pwc-archive/papers-with-abstracts huggingface dataset:
Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
Authors: Wenwen Min, Juan Liu, Shihua Zhang
Date: 2016-09-21 00:00:00
Tasks: regression

Molecular profiling data (e.g., gene expression) has been used for clinical
risk prediction and biomarker discovery. However, it is necessary to integrate
other prior knowledge like biological pathways or gene interaction networks to
improve the predictive ability and biological interpretability of biomarkers.
Here, we first introduce a general regularized Logistic Regression (LR)
framework with regularized term $\lambda \|\bm{w}\|_1 +
\eta\bm{w}^T\bm{M}\bm{w}$, which can reduce to different penalties, including
Lasso, elastic net, and network-regularized terms with different $\bm{M}$. This
framework can be easily solved in a unified manner by a cyclic coordinate
descent algorithm which can avoid inverse matrix operation and accelerate the
computing speed. However, if those estimated $\bm{w}_i$ and $\bm{w}_j$ have
opposite signs, then the traditional network-regularized penalty may not
perform well. To address it, we introduce a novel network-regularized sparse LR
model with a new penalty $\lambda \|\bm{w}\|_1 + \eta|\bm{w}|^T\bm{M}|\bm{w}|$
to consider the difference between the absolute values of the coefficients. And
we develop two efficient algorithms to solve it. Finally, we test our methods
and compare them with the related ones using simulated and real data to show
their efficiency.
Learning rotation invariant convolutional filters for texture classification
Authors: Diego Marcos, Michele Volpi, Devis Tuia
Date: 2016-04-22 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Texture Classification

We present a method for learning discriminative filters using a shallow
Convolutional Neural Network (CNN). We encode rotation invariance directly in
the model by tying the weights of groups of filters to several rotated versions
of the canonical filter in the group. These filters can be used to extract
rotation invariant features well-suited for image classification. We test this
learning procedure on a texture classification benchmark, where the
orientations of the training images differ from those of the test images. We
obtain results comparable to the state-of-the-art. Compared to standard shallow
CNNs, the proposed method obtains higher classification performance while
reducing by an order of magnitude the number of parameters to be learned.
Early Warning System for Seismic Events in Coal Mines Using Machine Learning
Authors: Robert Bogucki, Jan Lasek, Jan Kanty Milczek, Michal Tadeusiak
Date: 2016-09-21 00:00:00
Tasks: BIG-bench Machine Learning

This document describes an approach to the problem of predicting dangerous
seismic events in active coal mines up to 8 hours in advance. It was developed
as a part of the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic
Events in Active Coal Mines. The solutions presented consist of ensembles of
various predictive models trained on different sets of features. The best one
achieved a winning score of 0.939 AUC.
Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference
Authors: Dimitrios A. Adamos, Stavros I. Dimitriadis, Nikolaos A. Laskaris
Date: 2016-09-21 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Music Recommendation, Recommendation Systems

Recent advances in biosensors technology and mobile electroencephalographic
(EEG) interfaces have opened new application fields for cognitive monitoring. A
computable biomarker for the assessment of spontaneous aesthetic brain
responses during music listening is introduced here. It derives from
well-established measures of cross-frequency coupling (CFC) and quantifies the
music-induced alterations in the dynamic relationships between brain rhythms.
During a stage of exploratory analysis, and using the signals from a suitably
designed experiment, we established the biomarker, which acts on brain
activations recorded over the left prefrontal cortex and focuses on the
functional coupling between high-beta and low-gamma oscillations. Based on data
from an additional experimental paradigm, we validated the introduced biomarker
and showed its relevance for expressing the subjective aesthetic appreciation
of a piece of music. Our approach resulted in an affordable tool that can
promote human-machine interaction and, by serving as a personalized music
annotation strategy, can be potentially integrated into modern flexible music
recommendation systems.
  Keywords: Cross-frequency coupling; Human-computer interaction;
Brain-computer interface
Semiring Programming: A Declarative Framework for Generalized Sum Product Problems
Authors: Vaishak Belle, Luc De Raedt
Date: 2016-09-21 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning

To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming. Although it is widely accepted that solving real-world problems requires an integration amongst these, contemporary representation methodologies offer little support for this. In an attempt to alleviate this situation, we introduce a new declarative programming framework that provides abstractions of well-known problems such as SAT, Bayesian inference, generative models, and convex optimization. The semantics of programs is defined in terms of first-order structures with semiring labels, which allows us to freely combine and integrate problems from different AI disciplines.
AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
Authors: Pin-Yu Chen, Thibaut Gensollen, Alfred O. Hero III
Date: 2016-09-21 00:00:00
Tasks: Clustering, Graph Clustering, Spectral Graph Clustering

One of the longstanding problems in spectral graph clustering (SGC) is the
so-called model order selection problem: automated selection of the correct
number of clusters. This is equivalent to the problem of finding the number of
connected components or communities in an undirected graph. In this paper, we
propose AMOS, an automated model order selection algorithm for SGC. Based on a
recent analysis of clustering reliability for SGC under the random
interconnection model, AMOS works by incrementally increasing the number of
clusters, estimating the quality of identified clusters, and providing a series
of clustering reliability tests. Consequently, AMOS outputs clusters of minimal
model order with statistical clustering reliability guarantees. Comparing to
three other automated graph clustering methods on real-world datasets, AMOS
shows superior performance in terms of multiple external and internal
clustering metrics.
Multi-View Constraint Propagation with Consensus Prior Knowledge
Authors: Yaoyi Li, Hongtao Lu
Date: 2016-09-21 00:00:00
Tasks: Clustering

In many applications, the pairwise constraint is a kind of weaker supervisory
information which can be collected easily. The constraint propagation has been
proved to be a success of exploiting such side-information. In recent years,
some methods of multi-view constraint propagation have been proposed. However,
the problem of reasonably fusing different views remains unaddressed. In this
paper, we present a method dubbed Consensus Prior Constraint Propagation
(CPCP), which can provide the prior knowledge of the robustness of each data
instance and its neighborhood. With the robustness generated from the consensus
information of each view, we build a unified affinity matrix as a result of the
propagation. Specifically, we fuse the affinity of different views at a data
instance level instead of a view level. This paper also introduces an approach
to deal with the imbalance between the positive and negative constraints. The
proposed method has been tested in clustering tasks on two publicly available
multi-view data sets to show the superior performance.
Detecting facial landmarks in the video based on a hybrid framework
Authors: Nian Cai, Zhineng Lin, Fu Zhang, Guandong Cen, Han Wang
Date: 2016-09-21 00:00:00
Tasks: Face Detection

To dynamically detect the facial landmarks in the video, we propose a novel
hybrid framework termed as detection-tracking-detection (DTD). First, the face
bounding box is achieved from the first frame of the video sequence based on a
traditional face detection method. Then, a landmark detector detects the facial
landmarks, which is based on a cascaded deep convolution neural network (DCNN).
Next, the face bounding box in the current frame is estimated and validated
after the facial landmarks in the previous frame are tracked based on the
median flow. Finally, the facial landmarks in the current frame are exactly
detected from the validated face bounding box via the landmark detector.
Experimental results indicate that the proposed framework can detect the facial
landmarks in the video sequence more effectively and with lower consuming time
compared to the frame-by-frame method via the DCNN.
Large-Scale Strategic Games and Adversarial Machine Learning
Authors: Tansu Alpcan, Benjamin I. P. Rubinstein, Christopher Leckie
Date: 2016-09-21 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Decision making in modern large-scale and complex systems such as
communication networks, smart electricity grids, and cyber-physical systems
motivate novel game-theoretic approaches. This paper investigates big strategic
(non-cooperative) games where a finite number of individual players each have a
large number of continuous decision variables and input data points. Such
high-dimensional decision spaces and big data sets lead to computational
challenges, relating to efforts in non-linear optimization scaling up to large
systems of variables. In addition to these computational challenges, real-world
players often have limited information about their preference parameters due to
the prohibitive cost of identifying them or due to operating in dynamic online
settings. The challenge of limited information is exacerbated in high
dimensions and big data sets. Motivated by both computational and information
limitations that constrain the direct solution of big strategic games, our
investigation centers around reductions using linear transformations such as
random projection methods and their effect on Nash equilibrium solutions.
Specific analytical results are presented for quadratic games and
approximations. In addition, an adversarial learning game is presented where
random projection and sampling schemes are investigated.
Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Authors: Haoran Chen, Yanfeng Sun, Junbin Gao, Yongli Hu, Bao-Cai Yin
Date: 2016-09-21 00:00:00
Tasks: General Classification, regression

