pwc-archive/papers-with-abstracts huggingface dataset:
Integrative analysis of gene expression and phenotype data
Authors: Min Xu
Date: 2015-06-29 00:00:00
Tasks: Experimental Design

The linking genotype to phenotype is the fundamental aim of modern genetics.
We focus on study of links between gene expression data and phenotype data
through integrative analysis. We propose three approaches.
  1) The inherent complexity of phenotypes makes high-throughput phenotype
profiling a very difficult and laborious process. We propose a method of
automated multi-dimensional profiling which uses gene expression similarity.
Large-scale analysis show that our method can provide robust profiling that
reveals different phenotypic aspects of samples. This profiling technique is
also capable of interpolation and extrapolation beyond the phenotype
information given in training data. It can be used in many applications,
including facilitating experimental design and detecting confounding factors.
  2) Phenotype association analysis problems are complicated by small sample
size and high dimensionality. Consequently, phenotype-associated gene subsets
obtained from training data are very sensitive to selection of training
samples, and the constructed sample phenotype classifiers tend to have poor
generalization properties. To eliminate these obstacles, we propose a novel
approach that generates sequences of increasingly discriminative gene cluster
combinations. Our experiments on both simulated and real datasets show robust
and accurate classification performance.
  3) Many complex phenotypes, such as cancer, are the product of not only gene
expression, but also gene interaction. We propose an integrative approach to
find gene network modules that activate under different phenotype conditions.
Using our method, we discovered cancer subtype-specific network modules, as
well as the ways in which these modules coordinate. In particular, we detected
a breast-cancer specific tumor suppressor network module with a hub gene,
PDGFRL, which may play an important role in this module.
Compressed Sensing of Multi-Channel EEG Signals: The Simultaneous Cosparsity and Low Rank Optimization
Authors: Yipeng Liu, Maarten De Vos, Sabine Van Huffel
Date: 2015-06-29 00:00:00
Tasks: compressed sensing, EEG, Electroencephalogram (EEG)

Goal: This paper deals with the problems that some EEG signals have no good
sparse representation and single channel processing is not computationally
efficient in compressed sensing of multi-channel EEG signals. Methods: An
optimization model with L0 norm and Schatten-0 norm is proposed to enforce
cosparsity and low rank structures in the reconstructed multi-channel EEG
signals. Both convex relaxation and global consensus optimization with
alternating direction method of multipliers are used to compute the
optimization model. Results: The performance of multi-channel EEG signal
reconstruction is improved in term of both accuracy and computational
complexity. Conclusion: The proposed method is a better candidate than previous
sparse signal recovery methods for compressed sensing of EEG signals.
Significance: The proposed method enables successful compressed sensing of EEG
signals even when the signals have no good sparse representation. Using
compressed sensing would much reduce the power consumption of wireless EEG
system.
The Multi-Strand Graph for a PTZ Tracker
Authors: Shachaf Melman, Yael Moses, Gérard Medioni, Yinghao Cai
Date: 2015-06-29 00:00:00
Tasks: Scheduling

High-resolution images can be used to resolve matching ambiguities between
trajectory fragments (tracklets), which is one of the main challenges in
multiple target tracking. A PTZ camera, which can pan, tilt and zoom, is a
powerful and efficient tool that offers both close-up views and wide area
coverage on demand. The wide-area view makes it possible to track many targets
while the close-up view allows individuals to be identified from
high-resolution images of their faces. A central component of a PTZ tracking
system is a scheduling algorithm that determines which target to zoom in on.
  In this paper we study this scheduling problem from a theoretical
perspective, where the high resolution images are also used for tracklet
matching. We propose a novel data structure, the Multi-Strand Tracking Graph
(MSG), which represents the set of tracklets computed by a tracker and the
possible associations between them. The MSG allows efficient scheduling as well
as resolving -- directly or by elimination -- matching ambiguities between
tracklets. The main feature of the MSG is the auxiliary data saved in each
vertex, which allows efficient computation while avoiding time-consuming graph
traversal. Synthetic data simulations are used to evaluate our scheduling
algorithm and to demonstrate its superiority over a na\"ive one.
Nonparametric Estimation of Band-limited Probability Density Functions
Authors: Rahul Agarwal, Zhe Chen, Sridevi V. Sarma
Date: 2015-03-20 00:00:00
Tasks: Density Estimation

