pwc-archive/papers-with-abstracts huggingface dataset:
Kernelized Multiview Projection
Authors: Mengyang Yu, Li Liu, Ling Shao
Date: 2015-08-03 00:00:00

Conventional vision algorithms adopt a single type of feature or a simple
concatenation of multiple features, which is always represented in a
high-dimensional space. In this paper, we propose a novel unsupervised spectral
embedding algorithm called Kernelized Multiview Projection (KMP) to better fuse
and embed different feature representations. Computing the kernel matrices from
different features/views, KMP can encode them with the corresponding weights to
achieve a low-dimensional and semantically meaningful subspace where the
distribution of each view is sufficiently smooth and discriminative. More
crucially, KMP is linear for the reproducing kernel Hilbert space (RKHS) and
solves the out-of-sample problem, which allows it to be competent for various
practical applications. Extensive experiments on three popular image datasets
demonstrate the effectiveness of our multiview embedding algorithm.
Parameter Database : Data-centric Synchronization for Scalable Machine Learning
Authors: Naman Goel, Divyakant Agrawal, Sanjay Chawla, Ahmed Elmagarmid
Date: 2015-08-04 00:00:00
Tasks: BIG-bench Machine Learning

We propose a new data-centric synchronization framework for carrying out of
machine learning (ML) tasks in a distributed environment. Our framework
exploits the iterative nature of ML algorithms and relaxes the application
agnostic bulk synchronization parallel (BSP) paradigm that has previously been
used for distributed machine learning. Data-centric synchronization complements
function-centric synchronization based on using stale updates to increase the
throughput of distributed ML computations. Experiments to validate our
framework suggest that we can attain substantial improvement over BSP while
guaranteeing sequential correctness of ML tasks.
Hierarchical Saliency Detection on Extended CSSD
Authors: Jianping Shi, Qiong Yan, Li Xu, Jiaya Jia
Date: 2014-08-11 00:00:00
Tasks: Saliency Detection

Complex structures commonly exist in natural images. When an image contains
small-scale high-contrast patterns either in the background or foreground,
saliency detection could be adversely affected, resulting erroneous and
non-uniform saliency assignment. The issue forms a fundamental challenge for
prior methods. We tackle it from a scale point of view and propose a
multi-layer approach to analyze saliency cues. Different from varying patch
sizes or downsizing images, we measure region-based scales. The final saliency
values are inferred optimally combining all the saliency cues in different
scales using hierarchical inference. Through our inference model, single-scale
information is selected to obtain a saliency map. Our method improves detection
quality on many images that cannot be handled well traditionally. We also
construct an extended Complex Scene Saliency Dataset (ECSSD) to include complex
but general natural images.
Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images
Authors: Chen Sun, Sanketh Shetty, Rahul Sukthankar, Ram Nevatia
Date: 2015-04-04 00:00:00
Tasks: Action Localization, Action Recognition, Temporal Action Localization, Temporal Localization

We address the problem of fine-grained action localization from temporally
untrimmed web videos. We assume that only weak video-level annotations are
available for training. The goal is to use these weak labels to identify
temporal segments corresponding to the actions, and learn models that
generalize to unconstrained web videos. We find that web images queried by
action names serve as well-localized highlights for many actions, but are
noisily labeled. To solve this problem, we propose a simple yet effective
method that takes weak video labels and noisy image labels as input, and
generates localized action frames as output. This is achieved by cross-domain
transfer between video frames and web images, using pre-trained deep
convolutional neural networks. We then use the localized action frames to train
action recognition models with long short-term memory networks. We collect a
fine-grained sports action data set FGA-240 of more than 130,000 YouTube
videos. It has 240 fine-grained actions under 85 sports activities. Convincing
results are shown on the FGA-240 data set, as well as the THUMOS 2014
localization data set with untrimmed training videos.
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing
Authors: Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman
Date: 2015-08-04 00:00:00
Tasks: Two-sample testing