Partial least squares regression (PLSR) has been a popular technique to
explore the linear relationship between two datasets. However, most of
algorithm implementations of PLSR may only achieve a suboptimal solution
through an optimization on the Euclidean space. In this paper, we propose
several novel PLSR models on Riemannian manifolds and develop optimization
algorithms based on Riemannian geometry of manifolds. This algorithm can
calculate all the factors of PLSR globally to avoid suboptimal solutions. In a
number of experiments, we have demonstrated the benefits of applying the
proposed model and algorithm to a variety of learning tasks in pattern
recognition and object classification.
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling
Authors: P. K. Srijith, P. Balamurugan, Shirish Shevade
Date: 2014-12-25 00:00:00
Tasks: Gaussian Processes

Several machine learning problems arising in natural language processing can
be modeled as a sequence labeling problem. We provide Gaussian process models
based on pseudo-likelihood approximation to perform sequence labeling. Gaussian
processes (GPs) provide a Bayesian approach to learning in a kernel based
framework. The pseudo-likelihood model enables one to capture long range
dependencies among the output components of the sequence without becoming
computationally intractable. We use an efficient variational Gaussian
approximation method to perform inference in the proposed model. We also
provide an iterative algorithm which can effectively make use of the
information from the neighboring labels to perform prediction. The ability to
capture long range dependencies makes the proposed approach useful for a wide
range of sequence labeling problems. Numerical experiments on some sequence
labeling data sets demonstrate the usefulness of the proposed approach.
MovieQA: Understanding Stories in Movies through Question-Answering
Authors: Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, Sanja Fidler
Date: 2015-12-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Diversity, Question Answering

We introduce the MovieQA dataset which aims to evaluate automatic story
comprehension from both video and text. The dataset consists of 14,944
questions about 408 movies with high semantic diversity. The questions range
from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events
occurred. Each question comes with a set of five possible answers; a correct
one and four deceiving answers provided by human annotators. Our dataset is
unique in that it contains multiple sources of information -- video clips,
plots, subtitles, scripts, and DVS. We analyze our data through various
statistics and methods. We further extend existing QA techniques to show that
question-answering with such open-ended semantics is hard. We make this data
set public along with an evaluation benchmark to encourage inspiring work in
this challenging domain.
Kernel Cuts: MRF meets Kernel & Spectral Clustering
Authors: Meng Tang, Dmitrii Marin, Ismail Ben Ayed, Yuri Boykov
Date: 2015-06-24 00:00:00
Tasks: Clustering