In this paper, a nonparametric maximum likelihood (ML) estimator for
band-limited (BL) probability density functions (pdfs) is proposed. The BLML
estimator is consistent and computationally efficient. To compute the BLML
estimator, three approximate algorithms are presented: a binary quadratic
programming (BQP) algorithm for medium scale problems, a Trivial algorithm for
large-scale problems that yields a consistent estimate if the underlying pdf is
strictly positive and BL, and a fast implementation of the Trivial algorithm
that exploits the band-limited assumption and the Nyquist sampling theorem
("BLMLQuick"). All three BLML estimators outperform kernel density estimation
(KDE) algorithms (adaptive and higher order KDEs) with respect to the mean
integrated squared error for data generated from both BL and infinite-band
pdfs. Further, the BLMLQuick estimate is remarkably faster than the KD
algorithms. Finally, the BLML method is applied to estimate the conditional
intensity function of a neuronal spike train (point process) recorded from a
rat's entorhinal cortex grid cell, for which it outperforms state-of-the-art
estimators used in neuroscience.
A Bayesian Model for Generative Transition-based Dependency Parsing
Authors: Jan Buys, Phil Blunsom
Date: 2015-06-13 00:00:00
Proceeding: WS 2015 8
Tasks: Dependency Parsing, Language Modeling, Language Modelling, POS, Text Generation, Transition-Based Dependency Parsing

We propose a simple, scalable, fully generative model for transition-based
dependency parsing with high accuracy. The model, parameterized by Hierarchical
Pitman-Yor Processes, overcomes the limitations of previous generative models
by allowing fast and accurate inference. We propose an efficient decoding
algorithm based on particle filtering that can adapt the beam size to the
uncertainty in the model while jointly predicting POS tags and parse trees. The
UAS of the parser is on par with that of a greedy discriminative baseline. As a
language model, it obtains better perplexity than a n-gram model by performing
semi-supervised learning over a large unlabelled corpus. We show that the model
is able to generate locally and syntactically coherent sentences, opening the
door to further applications in language generation.
Oriented Edge Forests for Boundary Detection
Authors: Sam Hallman, Charless C. Fowlkes
Date: 2014-12-13 00:00:00
Proceeding: CVPR 2015 6
Tasks: Boundary Detection, Clustering

We present a simple, efficient model for learning boundary detection based on
a random forest classifier. Our approach combines (1) efficient clustering of
training examples based on simple partitioning of the space of local edge
orientations and (2) scale-dependent calibration of individual tree output
probabilities prior to multiscale combination. The resulting model outperforms
published results on the challenging BSDS500 boundary detection benchmark.
Further, on large datasets our model requires substantially less memory for
training and speeds up training time by a factor of 10 over the structured
forest model.
Deep-Plant: Plant Identification with convolutional neural networks
Authors: Sue Han Lee, Chee Seng Chan, Paul Wilkin, Paolo Remagnino
Date: 2015-06-28 00:00:00

This paper studies convolutional neural networks (CNN) to learn unsupervised
feature representations for 44 different plant species, collected at the Royal
Botanic Gardens, Kew, England. To gain intuition on the chosen features from
the CNN model (opposed to a 'black box' solution), a visualisation technique
based on the deconvolutional networks (DN) is utilized. It is found that
venations of different order have been chosen to uniquely represent each of the
plant species. Experimental results using these CNN features with different
classifiers show consistency and superiority compared to the state-of-the art
solutions which rely on hand-crafted features.
Topic2Vec: Learning Distributed Representations of Topics
Authors: Li-Qiang Niu, Xin-yu Dai
Date: 2015-06-28 00:00:00

