pwc-archive/papers-with-abstracts huggingface dataset:
Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation
Authors: Jose Antonio Miñarro-Giménez, Oscar Marín-Alonso, Matthias Samwald
Date: 2015-02-12 00:00:00

BACKGROUND: The amount of biomedical literature is rapidly growing and it is
becoming increasingly difficult to keep manually curated knowledge bases and
ontologies up-to-date. In this study we applied the word2vec deep learning
toolkit to medical corpora to test its potential for identifying relationships
from unstructured text. We evaluated the efficiency of word2vec in identifying
properties of pharmaceuticals based on mid-sized, unstructured medical text
corpora available on the web. Properties included relationships to diseases
('may treat') or physiological processes ('has physiological effect'). We
compared the relationships identified by word2vec with manually curated
information from the National Drug File - Reference Terminology (NDF-RT)
ontology as a gold standard. RESULTS: Our results revealed a maximum accuracy
of 49.28% which suggests a limited ability of word2vec to capture linguistic
regularities on the collected medical corpora compared with other published
results. We were able to document the influence of different parameter settings
on result accuracy and found and unexpected trade-off between ranking quality
and accuracy. Pre-processing corpora to reduce syntactic variability proved to
be a good strategy for increasing the utility of the trained vector models.
CONCLUSIONS: Word2vec is a very efficient implementation for computing vector
representations and for its ability to identify relationships in textual data
without any prior domain knowledge. We found that the ranking and retrieved
results generated by word2vec were not of sufficient quality for automatic
population of knowledge bases and ontologies, but could serve as a starting
point for further manual curation.
Over-Sampling in a Deep Neural Network
Authors: Andrew J. R. Simpson
Date: 2015-02-12 00:00:00

Deep neural networks (DNN) are the state of the art on many engineering
problems such as computer vision and audition. A key factor in the success of
the DNN is scalability - bigger networks work better. However, the reason for
this scalability is not yet well understood. Here, we interpret the DNN as a
discrete system, of linear filters followed by nonlinear activations, that is
subject to the laws of sampling theory. In this context, we demonstrate that
over-sampled networks are more selective, learn faster and learn more robustly.
Our findings may ultimately generalize to the human brain.
Ordering-sensitive and Semantic-aware Topic Modeling
Authors: Min Yang, Tianyi Cui, Wenting Tu
Date: 2015-02-12 00:00:00
Tasks: Retrieval

Topic modeling of textual corpora is an important and challenging problem. In
most previous work, the "bag-of-words" assumption is usually made which ignores
the ordering of words. This assumption simplifies the computation, but it
unrealistically loses the ordering information and the semantic of words in the
context. In this paper, we present a Gaussian Mixture Neural Topic Model
(GMNTM) which incorporates both the ordering of words and the semantic meaning
of sentences into topic modeling. Specifically, we represent each topic as a
cluster of multi-dimensional vectors and embed the corpus into a collection of
vectors generated by the Gaussian mixture model. Each word is affected not only
by its topic, but also by the embedding vector of its surrounding words and the
context. The Gaussian mixture components and the topic of documents, sentences
and words can be learnt jointly. Extensive experiments show that our model can
learn better topics and more accurate word distributions for each topic.
Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM
obtains significantly better performance in terms of perplexity, retrieval
accuracy and classification accuracy.
A Predictive System for detection of Bankruptcy using Machine Learning techniques
Authors: Kalyan Nagaraj, Amulyashree Sridhar
Date: 2015-02-12 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Bankruptcy is a legal procedure that claims a person or organization as a
debtor. It is essential to ascertain the risk of bankruptcy at initial stages
to prevent financial losses. In this perspective, different soft computing
techniques can be employed to ascertain bankruptcy. This study proposes a
bankruptcy prediction system to categorize the companies based on extent of
risk. The prediction system acts as a decision support tool for detection of
bankruptcy
  Keywords: Bankruptcy, soft computing, decision support tool
Towards zero-configuration condition monitoring based on dictionary learning
Authors: Sergio Martin-del-Campo, Fredrik Sandin
Date: 2015-02-12 00:00:00
Tasks: Dictionary Learning

Condition-based predictive maintenance can significantly improve overall
equipment effectiveness provided that appropriate monitoring methods are used.
Online condition monitoring systems are customized to each type of machine and
need to be reconfigured when conditions change, which is costly and requires
expert knowledge. Basic feature extraction methods limited to signal
distribution functions and spectra are commonly used, making it difficult to
automatically analyze and compare machine conditions. In this paper, we
investigate the possibility to automate the condition monitoring process by
continuously learning a dictionary of optimized shift-invariant feature vectors
using a well-known sparse approximation method. We study how the feature
vectors learned from a vibration signal evolve over time when a fault develops
within a ball bearing of a rotating machine. We quantify the adaptation rate of
learned features and find that this quantity changes significantly in the
transitions between normal and faulty states of operation of the ball bearing.
On the Easiest and Hardest Fitness Functions
Authors: Jun He, Tianshi Chen, Xin Yao
Date: 2012-03-28 00:00:00
Tasks: Evolutionary Algorithms