We propose a new segmentation model combining common regularization energies,
e.g. Markov Random Field (MRF) potentials, and standard pairwise clustering
criteria like Normalized Cut (NC), average association (AA), etc. These
clustering and regularization models are widely used in machine learning and
computer vision, but they were not combined before due to significant
differences in the corresponding optimization, e.g. spectral relaxation and
combinatorial max-flow techniques. On the one hand, we show that many common
applications using MRF segmentation energies can benefit from a high-order NC
term, e.g. enforcing balanced clustering of arbitrary high-dimensional image
features combining color, texture, location, depth, motion, etc. On the other
hand, standard clustering applications can benefit from an inclusion of common
pairwise or higher-order MRF constraints, e.g. edge alignment, bin-consistency,
label cost, etc. To address joint energies like NC+MRF, we propose efficient
Kernel Cut algorithms based on bound optimization. While focusing on graph cut
and move-making techniques, our new unary (linear) kernel and spectral bound
formulations for common pairwise clustering criteria allow to integrate them
with any regularization functionals with existing discrete or continuous
solvers.
How Relevant Are Chess Composition Conventions?
Authors: Azlan Iqbal
Date: 2013-09-12 00:00:00

Composition conventions are guidelines used by human composers in composing
chess problems. They are particularly significant in composition tournaments.
Examples include, not having any check in the first move of the solution and
not dressing up the board with unnecessary pieces. Conventions are often
associated or even directly conflated with the overall aesthetics or beauty of
a composition. Using an existing experimentally-validated computational
aesthetics model for three-move mate problems, we analyzed sets of
computer-generated compositions adhering to at least 2, 3 and 4 comparable
conventions to test if simply conforming to more conventions had a positive
effect on their aesthetics, as is generally believed by human composers. We
found slight but statistically significant evidence that it does, but only to a
point. We also analyzed human judge scores of 145 three-move mate problems
composed by humans to see if they had any positive correlation with the
computational aesthetic scores of those problems. We found that they did not.
These seemingly conflicting findings suggest two main things. First, the right
amount of adherence to composition conventions in a composition has a positive
effect on its perceived aesthetics. Second, human judges either do not look at
the same conventions related to aesthetics in the model used or emphasize
others that have less to do with beauty as perceived by the majority of
players, even though they may mistakenly consider their judgements beautiful in
the traditional, non-esoteric sense. Human judges may also be relying
significantly on personal tastes as we found no correlation between their
individual scores either.
KU-ISPL Language Recognition System for NIST 2015 i-Vector Machine Learning Challenge
Authors: Suwon Shon, Seongkyu Mun, John H. L. Hansen, Hanseok Ko
Date: 2016-09-21 00:00:00
Tasks: BIG-bench Machine Learning

In language recognition, the task of rejecting/differentiating closely spaced
versus acoustically far spaced languages remains a major challenge. For
confusable closely spaced languages, the system needs longer input test
duration material to obtain sufficient information to distinguish between
languages. Alternatively, if languages are distinct and not
acoustically/linguistically similar to others, duration is not a sufficient
remedy. The solution proposed here is to explore duration distribution analysis
for near/far languages based on the Language Recognition i-Vector Machine
Learning Challenge 2015 (LRiMLC15) database. Using this knowledge, we propose a
likelihood ratio based fusion approach that leveraged both score and duration
information. The experimental results show that the use of duration and score
fusion improves language recognition performance by 5% relative in LRiMLC15
cost.
An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse Datasets
Authors: Janani Balaji, Faizan Javed, Mayank Kejriwal, Chris Min, Sam Sander, Ozgur Ozturk
Date: 2016-09-20 00:00:00
Tasks: Blocking, Entity Resolution

Entity Resolution, also called record linkage or deduplication, refers to the
process of identifying and merging duplicate versions of the same entity into a
unified representation. The standard practice is to use a Rule based or Machine
Learning based model that compares entity pairs and assigns a score to
represent the pairs' Match/Non-Match status. However, performing an exhaustive
pair-wise comparison on all pairs of records leads to quadratic matcher
complexity and hence a Blocking step is performed before the Matching to group
similar entities into smaller blocks that the matcher can then examine
exhaustively. Several blocking schemes have been developed to efficiently and
effectively block the input dataset into manageable groups. At CareerBuilder
(CB), we perform deduplication on massive datasets of people profiles collected
from disparate sources with varying informational content. We observed that,
employing a single blocking technique did not cover the base for all possible
scenarios due to the multi-faceted nature of our data sources. In this paper,
we describe our ensemble approach to blocking that combines two different
blocking techniques to leverage their respective strengths.
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Authors: Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing
Date: 2016-09-21 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Density Estimation