Latent Dirichlet Allocation (LDA) mining thematic structure of documents
plays an important role in nature language processing and machine learning
areas. However, the probability distribution from LDA only describes the
statistical relationship of occurrences in the corpus and usually in practice,
probability is not the best choice for feature representations. Recently,
embedding methods have been proposed to represent words and documents by
learning essential concepts and representations, such as Word2Vec and Doc2Vec.
The embedded representations have shown more effectiveness than LDA-style
representations in many tasks. In this paper, we propose the Topic2Vec approach
which can learn topic representations in the same semantic vector space with
words, as an alternative to probability. The experimental results show that
Topic2Vec achieves interesting and meaningful results.
The fundamental nature of the log loss function
Authors: Vladimir Vovk
Date: 2015-02-22 00:00:00
Tasks: Prediction

The standard loss functions used in the literature on probabilistic
prediction are the log loss function, the Brier loss function, and the
spherical loss function; however, any computable proper loss function can be
used for comparison of prediction algorithms. This note shows that the log loss
function is most selective in that any prediction algorithm that is optimal for
a given data sequence (in the sense of the algorithmic theory of randomness)
under the log loss function will be optimal under any computable proper mixable
loss function; on the other hand, there is a data sequence and a prediction
algorithm that is optimal for that sequence under either of the two other
standard loss functions but not under the log loss function.
Non-Normal Mixtures of Experts
Authors: Faicel Chamroukhi
Date: 2015-06-22 00:00:00
Tasks: Clustering, Mixture-of-Experts, regression

Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in
data for regression, classification and clustering. For continuous data which
we consider here in the context of regression and cluster analysis, MoE usually
use normal experts, that is, expert components following the Gaussian
distribution. However, for a set of data containing a group or groups of
observations with asymmetric behavior, heavy tails or atypical observations,
the use of normal experts may be unsuitable and can unduly affect the fit of
the MoE model. In this paper, we introduce new non-normal mixture of experts
(NNMoE) which can deal with these issues regarding possibly skewed,
heavy-tailed data and with outliers. The proposed models are the skew-normal
MoE and the robust $t$ MoE and skew $t$ MoE, respectively named SNMoE, TMoE and
STMoE. We develop dedicated expectation-maximization (EM) and expectation
conditional maximization (ECM) algorithms to estimate the parameters of the
proposed models by monotonically maximizing the observed data log-likelihood.
We describe how the presented models can be used in prediction and in
model-based clustering of regression data. Numerical experiments carried out on
simulated data show the effectiveness and the robustness of the proposed models
in terms modeling non-linear regression functions as well as in model-based
clustering. Then, to show their usefulness for practical applications, the
proposed models are applied to the real-world data of tone perception for
musical data analysis, and the one of temperature anomalies for the analysis of
climate change data.
Log-Optimal Portfolio Selection Using the Blackwell Approachability Theorem
Authors: Vladimir V'yugin
Date: 2014-10-22 00:00:00

We present a method for constructing the log-optimal portfolio using the
well-calibrated forecasts of market values. Dawid's notion of calibration and
the Blackwell approachability theorem are used for computing well-calibrated
forecasts. We select a portfolio using this "artificial" probability
distribution of market values. Our portfolio performs asymptotically at least
as well as any stationary portfolio that redistributes the investment at each
round using a continuous function of side information. Unlike in classical
mathematical finance theory, no stochastic assumptions are made about market
values.
Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel
Date: 2014-09-18 00:00:00
Tasks: Ensemble Learning, object-detection, Object Detection, Pedestrian Detection

