pwc-archive/papers-with-abstracts huggingface dataset:
Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition
Authors: Marius Leordeanu, Rahul Sukthankar
Date: 2014-04-02 00:00:00
Tasks: Object Recognition

We propose a general multi-class visual recognition model, termed the
Classifier Graph, which aims to generalize and integrate ideas from many of
today's successful hierarchical recognition approaches. Our graph-based model
has the advantage of enabling rich interactions between classes from different
levels of interpretation and abstraction. The proposed multi-class system is
efficiently learned using step by step updates. The structure consists of
simple logistic linear layers with inputs from features that are automatically
selected from a large pool. Each newly learned classifier becomes a potential
new feature. Thus, our feature pool can consist both of initial manually
designed features as well as learned classifiers from previous steps (graph
nodes), each copied many times at different scales and locations. In this
manner we can learn and grow both a deep, complex graph of classifiers and a
rich pool of features at different levels of abstraction and interpretation.
Our proposed graph of classifiers becomes a multi-class system with a recursive
structure, suitable for deep detection and recognition of several classes
simultaneously.
Cellular Automata and Its Applications in Bioinformatics: A Review
Authors: Pokkuluri Kiran Sree, Inampudi Ramesh Babu, SSSN Usha Devi N
Date: 2014-04-02 00:00:00

This paper aims at providing a survey on the problems that can be easily
addressed by cellular automata in bioinformatics. Some of the authors have
proposed algorithms for addressing some problems in bioinformatics but the
application of cellular automata in bioinformatics is a virgin field in
research. None of the researchers has tried to relate the major problems in
bioinformatics and find a common solution. Extensive literature surveys were
conducted. We have considered some papers in various journals and conferences
for conduct of our research. This paper provides intuition towards relating
various problems in bioinformatics logically and tries to attain a common frame
work for addressing the same.
An Enhanced Features Extractor for a Portfolio of Constraint Solvers
Authors: Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro
Date: 2013-08-01 00:00:00

Recent research has shown that a single arbitrarily efficient solver can be
significantly outperformed by a portfolio of possibly slower on-average
solvers. The solver selection is usually done by means of (un)supervised
learning techniques which exploit features extracted from the problem
specification. In this paper we present an useful and flexible framework that
is able to extract an extensive set of features from a Constraint
(Satisfaction/Optimization) Problem defined in possibly different modeling
languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results
showing that the performances that can be obtained using these features are
effective and competitive with state of the art CSP portfolio techniques.
Theory and Application of Shapelets to the Analysis of Surface Self-assembly Imaging
Authors: Robert Suderman, Daniel Lizotte, Nasser Mohieddin Abukhdeir
Date: 2014-04-02 00:00:00

A method for quantitative analysis of local pattern strength and defects in
surface self-assembly imaging is presented and applied to images of stripe and
hexagonal ordered domains. The presented method uses "shapelet" functions which
were originally developed for quantitative analysis of images of galaxies
($\propto 10^{20}\mathrm{m}$). In this work, they are used instead to quantify
the presence of translational order in surface self-assembled films ($\propto
10^{-9}\mathrm{m}$) through reformulation into "steerable" filters. The
resulting method is both computationally efficient (with respect to the number
of filter evaluations), robust to variation in pattern feature shape, and,
unlike previous approaches, is applicable to a wide variety of pattern types.
An application of the method is presented which uses a nearest-neighbour
analysis to distinguish between uniform (defect-free) and non-uniform
(strained, defect-containing) regions within imaged self-assembled domains,
both with striped and hexagonal patterns.
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
Authors: Adel Javanmard, Andrea Montanari
Date: 2013-06-13 00:00:00
Proceeding: NeurIPS 2013
Tasks: parameter estimation, regression, Two-sample testing, Vocal Bursts Intensity Prediction

Fitting high-dimensional statistical models often requires the use of
non-linear parameter estimation procedures. As a consequence, it is generally
impossible to obtain an exact characterization of the probability distribution
of the parameter estimates. This in turn implies that it is extremely
challenging to quantify the \emph{uncertainty} associated with a certain
parameter estimate. Concretely, no commonly accepted procedure exists for
computing classical measures of uncertainty and statistical significance as
confidence intervals or $p$-values for these models.
  We consider here high-dimensional linear regression problem, and propose an
efficient algorithm for constructing confidence intervals and $p$-values. The
resulting confidence intervals have nearly optimal size. When testing for the
null hypothesis that a certain parameter is vanishing, our method has nearly
optimal power.
  Our approach is based on constructing a `de-biased' version of regularized
M-estimators. The new construction improves over recent work in the field in
that it does not assume a special structure on the design matrix. We test our
method on synthetic data and a high-throughput genomic data set about
riboflavin production rate.
Equivalence of Kernel Machine Regression and Kernel Distance Covariance for Multidimensional Trait Association Studies
Authors: Wen-Yu Hua, Debashis Ghosh
Date: 2014-02-11 00:00:00
Tasks: regression

