pwc-archive/papers-with-abstracts huggingface dataset:
Gene Similarity-based Approaches for Determining Core-Genes of Chloroplasts
Authors: Bassam AlKindy, Christophe Guyeux, Jean-François Couchot, Michel Salomon, Jacques M. Bahi
Date: 2014-12-17 00:00:00
Tasks: Clustering

In computational biology and bioinformatics, the manner to understand
evolution processes within various related organisms paid a lot of attention
these last decades. However, accurate methodologies are still needed to
discover genes content evolution. In a previous work, two novel approaches
based on sequence similarities and genes features have been proposed. More
precisely, we proposed to use genes names, sequence similarities, or both,
insured either from NCBI or from DOGMA annotation tools. Dogma has the
advantage to be an up-to-date accurate automatic tool specifically designed for
chloroplasts, whereas NCBI possesses high quality human curated genes (together
with wrongly annotated ones). The key idea of the former proposal was to take
the best from these two tools. However, the first proposal was limited by name
variations and spelling errors on the NCBI side, leading to core trees of low
quality. In this paper, these flaws are fixed by improving the comparison of
NCBI and DOGMA results, and by relaxing constraints on gene names while adding
a stage of post-validation on gene sequences. The two stages of similarity
measures, on names and sequences, are thus proposed for sequence clustering.
This improves results that can be obtained using either NCBI or DOGMA alone.
Results obtained with this quality control test are further investigated and
compared with previously released ones, on both computational and biological
aspects, considering a set of 99 chloroplastic genomes.
An Algebraical Model for Gray Level Images
Authors: Vasile Patrascu
Date: 2014-12-17 00:00:00
Tasks: Image Enhancement, model

In this paper we propose a new algebraical model for the gray level images.
It can be used for digital image processing. The model adresses to those images
which are generated in improper light conditions (very low or high level). The
vector space structure is able to illustrate some features into the image using
modified level of contrast and luminosity. Also, the defined structure could be
used in image enhancement. The general approach is presented with experimental
results to demonstrate image enhancement.
Iranian cashes recognition using mobile
Authors: Ismail Nojavani, Azade Rezaeezade, Amirhassan Monadjemi
Date: 2014-12-17 00:00:00
Tasks: Marketing

In economical societies of today, using cash is an inseparable aspect of
human life. People use cashes for marketing, services, entertainments, bank
operations and so on. This huge amount of contact with cash and the necessity
of knowing the monetary value of it caused one of the most challenging problems
for visually impaired people. In this paper we propose a mobile phone based
approach to identify monetary value of a picture taken from cashes using some
image processing and machine vision techniques. While the developed approach is
very fast, it can recognize the value of cash by average accuracy of about 95%
and can overcome different challenges like rotation, scaling, collision,
illumination changes, perspective, and some others.
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm
Authors: Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou
Date: 2014-12-17 00:00:00
Tasks: regression

In this paper we study the consistency of an empirical minimum error entropy
(MEE) algorithm in a regression setting. We introduce two types of consistency.
The error entropy consistency, which requires the error entropy of the learned
function to approximate the minimum error entropy, is shown to be always true
if the bandwidth parameter tends to 0 at an appropriate rate. The regression
consistency, which requires the learned function to approximate the regression
function, however, is a complicated issue. We prove that the error entropy
consistency implies the regression consistency for homoskedastic models where
the noise is independent of the input variable. But for heteroskedastic models,
a counterexample is used to show that the two types of consistency do not
coincide. A surprising result is that the regression consistency is always
true, provided that the bandwidth parameter tends to infinity at an appropriate
rate. Regression consistency of two classes of special models is shown to hold
with fixed bandwidth parameter, which further illustrates the complexity of
regression consistency of MEE. Fourier transform plays crucial roles in our
analysis.
ANN Model to Predict Stock Prices at Stock Exchange Markets
Authors: B. W. Wanjawa, L. Muchemi
Date: 2014-12-17 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Stock exchanges are considered major players in financial sectors of many
countries. Most Stockbrokers, who execute stock trade, use technical,
fundamental or time series analysis in trying to predict stock prices, so as to
advise clients. However, these strategies do not usually guarantee good returns
because they guide on trends and not the most likely price. It is therefore
necessary to explore improved methods of prediction.
  The research proposes the use of Artificial Neural Network that is
feedforward multi-layer perceptron with error backpropagation and develops a
model of configuration 5:21:21:1 with 80% training data in 130,000 cycles. The
research develops a prototype and tests it on 2008-2012 data from stock markets
e.g. Nairobi Securities Exchange and New York Stock Exchange, where prediction
results show MAPE of between 0.71% and 2.77%. Validation done with Encog and
Neuroph realized comparable results. The model is thus capable of prediction on
typical stock markets.
Learning unbiased features
Authors: Yujia Li, Kevin Swersky, Richard Zemel
Date: 2014-12-17 00:00:00
Tasks: Domain Adaptation, Representation Learning, Transfer Learning

