pwc-archive/papers-with-abstracts huggingface dataset:
Approximate Lifted Inference with Probabilistic Databases
Authors: Wolfgang Gatterbauer, Dan Suciu
Date: 2014-12-02 00:00:00

This paper proposes a new approach for approximate evaluation of #P-hard
queries with probabilistic databases. In our approach, every query is evaluated
entirely in the database engine by evaluating a fixed number of query plans,
each providing an upper bound on the true probability, then taking their
minimum. We provide an algorithm that takes into account important schema
information to enumerate only the minimal necessary plans among all possible
plans. Importantly, this algorithm is a strict generalization of all known
results of PTIME self-join-free conjunctive queries: A query is safe if and
only if our algorithm returns one single plan. We also apply three relational
query optimization techniques to evaluate all minimal safe plans very fast. We
give a detailed experimental evaluation of our approach and, in the process,
provide a new way of thinking about the value of probabilistic methods over
non-probabilistic methods for ranking query answers.
Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve
Authors: Mehryar Mohri, Andres Muñoz Medina
Date: 2013-10-21 00:00:00
Tasks: Learning Theory

Second-price auctions with reserve play a critical role for modern search
engine and popular online sites since the revenue of these companies often
directly de- pends on the outcome of such auctions. The choice of the reserve
price is the main mechanism through which the auction revenue can be influenced
in these electronic markets. We cast the problem of selecting the reserve price
to optimize revenue as a learning problem and present a full theoretical
analysis dealing with the complex properties of the corresponding loss
function. We further give novel algorithms for solving this problem and report
the results of several experiments in both synthetic and real data
demonstrating their effectiveness.
Fuzzy human motion analysis: A review
Authors: Chern Hong Lim, Ekta Vats, Chee Seng Chan
Date: 2014-12-01 00:00:00

Human Motion Analysis (HMA) is currently one of the most popularly active
research domains as such significant research interests are motivated by a
number of real world applications such as video surveillance, sports analysis,
healthcare monitoring and so on. However, most of these real world applications
face high levels of uncertainties that can affect the operations of such
applications. Hence, the fuzzy set theory has been applied and showed great
success in the recent past. In this paper, we aim at reviewing the fuzzy set
oriented approaches for HMA, individuating how the fuzzy set may improve the
HMA, envisaging and delineating the future perspectives. To the best of our
knowledge, there is not found a single survey in the current literature that
has discussed and reviewed fuzzy approaches towards the HMA. For ease of
understanding, we conceptually classify the human motion into three broad
levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.
Group Factor Analysis
Authors: Arto Klami, Seppo Virtanen, Eemeli Leppäaho, Samuel Kaski
Date: 2014-11-21 00:00:00
Tasks: Variational Inference

Factor analysis provides linear factors that describe relationships between
individual variables of a data set. We extend this classical formulation into
linear factors that describe relationships between groups of variables, where
each group represents either a set of related variables or a data set. The
model also naturally extends canonical correlation analysis to more than two
sets, in a way that is more flexible than previous extensions. Our solution is
formulated as variational inference of a latent variable model with structural
sparsity, and it consists of two hierarchical levels: The higher level models
the relationships between the groups, whereas the lower models the observed
variables given the higher level. We show that the resulting solution solves
the group factor analysis problem accurately, outperforming alternative factor
analysis based solutions as well as more straightforward implementations of
group factor analysis. The method is demonstrated on two life science data
sets, one on brain activation and the other on systems biology, illustrating
its applicability to the analysis of different types of high-dimensional data
sources.
Justifying Answer Sets using Argumentation
Authors: Claudia Schulz, Francesca Toni
Date: 2014-11-20 00:00:00