Associating genetic markers with a multidimensional phenotype is an important
yet challenging problem. In this work, we establish the equivalence between two
popular methods: kernel-machine regression (KMR), and kernel distance
covariance (KDC). KMR is a semiparametric regression frameworks that models the
covariate effects parametrically, while the genetic markers are considered
non-parametrically. KDC represents a class of methods that includes distance
covariance (DC) and Hilbert-Schmidt Independence Criterion (HSIC), which are
nonparametric tests of independence. We show the equivalence between the score
test of KMR and the KDC statistic under certain conditions. This result leads
to a novel generalization of the KDC test that incorporates the covariates. Our
contributions are three-fold: (1) establishing the equivalence between KMR and
KDC; (2) showing that the principles of kernel machine regression can be
applied to the interpretation of KDC; (3) the development of a broader class of
KDC statistics, that the members are the quantities of different kernels. We
demonstrate the proposals using simulation studies. Data from the Alzheimer's
Disease Neuroimaging Initiative (ADNI) is used to explore the association
between the genetic variants on gene \emph{FLJ16124} and phenotypes represented
in 3D structural brain MR images adjusting for age and gender. The results
suggest that SNPs of \emph{FLJ16124} exhibit strong pairwise interaction
effects that are correlated to the changes of brain region volumes.
A Deep Representation for Invariance And Music Classification
Authors: Chiyuan Zhang, Georgios Evangelopoulos, Stephen Voinea, Lorenzo Rosasco, Tomaso Poggio
Date: 2014-04-01 00:00:00
Tasks: Classification, General Classification, Genre classification, Music Classification, Music Genre Classification

Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.
Toward computational cumulative biology by combining models of biological datasets
Authors: Ali Faisal, Jaakko Peltonen, Elisabeth Georgii, Johan Rung, Samuel Kaski
Date: 2014-04-01 00:00:00
Tasks: Retrieval

A main challenge of data-driven sciences is how to make maximal use of the
progressively expanding databases of experimental datasets in order to keep
research cumulative. We introduce the idea of a modeling-based dataset
retrieval engine designed for relating a researcher's experimental dataset to
earlier work in the field. The search is (i) data-driven to enable new
findings, going beyond the state of the art of keyword searches in annotations,
(ii) modeling-driven, to both include biological knowledge and insights learned
from data, and (iii) scalable, as it is accomplished without building one
unified grand model of all data. Assuming each dataset has been modeled
beforehand, by the researchers or by database managers, we apply a rapidly
computable and optimizable combination model to decompose a new dataset into
contributions from earlier relevant models. By using the data-driven
decomposition we identify a network of interrelated datasets from a large
annotated human gene expression atlas. While tissue type and disease were major
driving forces for determining relevant datasets, the found relationships were
richer and the model-based search was more accurate than keyword search; it
moreover recovered biologically meaningful relationships that are not
straightforwardly visible from annotations, for instance, between cells in
different developmental stages such as thymocytes and T-cells. Data-driven
links and citations matched to a large extent; the data-driven links even
uncovered corrections to the publication data, as two of the most linked
datasets were not highly cited and turned out to have wrong publication entries
in the database.
Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems
Authors: Jan-Peter Calliess, Antonis Papachristodoulou, Stephen J. Roberts
Date: 2013-11-18 00:00:00

This work proposes a new method for simultaneous probabilistic identification
and control of an observable, fully-actuated mechanical system. Identification
is achieved by conditioning stochastic process priors on observations of
configurations and noisy estimates of configuration derivatives. In contrast to
previous work that has used stochastic processes for identification, we
leverage the structural knowledge afforded by Lagrangian mechanics and learn
the drift and control input matrix functions of the control-affine system
separately. We utilise feedback-linearisation to reduce, in expectation, the
uncertain nonlinear control problem to one that is easy to regulate in a
desired manner. Thereby, our method combines the flexibility of nonparametric
Bayesian learning with epistemological guarantees on the expected closed-loop
trajectory. We illustrate our method in the context of torque-actuated pendula
where the dynamics are learned with a combination of normal and log-normal
processes.
Household Electricity Demand Forecasting -- Benchmarking State-of-the-Art Methods
Authors: Andreas Veit, Christoph Goebel, Rohit Tidke, Christoph Doblander, Hans-Arno Jacobsen
Date: 2014-04-01 00:00:00
Tasks: Benchmarking, Demand Forecasting