The hardness of fitness functions is an important research topic in the field
of evolutionary computation. In theory, the study can help understanding the
ability of evolutionary algorithms. In practice, the study may provide a
guideline to the design of benchmarks. The aim of this paper is to answer the
following research questions: Given a fitness function class, which functions
are the easiest with respect to an evolutionary algorithm? Which are the
hardest? How are these functions constructed? The paper provides theoretical
answers to these questions. The easiest and hardest fitness functions are
constructed for an elitist (1+1) evolutionary algorithm to maximise a class of
fitness functions with the same optima. It is demonstrated that the unimodal
functions are the easiest and deceptive functions are the hardest in terms of
the time-fitness landscape. The paper also reveals that the easiest fitness
function to one algorithm may become the hardest to another algorithm, and vice
versa.
Web spam classification using supervised artificial neural network algorithms
Authors: Ashish Chandra, Mohammad Suaib, Dr. Rizwan Beg
Date: 2015-02-12 00:00:00
Tasks: Classification, General Classification

Due to the rapid growth in technology employed by the spammers, there is a
need of classifiers that are more efficient, generic and highly adaptive.
Neural Network based technologies have high ability of adaption as well as
generalization. As per our knowledge, very little work has been done in this
field using neural network. We present this paper to fill this gap. This paper
evaluates performance of three supervised learning algorithms of artificial
neural network by creating classifiers for the complex problem of latest web
spam pattern classification. These algorithms are Conjugate Gradient algorithm,
Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique
Authors: Md. Hanif Seddiqui, Rudra Pratap Deb Nath, Masaki Aono
Date: 2015-02-12 00:00:00

The proliferation of heterogeneous data sources of semantic knowledge base
intensifies the need of an automatic instance matching technique. However, the
efficiency of instance matching is often influenced by the weight of a property
associated to instances. Automatic weight generation is a non-trivial, however
an important task in instance matching technique. Therefore, identifying an
appropriate metric for generating weight for a property automatically is
nevertheless a formidable task. In this paper, we investigate an approach of
generating weights automatically by considering hypotheses: (1) the weight of a
property is directly proportional to the ratio of the number of its distinct
values to the number of instances contain the property, and (2) the weight is
also proportional to the ratio of the number of distinct values of a property
to the number of instances in a training dataset. The basic intuition behind
the use of our approach is the classical theory of information content that
infrequent words are more informative than frequent ones. Our mathematical
model derives a metric for generating property weights automatically, which is
applied in instance matching system to produce re-conciliated instances
efficiently. Our experiments and evaluations show the effectiveness of our
proposed metric of automatic weight generation for properties in an instance
matching technique.
Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review
Authors: Selma Dilek, Hüseyin Çakır, Mustafa Aydın
Date: 2015-02-12 00:00:00

With the advances in information technology (IT) criminals are using
cyberspace to commit numerous cyber crimes. Cyber infrastructures are highly
vulnerable to intrusions and other threats. Physical devices and human
intervention are not sufficient for monitoring and protection of these
infrastructures; hence, there is a need for more sophisticated cyber defense
systems that need to be flexible, adaptable and robust, and able to detect a
wide variety of threats and make intelligent real-time decisions. Numerous
bio-inspired computing methods of Artificial Intelligence have been
increasingly playing an important role in cyber crime detection and prevention.
The purpose of this study is to present advances made so far in the field of
applying AI techniques for combating cyber crimes, to demonstrate how these
techniques can be an effective tool for detection and prevention of cyber
attacks, as well as to give the scope for future work.
Convergence of gradient based pre-training in Denoising autoencoders
Authors: Vamsi K. Ithapu, Sathya Ravi, Vikas Singh
Date: 2015-02-12 00:00:00
Tasks: Denoising, Unsupervised Pre-training

The success of deep architectures is at least in part attributed to the
layer-by-layer unsupervised pre-training that initializes the network. Various
papers have reported extensive empirical analysis focusing on the design and
implementation of good pre-training procedures. However, an understanding
pertaining to the consistency of parameter estimates, the convergence of
learning procedures and the sample size estimates is still unavailable in the
literature. In this work, we study pre-training in classical and distributed
denoising autoencoders with these goals in mind. We show that the gradient
converges at the rate of $\frac{1}{\sqrt{N}}$ and has a sub-linear dependence
on the size of the autoencoder network. In a distributed setting where disjoint
sections of the whole network are pre-trained synchronously, we show that the
convergence improves by at least $\tau^{3/4}$, where $\tau$ corresponds to the
size of the sections. We provide a broad set of experiments to empirically
evaluate the suggested behavior.
Speeding up Permutation Testing in Neuroimaging
Authors: Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh
Date: 2015-02-12 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Matrix Completion, Two-sample testing

