases such as dengue, diabetes and cancer in
bioinformatics research. In the proposed approach we have used WEKA with 10
cross validation to evaluate data and compare results. Weka has an extensive
collection of different machine learning and data mining algorithms. In this
paper we have firstly classified the dengue data set and then compared the
different data mining techniques in weka through Explorer, knowledge flow and
Experimenter interfaces. Furthermore in order to validate our approach we have
used a dengue dataset with 108 instances but weka used 99 rows and 18
attributes to determine the prediction of disease and their accuracy using
classifications of different algorithms to find out the best performance. The
main objective of this paper is to classify data and assist the users in
extracting useful information from data and easily identify a suitable
algorithm for accurate predictive model from it. From the findings of this
paper it can be concluded that Na\"ive Bayes and J48 are the best performance
algorithms for classified accuracy because they achieved maximum accuracy= 100%
with 99 correctly classified instances, maximum ROC = 1, had least mean
absolute error and it took minimum time for building this model through
Explorer and Knowledge flow results
Proper Complex Gaussian Processes for Regression
Authors: Rafael Boloix-Tortosa, F. Javier Payán-Somet, Eva Arias-de-Reyna, Juan José Murillo-Fuentes
Date: 2015-02-17 00:00:00
Tasks: Gaussian Processes, regression

Complex-valued signals are used in the modeling of many systems in
engineering and science, hence being of fundamental interest. Often, random
complex-valued signals are considered to be proper. A proper complex random
variable or process is uncorrelated with its complex conjugate. This assumption
is a good model of the underlying physics in many problems, and simplifies the
computations. While linear processing and neural networks have been widely
studied for these signals, the development of complex-valued nonlinear kernel
approaches remains an open problem. In this paper we propose Gaussian processes
for regression as a framework to develop 1) a solution for proper
complex-valued kernel regression and 2) the design of the reproducing kernel
for complex-valued inputs, using the convolutional approach for
cross-covariances. In this design we pay attention to preserve, in the complex
domain, the measure of similarity between near inputs. The hyperparameters of
the kernel are learned maximizing the marginal likelihood using Wirtinger
derivatives. Besides, the approach is connected to the multiple output learning
scenario. In the experiments included, we first solve a proper complex Gaussian
process where the cross-covariance does not cancel, a challenging scenario when
dealing with proper complex signals. Then we successfully use these novel
results to solve some problems previously proposed in the literature as
benchmarks, reporting a remarkable improvement in the estimation error.
Phrase Based Language Model for Statistical Machine Translation: Empirical Study
Authors: Geliang Chen
Date: 2015-01-21 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Re-Ranking, Sentence, speech-recognition, Speech Recognition, Translation

Reordering is a challenge to machine translation (MT) systems. In MT, the
widely used approach is to apply word based language model (LM) which considers
the constituent units of a sentence as words. In speech recognition (SR), some
phrase based LM have been proposed. However, those LMs are not necessarily
suitable or optimal for reordering. We propose two phrase based LMs which
considers the constituent units of a sentence as phrases. Experiments show that
our phrase based LMs outperform the word based LM with the respect of
perplexity and n-best list re-ranking.
Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets
Authors: Jiajun Liu, Kun Zhao, Brano Kusy, Ji-Rong Wen, Raja Jurdak
Date: 2015-02-18 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

The prediction of periodical time-series remains challenging due to various
types of data distortions and misalignments. Here, we propose a novel model
called Temporal embedding-enhanced convolutional neural Network (TeNet) to
learn repeatedly-occurring-yet-hidden structural elements in periodical
time-series, called abstract snippets, for predicting future changes. Our model
uses convolutional neural networks and embeds a time-series with its potential
neighbors in the temporal domain for aligning it to the dominant patterns in
the dataset. The model is robust to distortions and misalignments in the
temporal domain and demonstrates strong prediction power for periodical
time-series.
  We conduct extensive experiments and discover that the proposed model shows
significant and consistent advantages over existing methods on a variety of
data modalities ranging from human mobility to household power consumption
records. Empirical results indicate that the model is robust to various factors
such as number of samples, variance of data, numerical ranges of data etc. The
experiments also verify that the intuition behind the model can be generalized
to multiple data types and applications and promises significant improvement in
prediction performances across the datasets studied.
CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data
Authors: Fangfang Li, Guandong Xu, Longbing Cao
Date: 2015-02-18 00:00:00
Tasks: Classification, Clustering, General Classification