Nonparametric two sample testing is a decision theoretic problem that
involves identifying differences between two random variables without making
parametric assumptions about their underlying distributions. We refer to the
most common settings as mean difference alternatives (MDA), for testing
differences only in first moments, and general difference alternatives (GDA),
which is about testing for any difference in distributions. A large number of
test statistics have been proposed for both these settings. This paper connects
three classes of statistics - high dimensional variants of Hotelling's t-test,
statistics based on Reproducing Kernel Hilbert Spaces, and energy statistics
based on pairwise distances. We ask the question: how much statistical power do
popular kernel and distance based tests for GDA have when the unknown
distributions differ in their means, compared to specialized tests for MDA?
  We formally characterize the power of popular tests for GDA like the Maximum
Mean Discrepancy with the Gaussian kernel (gMMD) and bandwidth-dependent
variants of the Energy Distance with the Euclidean norm (eED) in the
high-dimensional MDA regime. Some practically important properties include (a)
eED and gMMD have asymptotically equal power; furthermore they enjoy a free
lunch because, while they are additionally consistent for GDA, they also have
the same power as specialized high-dimensional t-test variants for MDA. All
these tests are asymptotically optimal (including matching constants) under MDA
for spherical covariances, according to simple lower bounds, (b) The power of
gMMD is independent of the kernel bandwidth, as long as it is larger than the
choice made by the median heuristic, (c) There is a clear and smooth
computation-statistics tradeoff for linear-time, subquadratic-time and
quadratic-time versions of these tests, with more computation resulting in
higher power.
Particle Swarm Optimization for Weighted Sum Rate Maximization in MIMO Broadcast Channels
Authors: Tung T. Vu, Ha Hoang Kha, Trung Q. Duong, Nguyen-Son Vo
Date: 2015-08-04 00:00:00
Tasks: Stochastic Optimization

In this paper, we investigate the downlink multiple-input-multipleoutput
(MIMO) broadcast channels in which a base transceiver station (BTS) broadcasts
multiple data streams to K MIMO mobile stations (MSs) simultaneously. In order
to maximize the weighted sum-rate (WSR) of the system subject to the
transmitted power constraint, the design problem is to find the pre-coding
matrices at BTS and the decoding matrices at MSs. However, such a design
problem is typically a nonlinear and nonconvex optimization and, thus, it is
quite hard to obtain the analytical solutions. To tackle with the mathematical
difficulties, we propose an efficient stochastic optimization algorithm to
optimize the transceiver matrices. Specifically, we utilize the linear minimum
mean square error (MMSE) Wiener filters at MSs. Then, we introduce the
constrained particle swarm optimization (PSO) algorithm to jointly optimize the
precoding and decoding matrices. Numerical experiments are exhibited to
validate the effectiveness of the proposed algorithm in terms of convergence,
computational complexity and total WSR.
Bayesian mixtures of spatial spline regressions
Authors: Faicel Chamroukhi
Date: 2015-08-04 00:00:00
Tasks: Clustering, Density Estimation, Handwritten Digit Recognition, regression

This work relates the framework of model-based clustering for spatial
functional data where the data are surfaces. We first introduce a Bayesian
spatial spline regression model with mixed-effects (BSSR) for modeling spatial
function data. The BSSR model is based on Nodal basis functions for spatial
regression and accommodates both common mean behavior for the data through a
fixed-effects part, and variability inter-individuals thanks to a
random-effects part. Then, in order to model populations of spatial functional
data issued from heterogeneous groups, we integrate the BSSR model into a
mixture framework. The resulting model is a Bayesian mixture of spatial spline
regressions with mixed-effects (BMSSR) used for density estimation and
model-based surface clustering. The models, through their Bayesian formulation,
allow to integrate possible prior knowledge on the data structure and
constitute a good alternative to recent mixture of spatial spline regressions
model estimated in a maximum likelihood framework via the
expectation-maximization (EM) algorithm. The Bayesian model inference is
performed by Markov Chain Monte Carlo (MCMC) sampling. We derive two Gibbs
sampler to infer the BSSR and the BMSSR models and apply them on simulated
surfaces and a real problem of handwritten digit recognition using the MNIST
data set. The obtained results highlight the potential benefit of the proposed
Bayesian approaches for modeling surfaces possibly dispersed in particular in
clusters.
Sparse PCA via Bipartite Matchings
Authors: Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis
Date: 2015-08-04 00:00:00
Proceeding: NeurIPS 2015 12