A key element in transfer learning is representation learning; if
representations can be developed that expose the relevant factors underlying
the data, then new tasks and domains can be learned readily based on mappings
of these salient factors. We propose that an important aim for these
representations are to be unbiased. Different forms of representation learning
can be derived from alternative definitions of unwanted bias, e.g., bias to
particular tasks, domains, or irrelevant underlying data dimensions. One very
useful approach to estimating the amount of bias in a representation comes from
maximum mean discrepancy (MMD) [5], a measure of distance between probability
distributions. We are not the first to suggest that MMD can be a useful
criterion in developing representations that apply across multiple domains or
tasks [1]. However, in this paper we describe a number of novel applications of
this criterion that we have devised, all based on the idea of developing
unbiased representations. These formulations include: a standard domain
adaptation framework; a method of learning invariant representations; an
approach based on noise-insensitive autoencoders; and a novel form of
generative model.
The supervised hierarchical Dirichlet process
Authors: Andrew M. Dai, Amos J. Storkey
Date: 2014-12-17 00:00:00
Tasks: regression

We propose the supervised hierarchical Dirichlet process (sHDP), a
nonparametric generative model for the joint distribution of a group of
observations and a response variable directly associated with that whole group.
We compare the sHDP with another leading method for regression on grouped data,
the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method
on two real-world classification problems and two real-world regression
problems. Bayesian nonparametric regression models based on the Dirichlet
process, such as the Dirichlet process-generalised linear models (DP-GLM) have
previously been explored; these models allow flexibility in modelling nonlinear
relationships. However, until now, Hierarchical Dirichlet Process (HDP)
mixtures have not seen significant use in supervised problems with grouped data
since a straightforward application of the HDP on the grouped data results in
learnt clusters that are not predictive of the responses. The sHDP solves this
problem by allowing for clusters to be learnt jointly from the group structure
and from the label assigned to each group.
Testing MCMC code
Authors: Roger B. Grosse, David K. Duvenaud
Date: 2014-12-16 00:00:00

Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic
modeling and inference, but are difficult to debug, and are prone to silent
failure if implemented naively. We outline several strategies for testing the
correctness of MCMC algorithms. Specifically, we advocate writing code in a
modular way, where conditional probability calculations are kept separate from
the logic of the sampler. We discuss strategies for both unit testing and
integration testing. As a running example, we show how a Python implementation
of Gibbs sampling for a mixture of Gaussians model can be tested.
Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods
Authors: Animashree Anandkumar, Rong Ge, Majid Janzamin
Date: 2014-08-03 00:00:00
Tasks: Tensor Decomposition