Many typical applications of object detection operate within a prescribed
false-positive range. In this situation the performance of a detector should be
assessed on the basis of the area under the ROC curve over that range, rather
than over the full curve, as the performance outside the range is irrelevant.
This measure is labelled as the partial area under the ROC curve (pAUC). We
propose a novel ensemble learning method which achieves a maximal detection
rate at a user-defined range of false positive rates by directly optimizing the
partial AUC using structured learning.
  In order to achieve a high object detection performance, we propose a new
approach to extract low-level visual features based on spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. Experimental results on both synthetic
and real-world data sets demonstrate the effectiveness of our approach, and we
show that it is possible to train state-of-the-art pedestrian detectors using
the proposed structured ensemble learning method with spatially pooled
features. The result is the current best reported performance on the
Caltech-USA pedestrian detection dataset.
Overlapping Communities Detection via Measure Space Embedding
Authors: Mark Kozdoba, Shie Mannor
Date: 2015-04-26 00:00:00
Tasks: Community Detection, Stochastic Block Model

We present a new algorithm for community detection. The algorithm uses random
walks to embed the graph in a space of measures, after which a modification of
$k$-means in that space is applied. The algorithm is therefore fast and easily
parallelizable. We evaluate the algorithm on standard random graph benchmarks,
including some overlapping community benchmarks, and find its performance to be
better or at least as good as previously known algorithms. We also prove a
linear time (in number of edges) guarantee for the algorithm on a
$p,q$-stochastic block model with $p \geq c\cdot N^{-\frac{1}{2} + \epsilon}$
and $p-q \geq c' \sqrt{p N^{-\frac{1}{2} + \epsilon} \log N}$.
Simultaneously Solving Computational Problems Using an Artificial Chemical Reactor
Authors: Jaderick P. Pabico
Date: 2015-06-28 00:00:00

This paper is centered on using chemical reaction as a computational metaphor
for simultaneously solving problems. An artificial chemical reactor that can
simultaneously solve instances of three unrelated problems was created. The
reactor is a distributed stochastic algorithm that simulates a chemical
universe wherein the molecular species are being represented either by a human
genomic contig panel, a Hamiltonian cycle, or an aircraft landing schedule. The
chemical universe is governed by reactions that can alter genomic sequences,
re-order Hamiltonian cycles, or reschedule an aircraft landing program.
Molecular masses were considered as measures of goodness of solutions, and
represented radiation hybrid (RH) vector similarities, costs of Hamiltonian
cycles, and penalty costs for landing an aircraft before and after target
landing times. This method, tested by solving in tandem with deterministic
algorithms, has been shown to find quality solutions in finding the minima RH
vector similarities of genomic data, minima costs in Hamiltonian cycles of the
traveling salesman, and minima costs for landing aircrafts before or after
target landing times.
Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
Authors: Lantian Li, Yiye Lin, Zhiyong Zhang, Dong Wang
Date: 2015-06-28 00:00:00
Tasks: Deep Learning, Dynamic Time Warping, Speaker Recognition

A deep learning approach has been proposed recently to derive speaker
identifies (d-vector) by a deep neural network (DNN). This approach has been
applied to text-dependent speaker recognition tasks and shows reasonable
performance gains when combined with the conventional i-vector approach.
Although promising, the existing d-vector implementation still can not compete
with the i-vector baseline. This paper presents two improvements for the deep
learning approach: a phonedependent DNN structure to normalize phone variation,
and a new scoring approach based on dynamic time warping (DTW). Experiments on
a text-dependent speaker recognition task demonstrated that the proposed
methods can provide considerable performance improvement over the existing
d-vector implementation.
Occam's Gates
Authors: Jonathan Raiman, Szymon Sidor
Date: 2015-06-27 00:00:00
Tasks: General Classification, Question Answering, Sentiment Analysis

We present a complimentary objective for training recurrent neural networks
(RNN) with gating units that helps with regularization and interpretability of
the trained model. Attention-based RNN models have shown success in many
difficult sequence to sequence classification problems with long and short term
dependencies, however these models are prone to overfitting. In this paper, we
describe how to regularize these models through an L1 penalty on the activation
of the gating units, and show that this technique reduces overfitting on a
variety of tasks while also providing to us a human-interpretable visualization
of the inputs used by the network. These tasks include sentiment analysis,
paraphrase recognition, and question answering.
Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation
Authors: Noam Shazeer, Joris Pelemans, Ciprian Chelba
Date: 2014-12-03 00:00:00
Tasks: Language Modeling, Language Modelling