Multiple hypothesis testing is a significant problem in nearly all
neuroimaging studies. In order to correct for this phenomena, we require a
reliable estimate of the Family-Wise Error Rate (FWER). The well known
Bonferroni correction method, while simple to implement, is quite conservative,
and can substantially under-power a study because it ignores dependencies
between test statistics. Permutation testing, on the other hand, is an exact,
non-parametric method of estimating the FWER for a given $\alpha$-threshold,
but for acceptably low thresholds the computational burden can be prohibitive.
In this paper, we show that permutation testing in fact amounts to populating
the columns of a very large matrix ${\bf P}$. By analyzing the spectrum of this
matrix, under certain conditions, we see that ${\bf P}$ has a low-rank plus a
low-variance residual decomposition which makes it suitable for highly
sub--sampled --- on the order of $0.5\%$ --- matrix completion methods. Based
on this observation, we propose a novel permutation testing methodology which
offers a large speedup, without sacrificing the fidelity of the estimated FWER.
Our evaluations on four different neuroimaging datasets show that a
computational speedup factor of roughly $50\times$ can be achieved while
recovering the FWER distribution up to very high accuracy. Further, we show
that the estimated $\alpha$-threshold is also recovered faithfully, and is
stable.
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
Authors: Dornoosh Zonoobi, Shahrooz Faghih Roohi, Ashraf A. Kassim
Date: 2014-08-25 00:00:00
Tasks: Dictionary Learning

In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods.
Multispectral Palmprint Recognition Using Textural Features
Authors: Shervin Minaee, Amirali Abdolrashidi
Date: 2014-08-28 00:00:00

In order to utilize identification to the best extent, we need robust and
fast algorithms and systems to process the data. Having palmprint as a reliable
and unique characteristic of every person, we extract and use its features
based on its geometry, lines and angles. There are countless ways to define
measures for the recognition task. To analyze a new point of view, we extracted
textural features and used them for palmprint recognition. Co-occurrence matrix
can be used for textural feature extraction. As classifiers, we have used the
minimum distance classifier (MDC) and the weighted majority voting system
(WMV). The proposed method is tested on a well-known multispectral palmprint
dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most
scenarios which outperforms all previous works in multispectral palmprint
recognition.
Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling
Authors: Lee-Kang Liu, Stanley H. Chan, Truong Q. Nguyen
Date: 2014-07-14 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

The rapid development of 3D technology and computer vision applications have
motivated a thrust of methodologies for depth acquisition and estimation.
However, most existing hardware and software methods have limited performance
due to poor depth precision, low resolution and high computational cost. In
this paper, we present a computationally efficient method to recover dense
depth maps from sparse measurements. We make three contributions. First, we
provide empirical evidence that depth maps can be encoded much more sparsely
than natural images by using common dictionaries such as wavelets and
contourlets. We also show that a combined wavelet-contourlet dictionary
achieves better performance than using either dictionary alone. Second, we
propose an alternating direction method of multipliers (ADMM) to achieve fast
reconstruction. A multi-scale warm start procedure is proposed to speed up the
convergence. Third, we propose a two-stage randomized sampling scheme to
optimally choose the sampling locations, thus maximizing the reconstruction
performance for any given sampling budget. Experimental results show that the
proposed method produces high quality dense depth estimates, and is robust to
noisy measurements. Applications to real data in stereo matching are
demonstrated.
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices
Authors: Florian Yger, Masashi Sugiyama
Date: 2015-02-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Metric Learning

Metric learning has been shown to be highly effective to improve the
performance of nearest neighbor classification. In this paper, we address the
problem of metric learning for Symmetric Positive Definite (SPD) matrices such
as covariance matrices, which arise in many real-world applications. Naively
using standard Mahalanobis metric learning methods under the Euclidean geometry
for SPD matrices is not appropriate, because the difference of SPD matrices can
be a non-SPD matrix and thus the obtained solution can be uninterpretable. To
cope with this problem, we propose to use a properly parameterized LogEuclidean
distance and optimize the metric with respect to kernel-target alignment, which
is a supervised criterion for kernel learning. Then the resulting non-trivial
optimization problem is solved by utilizing the Riemannian geometry. Finally,
we experimentally demonstrate the usefulness of our LogEuclidean metric
learning algorithm on real-world classification tasks for EEG signals and
texture patches.
Spectral Sparsification of Random-Walk Matrix Polynomials
Authors: Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng
Date: 2015-02-12 00:00:00