We consider the following multi-component sparse PCA problem: given a set of
data points, we seek to extract a small number of sparse components with
disjoint supports that jointly capture the maximum possible variance. These
components can be computed one by one, repeatedly solving the single-component
problem and deflating the input data matrix, but as we show this greedy
procedure is suboptimal. We present a novel algorithm for sparse PCA that
jointly optimizes multiple disjoint components. The extracted features capture
variance that lies within a multiplicative factor arbitrarily close to 1 from
the optimal. Our algorithm is combinatorial and computes the desired components
by solving multiple instances of the bipartite maximum weight matching problem.
Its complexity grows as a low order polynomial in the ambient dimension of the
input data matrix, but exponentially in its rank. However, it can be
effectively applied on a low-dimensional sketch of the data; this allows us to
obtain polynomial-time approximation guarantees via spectral bounds. We
evaluate our algorithm on real data-sets and empirically demonstrate that in
many cases it outperforms existing, deflation-based approaches.
Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns
Authors: Rodrigo Frassetto Nogueira, Roberto de Alencar Lotufo, Rubens Campos Machado
Date: 2015-08-03 00:00:00

With the growing use of biometric authentication systems in the past years,
spoof fingerprint detection has become increasingly important. In this work, we
implement and evaluate two different feature extraction techniques for
software-based fingerprint liveness detection: Convolutional Networks with
random weights and Local Binary Patterns. Both techniques were used in
conjunction with a Support Vector Machine (SVM) classifier. Dataset
Augmentation was used to increase classifier's performance and a variety of
preprocessing operations were tested, such as frequency filtering, contrast
equalization, and region of interest filtering. The experiments were made on
the datasets used in The Liveness Detection Competition of years 2009, 2011 and
2013, which comprise almost 50,000 real and fake fingerprints' images. Our best
method achieves an overall rate of 95.2% of correctly classified samples - an
improvement of 35% in test error when compared with the best previously
published results.
A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping
Authors: Tameem Adel, Alexander Wong, Daniel Stashuk
Date: 2015-08-03 00:00:00
Tasks: Classification, General Classification, Weakly Supervised Classification, Weakly-supervised Learning

In this study, a spectral graph-theoretic grouping strategy for weakly
supervised classification is introduced, where a limited number of labelled
samples and a larger set of unlabelled samples are used to construct a larger
annotated training set composed of strongly labelled and weakly labelled
samples. The inherent relationship between the set of strongly labelled samples
and the set of unlabelled samples is established via spectral grouping, with
the unlabelled samples subsequently weakly annotated based on the strongly
labelled samples within the associated spectral groups. A number of similarity
graph models for spectral grouping, including two new similarity graph models
introduced in this study, are explored to investigate their performance in the
context of weakly supervised classification in handling different types of
data. Experimental results using benchmark datasets as well as real EMG
datasets demonstrate that the proposed approach to weakly supervised
classification can provide noticeable improvements in classification
performance, and that the proposed similarity graph models can lead to ultimate
learning results that are either better than or on a par with existing
similarity graph models in the context of spectral grouping for weakly
supervised classification.
Appropriate Causal Models and the Stability of Causation
Authors: Joseph Y. Halpern
Date: 2014-12-11 00:00:00