Supervised classification approaches can predict labels for unknown data
because of the supervised training process. The success of classification is
heavily dependent on the labeled training data. Differently, clustering is
effective in revealing the aggregation property of unlabeled data, but the
performance of most clustering methods is limited by the absence of labeled
data. In real applications, however, it is time-consuming and sometimes
impossible to obtain labeled data. The combination of clustering and
classification is a promising and active approach which can largely improve the
performance. In this paper, we propose an innovative and effective clustering
framework based on self-adaptive labeling (CSAL) which integrates clustering
and classification on unlabeled data. Clustering is first employed to partition
data and a certain proportion of clustered data are selected by our proposed
labeling approach for training classifiers. In order to refine the trained
classifiers, an iterative process of Expectation-Maximization algorithm is
devised into the proposed clustering framework CSAL. Experiments are conducted
on publicly data sets to test different combinations of clustering algorithms
and classification models as well as various training data labeling methods.
The experimental results show that our approach along with the self-adaptive
method outperforms other methods.
Real time clustering of time series using triangular potentials
Authors: Aldo Pacchiano, Oliver Williams
Date: 2015-02-18 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Motivated by the problem of computing investment portfolio weightings we
investigate various methods of clustering as alternatives to traditional
mean-variance approaches. Such methods can have significant benefits from a
practical point of view since they remove the need to invert a sample
covariance matrix, which can suffer from estimation error and will almost
certainly be non-stationary. The general idea is to find groups of assets which
share similar return characteristics over time and treat each group as a single
composite asset. We then apply inverse volatility weightings to these new
composite assets. In the course of our investigation we devise a method of
clustering based on triangular potentials and we present associated theoretical
results as well as various examples based on synthetic data.
On Sex, Evolution, and the Multiplicative Weights Update Algorithm
Authors: Reshef Meir, David Parkes
Date: 2015-02-17 00:00:00

We consider a recent innovative theory by Chastain et al. on the role of sex
in evolution [PNAS'14]. In short, the theory suggests that the evolutionary
process of gene recombination implements the celebrated multiplicative weights
updates algorithm (MWUA). They prove that the population dynamics induced by
sexual reproduction can be precisely modeled by genes that use MWUA as their
learning strategy in a particular coordination game. The result holds in the
environments of \emph{weak selection}, under the assumption that the population
frequencies remain a product distribution.
  We revisit the theory, eliminating both the requirement of weak selection and
any assumption on the distribution of the population. Removing the assumption
of product distributions is crucial, since as we show, this assumption is
inconsistent with the population dynamics. We show that the marginal allele
distributions induced by the population dynamics precisely match the marginals
induced by a multiplicative weights update algorithm in this general setting,
thereby affirming and substantially generalizing these earlier results.
  We further revise the implications for convergence and utility or fitness
guarantees in coordination games. In contrast to the claim of Chastain et
al.[PNAS'14], we conclude that the sexual evolutionary dynamics does not entail
any property of the population distribution, beyond those already implied by
convergence.
Inductive Learning for Rule Generation from Ontology
Authors: Olegs Verhodubs
Date: 2015-02-17 00:00:00
Tasks: Inductive Learning

This paper presents an idea of inductive learning use for rule generation
from ontologies. The main purpose of the paper is to evaluate the possibility
of inductive learning use in rule generation from ontologies and to develop the
way how this can be done. Generated rules are necessary to supplement or even
to develop the Semantic Web Expert System (SWES) knowledge base. The SWES
emerges as the result of evolution of expert system concept toward the Web, and
the SWES is based on the Semantic Web technologies. Available publications show
that the problem of rule generation from ontologies based on inductive learning
is not investigated deeply enough.
On the Inductive Bias of Dropout
Authors: David P. Helmbold, Philip M. Long
Date: 2014-12-15 00:00:00
Tasks: Inductive Bias

Dropout is a simple but effective technique for learning in neural networks
and other settings. A sound theoretical understanding of dropout is needed to
determine when dropout should be applied and how to use it most effectively. In
this paper we continue the exploration of dropout as a regularizer pioneered by
Wager, et.al. We focus on linear classification where a convex proxy to the
misclassification loss (i.e. the logistic loss used in logistic regression) is
minimized. We show: (a) when the dropout-regularized criterion has a unique
minimizer, (b) when the dropout-regularization penalty goes to infinity with
the weights, and when it remains bounded, (c) that the dropout regularization
can be non-monotonic as individual weights increase from 0, and (d) that the
dropout regularization penalty may not be convex. This last point is
particularly surprising because the combination of dropout regularization with
any convex loss proxy is always a convex function.
  In order to contrast dropout regularization with $L_2$ regularization, we
formalize the notion of when different sources are more compatible with
different regularizers. We then exhibit distributions that are provably more
compatible with dropout regularization than $L_2$ regularization, and vice
versa. These sources provide additional insight into how the inductive biases
of dropout and $L_2$ regularization differ. We provide some similar results for
$L_1$ regularization.
Context Tricks for Cheap Semantic Segmentation
Authors: Thanapong Intharah, Gabriel J. Brostow
Date: 2015-02-17 00:00:00
Tasks: Segmentation, Semantic Segmentation