An answer set is a plain set of literals which has no further structure that
would explain why certain literals are part of it and why others are not. We
show how argumentation theory can help to explain why a literal is or is not
contained in a given answer set by defining two justification methods, both of
which make use of the correspondence between answer sets of a logic program and
stable extensions of the Assumption-Based Argumentation (ABA) framework
constructed from the same logic program. Attack Trees justify a literal in
argumentation-theoretic terms, i.e. using arguments and attacks between them,
whereas ABA-Based Answer Set Justifications express the same justification
structure in logic programming terms, that is using literals and their
relationships. Interestingly, an ABA-Based Answer Set Justification corresponds
to an admissible fragment of the answer set in question, and an Attack Tree
corresponds to an admissible fragment of the stable extension corresponding to
this answer set.
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
Authors: Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
Date: 2014-12-02 00:00:00

The increased affordability of whole genome sequencing has motivated its use
for phenotypic studies. We address the problem of learning interpretable models
for discrete phenotypes from whole genomes. We propose a general approach that
relies on the Set Covering Machine and a k-mer representation of the genomes.
We show results for the problem of predicting the resistance of Pseudomonas
Aeruginosa, an important human pathogen, against 4 antibiotics. Our results
demonstrate that extremely sparse models which are biologically relevant can be
learnt using this approach.
Convex Techniques for Model Selection
Authors: Dustin Tran
Date: 2014-11-27 00:00:00
Tasks: model, Model Selection, regression

We develop a robust convex algorithm to select the regularization parameter
in model selection. In practice this would be automated in order to save
practitioners time from having to tune it manually. In particular, we implement
and test the convex method for $K$-fold cross validation on ridge regression,
although the same concept extends to more complex models. We then compare its
performance with standard methods.
Watsonsim: Overview of a Question Answering Engine
Authors: Sean Gallagher, Wlodek Zadrozny, Walid Shalaby, Adarsh Avadhani
Date: 2014-12-02 00:00:00
Tasks: Question Answering

The objective of the project is to design and run a system similar to Watson,
designed to answer Jeopardy questions. In the course of a semester, we
developed an open source question answering system using the Indri, Lucene,
Bing and Google search engines, Apache UIMA, Open- and CoreNLP, and Weka among
additional modules. By the end of the semester, we achieved 18% accuracy on
Jeopardy questions, and work has not stopped since then.
Semantic HMC for Big Data Analysis
Authors: Thomas Hassan, Rafael Peixoto, Christophe Cruz, Aurlie Bertaux, Nuno Silva
Date: 2014-12-02 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Hierarchical Multi-label Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Analyzing Big Data can help corporations to im-prove their efficiency. In
this work we present a new vision to derive Value from Big Data using a
Semantic Hierarchical Multi-label Classification called Semantic HMC based in a
non-supervised Ontology learning process. We also proposea Semantic HMC
process, using scalable Machine-Learning techniques and Rule-based reasoning.
Hashing on Nonlinear Manifolds
Authors: Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, Zhenmin Tang, Heng Tao Shen
Date: 2014-12-02 00:00:00
Tasks: image-classification, Image Classification, Quantization, Retrieval, Semantic Retrieval

Learning based hashing methods have attracted considerable attention due to
their ability to greatly increase the scale at which existing algorithms may
operate. Most of these methods are designed to generate binary codes preserving
the Euclidean similarity in the original space. Manifold learning techniques,
in contrast, are better able to model the intrinsic structure embedded in the
original high-dimensional data. The complexities of these models, and the
problems with out-of-sample data, have previously rendered them unsuitable for
application to large-scale embedding, however. In this work, how to learn
compact binary embeddings on their intrinsic manifolds is considered. In order
to address the above-mentioned difficulties, an efficient, inductive solution
to the out-of-sample data problem, and a process by which non-parametric
manifold learning may be used as the basis of a hashing method is proposed. The
proposed approach thus allows the development of a range of new hashing
techniques exploiting the flexibility of the wide variety of manifold learning
approaches available. It is particularly shown that hashing on the basis of
t-SNE outperforms state-of-the-art hashing methods on large-scale benchmark
datasets, and is very effective for image classification with very short code
lengths. The proposed hashing framework is shown to be easily improved, for
example, by minimizing the quantization error with learned orthogonal
rotations. In addition, a supervised inductive manifold hashing framework is
developed by incorporating the label information, which is shown to greatly
advance the semantic retrieval performance.
Analytical Comparison of Noise Reduction Filters for Image Restoration Using SNR Estimation
Authors: Poorna Banerjee Dasgupta
Date: 2014-12-02 00:00:00
Tasks: Image Restoration