The increasing use of renewable energy sources with variable output, such as
solar photovoltaic and wind power generation, calls for Smart Grids that
effectively manage flexible loads and energy storage. The ability to forecast
consumption at different locations in distribution systems will be a key
capability of Smart Grids. The goal of this paper is to benchmark
state-of-the-art methods for forecasting electricity demand on the household
level across different granularities and time scales in an explorative way,
thereby revealing potential shortcomings and find promising directions for
future research in this area. We apply a number of forecasting methods
including ARIMA, neural networks, and exponential smoothening using several
strategies for training data selection, in particular day type and sliding
window based strategies. We consider forecasting horizons ranging between 15
minutes and 24 hours. Our evaluation is based on two data sets containing the
power usage of individual appliances at second time granularity collected over
the course of several months. The results indicate that forecasting accuracy
varies significantly depending on the choice of forecasting methods/strategy
and the parameter configuration. Measured by the Mean Absolute Percentage Error
(MAPE), the considered state-of-the-art forecasting methods rarely beat
corresponding persistence forecasts. Overall, we observed MAPEs in the range
between 5 and >100%. The average MAPE for the first data set was ~30%, while it
was ~85% for the other data set. These results show big room for improvement.
Based on the identified trends and experiences from our experiments, we
contribute a detailed discussion of promising future research.
Efficient Algorithms and Error Analysis for the Modified Nystrom Method
Authors: Shusen Wang, Zhihua Zhang
Date: 2014-04-01 00:00:00

Many kernel methods suffer from high time and space complexities and are thus
prohibitive in big-data applications. To tackle the computational challenge,
the Nystr\"om method has been extensively used to reduce time and space
complexities by sacrificing some accuracy. The Nystr\"om method speedups
computation by constructing an approximation of the kernel matrix using only a
few columns of the matrix. Recently, a variant of the Nystr\"om method called
the modified Nystr\"om method has demonstrated significant improvement over the
standard Nystr\"om method in approximation accuracy, both theoretically and
empirically.
  In this paper, we propose two algorithms that make the modified Nystr\"om
method practical. First, we devise a simple column selection algorithm with a
provable error bound. Our algorithm is more efficient and easier to implement
than and nearly as accurate as the state-of-the-art algorithm. Second, with the
selected columns at hand, we propose an algorithm that computes the
approximation in lower time complexity than the approach in the previous work.
Furthermore, we prove that the modified Nystr\"om method is exact under certain
conditions, and we establish a lower error bound for the modified Nystr\"om
method.
Traffic Monitoring Using M2M Communication
Authors: Shiu Kumar, Eun Sik Ham, Seong Ro Lee
Date: 2014-04-01 00:00:00

This paper presents an intelligent traffic monitoring system using wireless
vision sensor network that captures and processes the real-time video image to
obtain the traffic flow rate and vehicle speeds along different urban roadways.
This system will display the traffic states on the front roadways that can
guide the drivers to select the right way and avoid potential traffic
congestions. On the other hand, it will also monitor the vehicle speeds and
store the vehicle details, for those breaking the roadway speed limits, in its
database. The real-time traffic data is processed by the Personal Computer (PC)
at the sub roadway station and the traffic flow rate data is transmitted to the
main roadway station Arduino 3G via email, where the data is extracted and
traffic flow rate displayed.
Venture: a higher-order probabilistic programming platform with programmable inference
Authors: Vikash Mansinghka, Daniel Selsam, Yura Perov
Date: 2014-04-01 00:00:00
Tasks: Probabilistic Programming, Variational Inference

We describe Venture, an interactive virtual machine for probabilistic
programming that aims to be sufficiently expressive, extensible, and efficient
for general-purpose use. Like Church, probabilistic models and inference
problems in Venture are specified via a Turing-complete, higher-order
probabilistic language descended from Lisp. Unlike Church, Venture also
provides a compositional language for custom inference strategies built out of
scalable exact and approximate techniques. We also describe four key aspects of
Venture's implementation that build on ideas from probabilistic graphical
models. First, we describe the stochastic procedure interface (SPI) that
specifies and encapsulates primitive random variables. The SPI supports custom
control flow, higher-order probabilistic procedures, partially exchangeable
sequences and ``likelihood-free'' stochastic simulators. It also supports
external models that do inference over latent variables hidden from Venture.
Second, we describe probabilistic execution traces (PETs), which represent
execution histories of Venture programs. PETs capture conditional dependencies,
existential dependencies and exchangeable coupling. Third, we describe
partitions of execution histories called scaffolds that factor global inference
problems into coherent sub-problems. Finally, we describe a family of
stochastic regeneration algorithms for efficiently modifying PET fragments
contained within scaffolds. Stochastic regeneration linear runtime scaling in
cases where many previous approaches scaled quadratically. We show how to use
stochastic regeneration and the SPI to implement general-purpose inference
strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals
based on particle Markov chain Monte Carlo and mean-field variational inference
techniques.
Using HMM in Strategic Games
Authors: Mario Benevides, Isaque Lima, Rafael Nader, Pedro Rougemont
Date: 2014-04-01 00:00:00