Accurate semantic labeling of image pixels is difficult because intra-class
variability is often greater than inter-class variability. In turn, fast
semantic segmentation is hard because accurate models are usually too
complicated to also run quickly at test-time. Our experience with building and
running semantic segmentation systems has also shown a reasonably obvious
bottleneck on model complexity, imposed by small training datasets. We
therefore propose two simple complementary strategies that leverage context to
give better semantic segmentation, while scaling up or down to train on
different-sized datasets.
  As easy modifications for existing semantic segmentation algorithms, we
introduce Decorrelated Semantic Texton Forests, and the Context Sensitive Image
Level Prior. The proposed modifications are tested using a Semantic Texton
Forest (STF) system, and the modifications are validated on two standard
benchmark datasets, MSRC-21 and PascalVOC-2010. In Python based comparisons,
our system is insignificantly slower than STF at test-time, yet produces
superior semantic segmentations overall, with just push-button training.
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework
Authors: Chuan-Yung Tsai, David D. Cox
Date: 2015-02-17 00:00:00
Tasks: Object Recognition

A central challenge in sensory neuroscience is describing how the activity of
populations of neurons can represent useful features of the external
environment. However, while neurophysiologists have long been able to record
the responses of neurons in awake, behaving animals, it is another matter
entirely to say what a given neuron does. A key problem is that in many sensory
domains, the space of all possible stimuli that one might encounter is
effectively infinite; in vision, for instance, natural scenes are
combinatorially complex, and an organism will only encounter a tiny fraction of
possible stimuli. As a result, even describing the response properties of
sensory neurons is difficult, and investigations of neuronal functions are
almost always critically limited by the number of stimuli that can be
considered. In this paper, we propose a closed-loop, optimization-based
experimental framework for characterizing the response properties of sensory
neurons, building on past efforts in closed-loop experimental methods, and
leveraging recent advances in artificial neural networks to serve as as a
proving ground for our techniques. Specifically, using deep convolutional
neural networks, we asked whether modern black-box optimization techniques can
be used to interrogate the "tuning landscape" of an artificial neuron in a
deep, nonlinear system, without imposing significant constraints on the space
of stimuli under consideration. We introduce a series of measures to quantify
the tuning landscapes, and show how these relate to the performances of the
networks in an object recognition task. To the extent that deep convolutional
neural networks increasingly serve as de facto working hypotheses for
biological vision, we argue that developing a unified approach for studying
both artificial and biological systems holds great potential to advance both
fields together.
Outperforming Word2Vec on Analogy Tasks with Random Projections
Authors: Abram Demski, Volkan Ustun, Paul Rosenbloom, Cody Kommers
Date: 2014-12-20 00:00:00
Tasks: Dimensionality Reduction

We present a distributed vector representation based on a simplification of
the BEAGLE system, designed in the context of the Sigma cognitive architecture.
Our method does not require gradient-based training of neural networks, matrix
decompositions as with LSA, or convolutions as with BEAGLE. All that is
involved is a sum of random vectors and their pointwise products. Despite the
simplicity of this technique, it gives state-of-the-art results on analogy
problems, in most cases better than Word2Vec. To explain this success, we
interpret it as a dimension reduction via random projection.
Spectrum Bandit Optimization
Authors: Marc Lelarge, Alexandre Proutiere, M. Sadegh Talebi
Date: 2013-02-27 00:00:00

We consider the problem of allocating radio channels to links in a wireless
network. Links interact through interference, modelled as a conflict graph
(i.e., two interfering links cannot be simultaneously active on the same
channel). We aim at identifying the channel allocation maximizing the total
network throughput over a finite time horizon. Should we know the average radio
conditions on each channel and on each link, an optimal allocation would be
obtained by solving an Integer Linear Program (ILP). When radio conditions are
unknown a priori, we look for a sequential channel allocation policy that
converges to the optimal allocation while minimizing on the way the throughput
loss or {\it regret} due to the need for exploring sub-optimal allocations. We
formulate this problem as a generic linear bandit problem, and analyze it first
in a stochastic setting where radio conditions are driven by a stationary
stochastic process, and then in an adversarial setting where radio conditions
can evolve arbitrarily. We provide new algorithms in both settings and derive
upper bounds on their regrets.