Causal models defined in terms of structural equations have proved to be
quite a powerful way of representing knowledge regarding causality. However, a
number of authors have given examples that seem to show that the Halpern-Pearl
(HP) definition of causality gives intuitively unreasonable answers. Here it is
shown that, for each of these examples, we can give two stories consistent with
the description in the example, such that intuitions regarding causality are
quite different for each story. By adding additional variables, we can
disambiguate the stories. Moreover, in the resulting causal models, the HP
definition of causality gives the intuitively correct answer. It is also shown
that, by adding extra variables, a modification to the original HP definition
made to deal with an example of Hopkins and Pearl may not be necessary. Given
how much can be done by adding extra variables, there might be a concern that
the notion of causality is somewhat unstable. Can adding extra variables in a
"conservative" way (i.e., maintaining all the relations between the variables
in the original model) cause the answer to the question "Is X=x a cause of Y=y"
to alternate between "yes" and "no"? It is shown that we can have such
alternation infinitely often, but if we take normality into consideration, we
cannot. Indeed, under appropriate normality assumptions. adding an extra
variable can change the answer from "yes" to "no", but after that, it cannot
cannot change back to "yes".
Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics: Evolutionary Algorithms for Bioinformatics
Authors: Ka-Chun Wong
Date: 2015-08-03 00:00:00
Tasks: Evolutionary Algorithms

Since genetic algorithm was proposed by John Holland (Holland J. H., 1975) in
the early 1970s, the study of evolutionary algorithm has emerged as a popular
research field (Civicioglu & Besdok, 2013). Researchers from various scientific
and engineering disciplines have been digging into this field, exploring the
unique power of evolutionary algorithms (Hadka & Reed, 2013). Many applications
have been successfully proposed in the past twenty years. For example,
mechanical design (Lampinen & Zelinka, 1999), electromagnetic optimization
(Rahmat-Samii & Michielssen, 1999), environmental protection (Bertini, Felice,
Moretti, & Pizzuti, 2010), finance (Larkin & Ryan, 2010), musical orchestration
(Esling, Carpentier, & Agon, 2010), pipe routing (Furuholmen, Glette, Hovin, &
Torresen, 2010), and nuclear reactor core design (Sacco, Henderson,
Rios-Coelho, Ali, & Pereira, 2009). In particular, its function optimization
capability was highlighted (Goldberg & Richardson, 1987) because of its high
adaptability to different function landscapes, to which we cannot apply
traditional optimization techniques (Wong, Leung, & Wong, 2009). Here we review
the applications of evolutionary algorithms in bioinformatics.
Unsupervised Learning in Genome Informatics
Authors: Ka-Chun Wong, Yue Li, Zhaolei Zhang
Date: 2015-08-03 00:00:00

With different genomes available, unsupervised learning algorithms are
essential in learning genome-wide biological insights. Especially, the
functional characterization of different genomes is essential for us to
understand lives. In this book chapter, we review the state-of-the-art
unsupervised learning algorithms for genome informatics from DNA to MicroRNA.
  DNA (DeoxyriboNucleic Acid) is the basic component of genomes. A significant
fraction of DNA regions (transcription factor binding sites) are bound by
proteins (transcription factors) to regulate gene expression at different
development stages in different tissues. To fully understand genetics, it is
necessary of us to apply unsupervised learning algorithms to learn and infer
those DNA regions. Here we review several unsupervised learning methods for
deciphering the genome-wide patterns of those DNA regions.
  MicroRNA (miRNA), a class of small endogenous non-coding RNA (RiboNucleic
acid) species, regulate gene expression post-transcriptionally by forming
imperfect base-pair with the target sites primarily at the 3$'$ untranslated
regions of the messenger RNAs. Since the 1993 discovery of the first miRNA
\emph{let-7} in worms, a vast amount of studies have been dedicated to
functionally characterizing the functional impacts of miRNA in a network
context to understand complex diseases such as cancer. Here we review several
representative unsupervised learning frameworks on inferring miRNA regulatory
network by exploiting the static sequence-based information pertinent to the
prior knowledge of miRNA targeting and the dynamic information of miRNA
activities implicated by the recently available large data compendia, which
interrogate genome-wide expression profiles of miRNAs and/or mRNAs across
various cell conditions.
Evolutionary Multimodal Optimization: A Short Survey
Authors: Ka-Chun Wong
Date: 2015-08-03 00:00:00
Tasks: Benchmarking, Diversity, Evolutionary Algorithms, global-optimization, Survey