We consider a fundamental algorithmic question in spectral graph theory:
Compute a spectral sparsifier of random-walk matrix-polynomial
$$L_\alpha(G)=D-\sum_{r=1}^d\alpha_rD(D^{-1}A)^r$$ where $A$ is the adjacency
matrix of a weighted, undirected graph, $D$ is the diagonal matrix of weighted
degrees, and $\alpha=(\alpha_1...\alpha_d)$ are nonnegative coefficients with
$\sum_{r=1}^d\alpha_r=1$. Recall that $D^{-1}A$ is the transition matrix of
random walks on the graph. The sparsification of $L_\alpha(G)$ appears to be
algorithmically challenging as the matrix power $(D^{-1}A)^r$ is defined by all
paths of length $r$, whose precise calculation would be prohibitively
expensive.
  In this paper, we develop the first nearly linear time algorithm for this
sparsification problem: For any $G$ with $n$ vertices and $m$ edges, $d$
coefficients $\alpha$, and $\epsilon > 0$, our algorithm runs in time
$O(d^2m\log^2n/\epsilon^{2})$ to construct a Laplacian matrix
$\tilde{L}=D-\tilde{A}$ with $O(n\log n/\epsilon^{2})$ non-zeros such that
$\tilde{L}\approx_{\epsilon}L_\alpha(G)$.
  Matrix polynomials arise in mathematical analysis of matrix functions as well
as numerical solutions of matrix equations. Our work is particularly motivated
by the algorithmic problems for speeding up the classic Newton's method in
applications such as computing the inverse square-root of the precision matrix
of a Gaussian random field, as well as computing the $q$th-root transition (for
$q\geq1$) in a time-reversible Markov model. The key algorithmic step for both
applications is the construction of a spectral sparsifier of a constant degree
random-walk matrix-polynomials introduced by Newton's method. Our algorithm can
also be used to build efficient data structures for effective resistances for
multi-step time-reversible Markov models, and we anticipate that it could be
useful for other tasks in network analysis.
How to show a probabilistic model is better
Authors: Mithun Chakraborty, Sanmay Das, Allen Lavoie
Date: 2015-02-11 00:00:00
Tasks: BIG-bench Machine Learning, model

We present a simple theoretical framework, and corresponding practical
procedures, for comparing probabilistic models on real data in a traditional
machine learning setting. This framework is based on the theory of proper
scoring rules, but requires only basic algebra and probability theory to
understand and verify. The theoretical concepts presented are well-studied,
primarily in the statistics literature. The goal of this paper is to advocate
their wider adoption for performance evaluation in empirical machine learning.
On Learning from Label Proportions
Authors: Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang
Date: 2014-02-24 00:00:00
Tasks: Marketing

Learning from Label Proportions (LLP) is a learning setting, where the
training data is provided in groups, or "bags", and only the proportion of each
class in each bag is known. The task is to learn a model to predict the class
labels of the individual instances. LLP has broad applications in political
science, marketing, healthcare, and computer vision. This work answers the
fundamental question, when and why LLP is possible, by introducing a general
framework, Empirical Proportion Risk Minimization (EPRM). EPRM learns an
instance label classifier to match the given label proportions on the training
data. Our result is based on a two-step analysis. First, we provide a VC bound
on the generalization error of the bag proportions. We show that the bag sample
complexity is only mildly sensitive to the bag size. Second, we show that under
some mild assumptions, good bag proportion prediction guarantees good instance
label prediction. The results together provide a formal guarantee that the
individual labels can indeed be learned in the LLP setting. We discuss
applications of the analysis, including justification of LLP algorithms,
learning with population proportions, and a paradigm for learning algorithms
with privacy guarantees. We also demonstrate the feasibility of LLP based on a
case study in real-world setting: predicting income based on census data.
Dependent Matérn Processes for Multivariate Time Series
Authors: Alexander Vandenberg-Rodes, Babak Shahbaba
Date: 2015-02-11 00:00:00
Tasks: Computational Efficiency, Time Series, Time Series Analysis

For the challenging task of modeling multivariate time series, we propose a
new class of models that use dependent Mat\'ern processes to capture the
underlying structure of data, explain their interdependencies, and predict
their unknown values. Although similar models have been proposed in the
econometric, statistics, and machine learning literature, our approach has
several advantages that distinguish it from existing methods: 1) it is flexible
to provide high prediction accuracy, yet its complexity is controlled to avoid
overfitting; 2) its interpretability separates it from black-box methods; 3)
finally, its computational efficiency makes it scalable for high-dimensional
time series. In this paper, we use several simulated and real data sets to
illustrate these advantages. We will also briefly discuss some extensions of
our model.
Variable and Fixed Interval Exponential Smoothing
Authors: Javier R. Movellan
Date: 2015-02-11 00:00:00
Tasks: Time Series, Time Series Analysis

Exponential smoothers are a simple and memory efficient way to compute
running averages of time series. Here we define and describe practical
properties of exponential smoothers for signals observed at constant and
variable intervals.