Noise removal from images is a part of image restoration in which we try to
reconstruct or recover an image that has been degraded by using apriori
knowledge of the degradation phenomenon. Noises present in images can be of
various types with their characteristic Probability Distribution Functions
(PDF). Noise removal techniques depend on the kind of noise present in the
image rather than on the image itself. This paper explores the effects of
applying noise reduction filters having similar properties on noisy images with
emphasis on Signal-to-Noise-Ratio (SNR) value estimation for comparing the
results.
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Authors: Dawen Liang, Matthew D. Hoffman
Date: 2014-11-07 00:00:00
Tasks: Variational Inference

Beta process is the standard nonparametric Bayesian prior for latent factor
model. In this paper, we derive a structured mean-field variational inference
algorithm for a beta process non-negative matrix factorization (NMF) model with
Poisson likelihood. Unlike the linear Gaussian model, which is well-studied in
the nonparametric Bayesian literature, NMF model with beta process prior does
not enjoy the conjugacy. We leverage the recently developed stochastic
structured mean-field variational inference to relax the conjugacy constraint
and restore the dependencies among the latent variables in the approximating
variational distribution. Preliminary results on both synthetic and real
examples demonstrate that the proposed inference algorithm can reasonably
recover the hidden structure of the data.
Feedforward semantic segmentation with zoom-out features
Authors: Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich
Date: 2014-12-02 00:00:00
Proceeding: CVPR 2015 6
Tasks: Segmentation, Semantic Segmentation, Structured Prediction, Superpixels

We introduce a purely feed-forward architecture for semantic segmentation. We
map small image elements (superpixels) to rich feature representations
extracted from a sequence of nested regions of increasing extent. These regions
are obtained by "zooming out" from the superpixel all the way to scene-level
resolution. This approach exploits statistical structure in the image and in
the label space without setting up explicit structured prediction mechanisms,
and thus avoids complex and expensive inference. Instead superpixels are
classified by a feedforward multilayer network. Our architecture achieves new
state of the art performance in semantic segmentation, obtaining 64.4% average
accuracy on the PASCAL VOC 2012 test set.
Expressiveness of Logic Programs under General Stable Model Semantics
Authors: Heng Zhang, Yan Zhang
Date: 2014-12-02 00:00:00
Tasks: Translation

The stable model semantics had been recently generalized to non-Herbrand
structures by several works, which provides a unified framework and solid
logical foundations for answer set programming. This paper focuses on the
expressiveness of normal and disjunctive programs under the general stable
model semantics. A translation from disjunctive programs to normal programs is
proposed for infinite structures. Over finite structures, some disjunctive
programs are proved to be intranslatable to normal programs if the arities of
auxiliary predicates and functions are bounded in a certain way. The
equivalence of the expressiveness of normal programs and disjunctive programs
over arbitrary structures is also shown to coincide with that over finite
structures, and coincide with whether NP is closed under complement. Moreover,
to capture the exact expressiveness, some intertranslatability results between
logic program classes and fragments of second-order logic are obtained.
An Intelligent Personal Robot Assistant
Authors: Ong Sing Goh, Lance Fung
Date: 2014-11-05 00:00:00

Recent development in developing humanoid robot poses new challenges to
human-machine interaction communication. A major challenge is to develop robots
that can behave like and interact with human in the most natural way possible.
This paper proposes a system to develop a robot that can receive command, and
talk to people in natural language. In addition, the robot can also be
"trained" to become an expert in sepcific areas to provide expert advice to
human-beings. Most important of all, the robot can display emotions through
facial expression, speech and gesture so that the interaction process will
become more comprehensive and compelling.
Tiered Clustering to Improve Lexical Entailment
Authors: John Wieting
Date: 2014-12-02 00:00:00
Tasks: Clustering, Lexical Entailment