We provide guarantees for learning latent variable models emphasizing on the
overcomplete regime, where the dimensionality of the latent space can exceed
the observed dimensionality. In particular, we consider multiview mixtures,
spherical Gaussian mixtures, ICA, and sparse coding models. We provide tight
concentration bounds for empirical moments through novel covering arguments. We
analyze parameter recovery through a simple tensor power update algorithm. In
the semi-supervised setting, we exploit the label or prior information to get a
rough estimate of the model parameters, and then refine it using the tensor
method on unlabeled samples. We establish that learning is possible when the
number of components scales as $k=o(d^{p/2})$, where $d$ is the observed
dimension, and $p$ is the order of the observed moment employed in the tensor
method. Our concentration bound analysis also leads to minimax sample
complexity for semi-supervised learning of spherical Gaussian mixtures. In the
unsupervised setting, we use a simple initialization algorithm based on SVD of
the tensor slices, and provide guarantees under the stricter condition that
$k\le \beta d$ (where constant $\beta$ can be larger than $1$), where the
tensor method recovers the components under a polynomial running time (and
exponential in $\beta$). Our analysis establishes that a wide range of
overcomplete latent variable models can be learned efficiently with low
computational and sample complexity through tensor decomposition methods.
Application of Topic Models to Judgments from Public Procurement Domain
Authors: Michał Łopuszyński
Date: 2014-12-16 00:00:00
Tasks: Information Retrieval, Keyword Extraction, Retrieval, Topic Models

In this work, automatic analysis of themes contained in a large corpora of
judgments from public procurement domain is performed. The employed technique
is unsupervised latent Dirichlet allocation (LDA). In addition, it is proposed,
to use LDA in conjunction with recently developed method of unsupervised
keyword extraction. Such an approach improves the interpretability of the
automatically obtained topics and allows for better computational performance.
The described analysis illustrates a potential of the method in detecting
recurring themes and discovering temporal trends in lodged contract appeals.
These results may be in future applied to improve information retrieval from
repositories of legal texts or as auxiliary material for legal analyses carried
out by human experts.
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model
Authors: Ethan X. Fang, Yang Ning, Han Liu
Date: 2014-12-16 00:00:00
Tasks: Model Selection, Vocal Bursts Intensity Prediction

This paper proposes a decorrelation-based approach to test hypotheses and
construct confidence intervals for the low dimensional component of high
dimensional proportional hazards models. Motivated by the geometric projection
principle, we propose new decorrelated score, Wald and partial likelihood ratio
statistics. Without assuming model selection consistency, we prove the
asymptotic normality of these test statistics, establish their semiparametric
optimality. We also develop new procedures for constructing pointwise
confidence intervals for the baseline hazard function and baseline survival
function. Thorough numerical results are provided to back up our theory.
A Multi-criteria neutrosophic group decision making metod based TOPSIS for supplier selection
Authors: Rıdvan Şahin, Muhammed Yiğider
Date: 2014-12-16 00:00:00
Tasks: Decision Making

The process of multiple criteria decision making (MCDM) is of determining the
best choice among all of the probable alternatives. The problem of supplier
selection on which decision maker has usually vague and imprecise knowledge is
a typical example of multi criteria group decision-making problem. The
conventional crisp techniques has not much effective for solving MCDM problems
because of imprecise or fuzziness nature of the linguistic assessments. To find
the exact values for MCDM problems is both difficult and impossible in more
cases in real world. So, it is more reasonable to consider the values of
alternatives according to the criteria as single valued neutrosophic sets
(SVNS). This paper deal with the technique for order preference by similarity
to ideal solution (TOPSIS) approach and extend the TOPSIS method to MCDM
problem with single valued neutrosophic information. The value of each
alternative and the weight of each criterion are characterized by single valued
neutrosophic numbers. Here, the importance of criteria and alternatives is
identified by aggregating individual opinions of decision makers (DMs) via
single valued neutrosophic weighted averaging (IFWA) operator. The proposed
method is, easy use, precise and practical for solving MCDM problem with single
valued neutrosophic data. Finally, to show the applicability of the developed
method, a numerical experiment for supplier choice is given as an application
of single valued neutrosophic TOPSIS method at end of this paper.
Locally Scale-Invariant Convolutional Neural Networks
Authors: Angjoo Kanazawa, Abhishek Sharma, David Jacobs
Date: 2014-12-16 00:00:00