In this paper we describe an approach to resolve strategic games in which
players can assume different types along the game. Our goal is to infer which
type the opponent is adopting at each moment so that we can increase the
player's odds. To achieve that we use Markov games combined with hidden Markov
model. We discuss a hypothetical example of a tennis game whose solution can be
applied to any game with similar characteristics.
Simultaneous Perturbation Algorithms for Batch Off-Policy Search
Authors: Raphael Fonteneau, L. A. Prashanth
Date: 2014-03-18 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

We propose novel policy search algorithms in the context of off-policy, batch
mode reinforcement learning (RL) with continuous state and action spaces. Given
a batch collection of trajectories, we perform off-line policy evaluation using
an algorithm similar to that by [Fonteneau et al., 2010]. Using this
Monte-Carlo like policy evaluator, we perform policy search in a class of
parameterized policies. We propose both first order policy gradient and second
order policy Newton algorithms. All our algorithms incorporate simultaneous
perturbation estimates for the gradient as well as the Hessian of the
cost-to-go vector, since the latter is unknown and only biased estimates are
available. We demonstrate their practicality on a simple 1-dimensional
continuous state space problem.
Big data and the SP theory of intelligence
Authors: J. Gerard Wolff
Date: 2013-06-13 00:00:00
Tasks: Management

This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" may, with advantage, be applied to the management and
analysis of big data. The SP system -- introduced in the article and fully
described elsewhere -- may help to overcome the problem of variety in big data:
it has potential as "a universal framework for the representation and
processing of diverse kinds of knowledge" (UFK), helping to reduce the
diversity of formalisms and formats for knowledge and the different ways in
which they are processed. It has strengths in the unsupervised learning or
discovery of structure in data, in pattern recognition, in the parsing and
production of natural language, in several kinds of reasoning, and more. It
lends itself to the analysis of streaming data, helping to overcome the problem
of velocity in big data. Central in the workings of the system is lossless
compression of information: making big data smaller and reducing problems of
storage and management. There is potential for substantial economies in the
transmission of data, for big cuts in the use of energy in computing, for
faster processing, and for smaller and lighter computers. The system provides a
handle on the problem of veracity in big data, with potential to assist in the
management of errors and uncertainties in data. It lends itself to the
visualisation of knowledge structures and inferential processes. A
high-parallel, open-source version of the SP machine would provide a means for
researchers everywhere to explore what can be done with the system and to
create new versions of it.
Coding for Random Projections and Approximate Near Neighbor Search
Authors: Ping Li, Michael Mitzenmacher, Anshumali Shrivastava
Date: 2014-03-31 00:00:00
Tasks: Quantization

This technical note compares two coding (quantization) schemes for random
projections in the context of sub-linear time approximate near neighbor search.
The first scheme is based on uniform quantization while the second scheme
utilizes a uniform quantization plus a uniformly random offset (which has been
popular in practice). The prior work compared the two schemes in the context of
similarity estimation and training linear classifiers, with the conclusion that
the step of random offset is not necessary and may hurt the performance
(depending on the similarity level). The task of near neighbor search is
related to similarity estimation with importance distinctions and requires own
study. In this paper, we demonstrate that in the context of near neighbor
search, the step of random offset is not needed either and may hurt the
performance (sometimes significantly so, depending on the similarity and other
parameters).
Non-characterizability of belief revision: an application of finite model theory
Authors: Gyorgy Turan, Jon Yaggie
Date: 2014-03-25 00:00:00

A formal framework is given for the characterizability of a class of belief
revision operators, defined using minimization over a class of partial
preorders, by postulates. It is shown that for partial orders
characterizability implies a definability property of the class of partial
orders in monadic second-order logic. Based on a non-definability result for a
class of partial orders, an example is given of a non-characterizable class of
revision operators. This appears to be the first non-characterizability result
in belief revision.