Many tasks in Natural Language Processing involve recognizing lexical
entailment. Two different approaches to this problem have been proposed
recently that are quite different from each other. The first is an asymmetric
similarity measure designed to give high scores when the contexts of the
narrower term in the entailment are a subset of those of the broader term. The
second is a supervised approach where a classifier is learned to predict
entailment given a concatenated latent vector representation of the word. Both
of these approaches are vector space models that use a single context vector as
a representation of the word. In this work, I study the effects of clustering
words into senses and using these multiple context vectors to infer entailment
using extensions of these two algorithms. I find that this approach offers some
improvement to these entailment algorithms.
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit
Authors: Ali Ayremlou, Thomas Goldstein, Ashok Veeraraghavan, Richard Baraniuk
Date: 2014-12-01 00:00:00
Tasks: Compressive Sensing, Denoising, Image Denoising, Object Recognition, Super-Resolution

Sparse approximations using highly over-complete dictionaries is a
state-of-the-art tool for many imaging applications including denoising,
super-resolution, compressive sensing, light-field analysis, and object
recognition. Unfortunately, the applicability of such methods is severely
hampered by the computational burden of sparse approximation: these algorithms
are linear or super-linear in both the data dimensionality and size of the
dictionary. We propose a framework for learning the hierarchical structure of
over-complete dictionaries that enables fast computation of sparse
representations. Our method builds on tree-based strategies for nearest
neighbor matching, and presents domain-specific enhancements that are highly
efficient for the analysis of image patches. Contrary to most popular methods
for building spatial data structures, out methods rely on shallow, balanced
trees with relatively few layers. We show an extensive array of experiments on
several applications such as image denoising/superresolution, compressive
video/light-field sensing where we practically achieve 100-1000x speedup (with
a less than 1dB loss in accuracy).
Predictive Overlapping Co-Clustering
Authors: Chandrima Sarkar, Jaideep Srivastava
Date: 2014-03-08 00:00:00
Tasks: Clustering, Community Detection, Recommendation Systems

In the past few years co-clustering has emerged as an important data mining
tool for two way data analysis. Co-clustering is more advantageous over
traditional one dimensional clustering in many ways such as, ability to find
highly correlated sub-groups of rows and columns. However, one of the
overlooked benefits of co-clustering is that, it can be used to extract
meaningful knowledge for various other knowledge extraction purposes. For
example, building predictive models with high dimensional data and
heterogeneous population is a non-trivial task. Co-clusters extracted from such
data, which shows similar pattern in both the dimension, can be used for a more
accurate predictive model building. Several applications such as finding
patient-disease cohorts in health care analysis, finding user-genre groups in
recommendation systems and community detection problems can benefit from
co-clustering technique that utilizes the predictive power of the data to
generate co-clusters for improved data analysis.
  In this paper, we present the novel idea of Predictive Overlapping
Co-Clustering (POCC) as an optimization problem for a more effective and
improved predictive analysis. Our algorithm generates optimal co-clusters by
maximizing predictive power of the co-clusters subject to the constraints on
the number of row and column clusters. In this paper precision, recall and
f-measure have been used as evaluation measures of the resulting co-clusters.
Results of our algorithm has been compared with two other well-known techniques
- K-means and Spectral co-clustering, over four real data set namely, Leukemia,
Internet-Ads, Ovarian cancer and MovieLens data set. The results demonstrate
the effectiveness and utility of our algorithm POCC in practice.
How to monitor and mitigate stair-casing in l1 trend filtering
Authors: Cristian R. Rojas, Bo Wahlberg
Date: 2014-12-01 00:00:00
Tasks: Denoising, Time Series, Time Series Analysis