Convolutional Neural Networks (ConvNets) have shown excellent results on many
visual classification tasks. With the exception of ImageNet, these datasets are
carefully crafted such that objects are well-aligned at similar scales.
Naturally, the feature learning problem gets more challenging as the amount of
variation in the data increases, as the models have to learn to be invariant to
certain changes in appearance. Recent results on the ImageNet dataset show that
given enough data, ConvNets can learn such invariances producing very
discriminative features [1]. But could we do more: use less parameters, less
data, learn more discriminative features, if certain invariances were built
into the learning process? In this paper we present a simple model that allows
ConvNets to learn features in a locally scale-invariant manner without
increasing the number of model parameters. We show on a modified MNIST dataset
that when faced with scale variation, building in scale-invariance allows
ConvNets to learn more discriminative features with reduced chances of
over-fitting.
Type-Driven Incremental Semantic Parsing with Polymorphism
Authors: Kai Zhao, Liang Huang
Date: 2014-11-19 00:00:00
Proceeding: HLT 2015 5
Tasks: Semantic Parsing, Vocal Bursts Type Prediction

Semantic parsing has made significant progress, but most current semantic
parsers are extremely slow (CKY-based) and rather primitive in representation.
We introduce three new techniques to tackle these problems. First, we design
the first linear-time incremental shift-reduce-style semantic parsing algorithm
which is more efficient than conventional cubic-time bottom-up semantic
parsers. Second, our parser, being type-driven instead of syntax-driven, uses
type-checking to decide the direction of reduction, which eliminates the need
for a syntactic grammar such as CCG. Third, to fully exploit the power of
type-driven semantic parsing beyond simple types (such as entities and truth
values), we borrow from programming language theory the concepts of subtype
polymorphism and parametric polymorphism to enrich the type system in order to
better guide the parsing. Our system learns very accurate parses in GeoQuery,
Jobs and Atis domains.
Analysis of Optimal Recombination in Genetic Algorithm for a Scheduling Problem with Setups
Authors: A. V. Eremeev, Ju. V. Kovalenko
Date: 2014-12-16 00:00:00
Tasks: Scheduling

In this paper, we perform an experimental study of optimal recombination
operator for makespan minimization problem on single machine with
sequence-dependent setup times ($1|s_{vu}|C_{\max}$). The computational
experiment on benchmark problems from TSPLIB library indicates practical
applicability of optimal recombination in crossover operator of genetic
algorithm for $1|s_{vu}|C_{\max}$.
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing
Authors: Justin Ziniel, Philip Schniter, Per Sederberg
Date: 2014-01-05 00:00:00
Tasks: Classification, Compressive Sensing, feature selection, General Classification

For the problem of binary linear classification and feature selection, we
propose algorithmic approaches to classifier design based on the generalized
approximate message passing (GAMP) algorithm, recently proposed in the context
of compressive sensing. We are particularly motivated by problems where the
number of features greatly exceeds the number of training examples, but where
only a few features suffice for accurate classification. We show that
sum-product GAMP can be used to (approximately) minimize the classification
error rate and max-sum GAMP can be used to minimize a wide variety of
regularized loss functions. Furthermore, we describe an
expectation-maximization (EM)-based scheme to learn the associated model
parameters online, as an alternative to cross-validation, and we show that
GAMP's state-evolution framework can be used to accurately predict the
misclassification rate. Finally, we present a detailed numerical study to
confirm the accuracy, speed, and flexibility afforded by our GAMP-based
approaches to binary linear classification and feature selection.
Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
Authors: Tomas Ekeberg, Stefan Engblom, Jing Liu
Date: 2014-09-11 00:00:00
Tasks: BIG-bench Machine Learning, GPU