Recently, there has been a surge of interest in using spectral methods for
estimating latent variable models. However, it is usually assumed that the
distribution of the observations conditioned on the latent variables is either
discrete or belongs to a parametric family. In this paper, we study the
estimation of an $m$-state hidden Markov model (HMM) with only smoothness
assumptions, such as H\"olderian conditions, on the emission densities. By
leveraging some recent advances in continuous linear algebra and numerical
analysis, we develop a computationally efficient spectral algorithm for
learning nonparametric HMMs. Our technique is based on computing an SVD on
nonparametric estimates of density functions by viewing them as
\emph{continuous matrices}. We derive sample complexity bounds via
concentration results for nonparametric density estimation and novel
perturbation theory results for continuous matrices. We implement our method
using Chebyshev polynomial approximations. Our method is competitive with other
baselines on synthetic and real problems and is also very computationally
efficient.
Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars
Authors: Zhuo Hui, Aswin C. Sankaranarayanan
Date: 2015-12-16 00:00:00
Tasks: BRDF estimation

This paper addresses the problem of estimating the shape of objects that
exhibit spatially-varying reflectance. We assume that multiple images of the
object are obtained under a fixed view-point and varying illumination, i.e.,
the setting of photometric stereo. At the core of our techniques is the
assumption that the BRDF at each pixel lies in the non-negative span of a known
BRDF dictionary.This assumption enables a per-pixel surface normal and BRDF
estimation framework that is computationally tractable and requires no
initialization in spite of the underlying problem being non-convex. Our
estimation framework first solves for the surface normal at each pixel using a
variant of example-based photometric stereo. We design an efficient multi-scale
search strategy for estimating the surface normal and subsequently, refine this
estimate using a gradient descent procedure. Given the surface normal estimate,
we solve for the spatially-varying BRDF by constraining the BRDF at each pixel
to be in the span of the BRDF dictionary, here, we use additional priors to
further regularize the solution. A hallmark of our approach is that it does not
require iterative optimization techniques nor the need for careful
initialization, both of which are endemic to most state-of-the-art techniques.
We showcase the performance of our technique on a wide range of simulated and
real scenes where we outperform competing methods.
Multiclass Classification Calibration Functions
Authors: Bernardo Ávila Pires, Csaba Szepesvári
Date: 2016-09-20 00:00:00
Tasks: Classification, General Classification

In this paper we refine the process of computing calibration functions for a
number of multiclass classification surrogate losses. Calibration functions are
a powerful tool for easily converting bounds for the surrogate risk (which can
be computed through well-known methods) into bounds for the true risk, the
probability of making a mistake. They are particularly suitable in
non-parametric settings, where the approximation error can be controlled, and
provide tighter bounds than the common technique of upper-bounding the 0-1 loss
by the surrogate loss.
  The abstract nature of the more sophisticated existing calibration function
results requires calibration functions to be explicitly derived on a
case-by-case basis, requiring repeated efforts whenever bounds for a new
surrogate loss are required. We devise a streamlined analysis that simplifies
the process of deriving calibration functions for a large number of surrogate
losses that have been proposed in the literature. The effort of deriving
calibration functions is then surmised in verifying, for a chosen surrogate
loss, a small number of conditions that we introduce.
  As case studies, we recover existing calibration functions for the well-known
loss of Lee et al. (2004), and also provide novel calibration functions for
well-known losses, including the one-versus-all loss and the logistic
regression loss, plus a number of other losses that have been shown to be
classification-calibrated in the past, but for which no calibration function
had been derived.