In this paper we study the estimation of changing trends in time-series using
$\ell_1$ trend filtering. This method generalizes 1D Total Variation (TV)
denoising for detection of step changes in means to detecting changes in
trends, and it relies on a convex optimization problem for which there are very
efficient numerical algorithms. It is known that TV denoising suffers from the
so-called stair-case effect, which leads to detecting false change points. The
objective of this paper is to show that $\ell_1$ trend filtering also suffers
from a certain stair-case problem. The analysis is based on an interpretation
of the dual variables of the optimization problem in the method as integrated
random walk. We discuss consistency conditions for $\ell_1$ trend filtering,
how to monitor their fulfillment, and how to modify the algorithm to avoid the
stair-case false detection problem.
Scalability and Optimization Strategies for GPU Enhanced Neural Networks (GeNN)
Authors: Naresh Balaji, Esin Yavuz, Thomas Nowotny
Date: 2014-12-01 00:00:00
Tasks: Code Generation, GPU

Simulation of spiking neural networks has been traditionally done on
high-performance supercomputers or large-scale clusters. Utilizing the parallel
nature of neural network computation algorithms, GeNN (GPU Enhanced Neural
Network) provides a simulation environment that performs on General Purpose
NVIDIA GPUs with a code generation based approach. GeNN allows the users to
design and simulate neural networks by specifying the populations of neurons at
different stages, their synapse connection densities and the model of
individual neurons. In this report we describe work on how to scale synaptic
weights based on the configuration of the user-defined network to ensure
sufficient spiking and subsequent effective learning. We also discuss
optimization strategies particular to GPU computing: sparse representation of
synapse connections and occupancy based block-size determination.
Game-theoretical control with continuous action sets
Authors: Steven Perkins, Panayotis Mertikopoulos, David S. Leslie
Date: 2014-12-01 00:00:00
Tasks: Reinforcement Learning

Motivated by the recent applications of game-theoretical learning techniques
to the design of distributed control systems, we study a class of control
problems that can be formulated as potential games with continuous action sets,
and we propose an actor-critic reinforcement learning algorithm that provably
converges to equilibrium in this class of problems. The method employed is to
analyse the learning process under study through a mean-field dynamical system
that evolves in an infinite-dimensional function space (the space of
probability distributions over the players' continuous controls). To do so, we
extend the theory of finite-dimensional two-timescale stochastic approximation
to an infinite-dimensional, Banach space setting, and we prove that the
continuous dynamics of the process converge to equilibrium in the case of
potential games. These results combine to give a provably-convergent learning
algorithm in which players do not need to keep track of the controls selected
by the other agents.
Neutrosophic information in the framework of multi-valued representation
Authors: Vasile Patrascu
Date: 2014-12-01 00:00:00

The paper presents some steps for multi-valued representation of neutrosophic
information. These steps are provided in the framework of multi-valued logics
using the following logical value: true, false, neutral, unknown and saturated.
Also, this approach provides some calculus formulae for the following
neutrosophic features: truth, falsity, neutrality, ignorance,
under-definedness, over-definedness, saturation and entropy. In addition, it
was defined net truth, definedness and neutrosophic score.
Projective simulation for classical learning agents: a comprehensive investigation
Authors: Julian Mautner, Adi Makmal, Daniel Manzano, Markus Tiersch, Hans J. Briegel
Date: 2013-05-07 00:00:00
Tasks: Q-Learning, Reinforcement Learning

We study the model of projective simulation (PS), a novel approach to
artificial intelligence based on stochastic processing of episodic memory which
was recently introduced [H.J. Briegel and G. De las Cuevas. Sci. Rep. 2, 400,
(2012)]. Here we provide a detailed analysis of the model and examine its
performance, including its achievable efficiency, its learning times and the
way both properties scale with the problems' dimension. In addition, we situate
the PS agent in different learning scenarios, and study its learning abilities.
A variety of new scenarios are being considered, thereby demonstrating the
model's flexibility. Furthermore, to put the PS scheme in context, we compare
its performance with those of Q-learning and learning classifier systems, two
popular models in the field of reinforcement learning. It is shown that PS is a
competitive artificial intelligence model of unique properties and strengths.