We present a novel family of language model (LM) estimation techniques named
Sparse Non-negative Matrix (SNM) estimation. A first set of experiments
empirically evaluating it on the One Billion Word Benchmark shows that SNM
$n$-gram LMs perform almost as well as the well-established Kneser-Ney (KN)
models. When using skip-gram features the models are able to match the
state-of-the-art recurrent neural network (RNN) LMs; combining the two modeling
techniques yields the best known result on the benchmark. The computational
advantages of SNM over both maximum entropy and RNN LM estimation are probably
its main strength, promising an approach that has the same flexibility in
combining arbitrary features effectively and yet should scale to very large
amounts of data as gracefully as $n$-gram LMs do.
A geometric alternative to Nesterov's accelerated gradient descent
Authors: Sébastien Bubeck, Yin Tat Lee, Mohit Singh
Date: 2015-06-26 00:00:00

We propose a new method for unconstrained optimization of a smooth and
strongly convex function, which attains the optimal rate of convergence of
Nesterov's accelerated gradient descent. The new algorithm has a simple
geometric interpretation, loosely inspired by the ellipsoid method. We provide
some numerical evidence that the new method can be superior to Nesterov's
accelerated gradient descent.
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Authors: Amar Shah, David A. Knowles, Zoubin Ghahramani
Date: 2015-06-26 00:00:00
Tasks: Topic Models, Variational Inference

Stochastic variational inference (SVI) is emerging as the most promising
candidate for scaling inference in Bayesian probabilistic models to large
datasets. However, the performance of these methods has been assessed primarily
in the context of Bayesian topic models, particularly latent Dirichlet
allocation (LDA). Deriving several new algorithms, and using synthetic, image
and genomic datasets, we investigate whether the understanding gleaned from LDA
applies in the setting of sparse latent factor models, specifically beta
process factor analysis (BPFA). We demonstrate that the big picture is
consistent: using Gibbs sampling within SVI to maintain certain posterior
dependencies is extremely effective. However, we find that different posterior
dependencies are important in BPFA relative to LDA. Particularly,
approximations able to model intra-local variable dependence perform best.
Online Matrix Completion and Online Robust PCA
Authors: Brian Lois, Namrata Vaswani
Date: 2015-03-11 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

This work studies two interrelated problems - online robust PCA (RPCA) and
online low-rank matrix completion (MC). In recent work by Cand\`{e}s et al.,
RPCA has been defined as a problem of separating a low-rank matrix (true data),
$L:=[\ell_1, \ell_2, \dots \ell_{t}, \dots , \ell_{t_{\max}}]$ and a sparse
matrix (outliers), $S:=[x_1, x_2, \dots x_{t}, \dots, x_{t_{\max}}]$ from their
sum, $M:=L+S$. Our work uses this definition of RPCA. An important application
where both these problems occur is in video analytics in trying to separate
sparse foregrounds (e.g., moving objects) and slowly changing backgrounds.
  While there has been a large amount of recent work on both developing and
analyzing batch RPCA and batch MC algorithms, the online problem is largely
open. In this work, we develop a practical modification of our recently
proposed algorithm to solve both the online RPCA and online MC problems. The
main contribution of this work is that we obtain correctness results for the
proposed algorithms under mild assumptions. The assumptions that we need are:
(a) a good estimate of the initial subspace is available (easy to obtain using
a short sequence of background-only frames in video surveillance); (b) the
$\ell_t$'s obey a `slow subspace change' assumption; (c) the basis vectors for
the subspace from which $\ell_t$ is generated are dense (non-sparse); (d) the
support of $x_t$ changes by at least a certain amount at least every so often;
and (e) algorithm parameters are appropriately set