The classical method of determining the atomic structure of complex molecules
by analyzing diffraction patterns is currently undergoing drastic developments.
Modern techniques for producing extremely bright and coherent X-ray lasers
allow a beam of streaming particles to be intercepted and hit by an ultrashort
high energy X-ray beam. Through machine learning methods the data thus
collected can be transformed into a three-dimensional volumetric intensity map
of the particle itself. The computational complexity associated with this
problem is very high such that clusters of data parallel accelerators are
required.
  We have implemented a distributed and highly efficient algorithm for
inversion of large collections of diffraction patterns targeting clusters of
hundreds of GPUs. With the expected enormous amount of diffraction data to be
produced in the foreseeable future, this is the required scale to approach real
time processing of data at the beam site. Using both real and synthetic data we
look at the scaling properties of the application and discuss the overall
computational viability of this exciting and novel imaging technique.
A Scalable Asynchronous Distributed Algorithm for Topic Modeling
Authors: Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon
Date: 2014-12-16 00:00:00
Tasks: Topic Models

Learning meaningful topic models with massive document collections which
contain millions of documents and billions of tokens is challenging because of
two reasons: First, one needs to deal with a large number of topics (typically
in the order of thousands). Second, one needs a scalable and efficient way of
distributing the computation across multiple machines. In this paper we present
a novel algorithm F+Nomad LDA which simultaneously tackles both these problems.
In order to handle large number of topics we use an appropriately modified
Fenwick tree. This data structure allows us to sample from a multinomial
distribution over $T$ items in $O(\log T)$ time. Moreover, when topic counts
change the data structure can be updated in $O(\log T)$ time. In order to
distribute the computation across multiple processor we present a novel
asynchronous framework inspired by the Nomad algorithm of
\cite{YunYuHsietal13}. We show that F+Nomad LDA significantly outperform
state-of-the-art on massive problems which involve millions of documents,
billions of words, and thousands of topics.
Sparse, guided feature connections in an Abstract Deep Network
Authors: Anthony Knittel, Alan Blair
Date: 2014-12-16 00:00:00
Tasks: Evolutionary Algorithms, image-classification, Image Classification, Protein Structure Prediction

We present a technique for developing a network of re-used features, where
the topology is formed using a coarse learning method, that allows
gradient-descent fine tuning, known as an Abstract Deep Network (ADN). New
features are built based on observed co-occurrences, and the network is
maintained using a selection process related to evolutionary algorithms. This
allows coarse ex- ploration of the problem space, effective for irregular
domains, while gradient descent allows pre- cise solutions. Accuracy on
standard UCI and Protein-Structure Prediction problems is comparable with
benchmark SVM and optimized GBML approaches, and shows scalability for
addressing large problems. The discrete implementation is symbolic, allowing
interpretability, while the continuous method using fine-tuning shows improved
accuracy. The binary multiplexer problem is explored, as an irregular domain
that does not support gradient descent learning, showing solution to the bench-
mark 135-bit problem. A convolutional implementation is demonstrated on image
classification, showing an error-rate of 0.79% on the MNIST problem, without a
pre-defined topology. The ADN system provides a method for developing a very
sparse, deep feature topology, based on observed relationships between
features, that is able to find solutions in irregular domains, and initialize a
network prior to gradient descent learning.
Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning
Authors: Paul Irofti
Date: 2014-12-16 00:00:00
Tasks: Dictionary Learning, GPU

Dictionary training for sparse representations involves dealing with large
chunks of data and complex algorithms that determine time consuming
implementations. SBO is an iterative dictionary learning algorithm based on
constructing unions of orthonormal bases via singular value decomposition, that
represents each data item through a single best fit orthobase. In this paper we
present a GPGPU approach of implementing SBO in OpenCL. We provide a lock-free
solution that ensures full-occupancy of the GPU by following the map-reduce
model for the sparse-coding stage and by making use of the Partitioned Global
Address Space (PGAS) model for developing parallel dictionary updates. The
resulting implementation achieves a favourable trade-off between algorithm
complexity and data representation quality compared to PAK-SVD which is the
standard overcomplete dictionary learning approach. We present and discuss
numerical results showing a significant acceleration of the execution time for
the dictionary learning process.