Real world problems always have different multiple solutions. For instance,
optical engineers need to tune the recording parameters to get as many optimal
solutions as possible for multiple trials in the varied-line-spacing
holographic grating design problem. Unfortunately, most traditional
optimization techniques focus on solving for a single optimal solution. They
need to be applied several times; yet all solutions are not guaranteed to be
found. Thus the multimodal optimization problem was proposed. In that problem,
we are interested in not only a single optimal point, but also the others. With
strong parallel search capability, evolutionary algorithms are shown to be
particularly effective in solving this type of problem. In particular, the
evolutionary algorithms for multimodal optimization usually not only locate
multiple optima in a single run, but also preserve their population diversity
throughout a run, resulting in their global optimization ability on multimodal
functions. In addition, the techniques for multimodal optimization are borrowed
as diversity maintenance techniques to other problems. In this chapter, we
describe and review the state-of-the-arts evolutionary algorithms for
multimodal optimization in terms of methodology, benchmarking, and application.
Dependency-based Convolutional Neural Networks for Sentence Embedding
Authors: Mingbo Ma, Liang Huang, Bing Xiang, Bo-Wen Zhou
Date: 2015-07-07 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: All, Classification, General Classification, Sentence, Sentence Embedding, Sentence-Embedding

In sentence modeling and classification, convolutional neural network
approaches have recently achieved state-of-the-art results, but all such
efforts process word vectors sequentially and neglect long-distance
dependencies. To exploit both deep learning and linguistic structures, we
propose a tree-based convolutional neural network model which exploit various
long-distance relationships between words. Our model improves the sequential
baselines on all three sentiment and question classification tasks, and
achieves the highest published accuracy on TREC.
Significance of Maximum Spectral Amplitude in Sub-bands for Spectral Envelope Estimation and Its Application to Statistical Parametric Speech Synthesis
Authors: Sivanand Achanta, Anandaswarup Vadapalli, Sai Krishna R., Suryakanth V. Gangashetty
Date: 2015-08-03 00:00:00
Tasks: Speech Synthesis

In this paper we propose a technique for spectral envelope estimation using
maximum values in the sub-bands of Fourier magnitude spectrum (MSASB). Most
other methods in the literature parametrize spectral envelope in cepstral
domain such as Mel-generalized cepstrum etc. Such cepstral domain
representations, although compact, are not readily interpretable. This
difficulty is overcome by our method which parametrizes in the spectral domain
itself. In our experiments, spectral envelope estimated using MSASB method was
incorporated in the STRAIGHT vocoder. Both objective and subjective results of
analysis-by-synthesis indicate that the proposed method is comparable to
STRAIGHT. We also evaluate the effectiveness of the proposed parametrization in
a statistical parametric speech synthesis framework using deep neural networks.
Time-series modeling with undecimated fully convolutional neural networks
Authors: Roni Mittelman
Date: 2015-08-03 00:00:00
Tasks: Time Series, Time Series Analysis

We present a new convolutional neural network-based time-series model.
Typical convolutional neural network (CNN) architectures rely on the use of
max-pooling operators in between layers, which leads to reduced resolution at
the top layers. Instead, in this work we consider a fully convolutional network
(FCN) architecture that uses causal filtering operations, and allows for the
rate of the output signal to be the same as that of the input signal. We
furthermore propose an undecimated version of the FCN, which we refer to as the
undecimated fully convolutional neural network (UFCNN), and is motivated by the
undecimated wavelet transform. Our experimental results verify that using the
undecimated version of the FCN is necessary in order to allow for effective
time-series modeling. The UFCNN has several advantages compared to other
time-series models such as the recurrent neural network (RNN) and long
short-term memory (LSTM), since it does not suffer from either the vanishing or
exploding gradients problems, and is therefore easier to train. Convolution
operations can also be implemented more efficiently compared to the recursion
that is involved in RNN-based models. We evaluate the performance of our model
in a synthetic target tracking task using bearing only measurements generated
from a state-space model, a probabilistic modeling of polyphonic music
sequences problem, and a high frequency trading task using a time-series of
ask/bid quotes and their corresponding volumes. Our experimental results using
synthetic and real datasets verify the significant advantages of the UFCNN
compared to the RNN and LSTM baselines.