pwc-archive/papers-with-abstracts huggingface dataset:
SIMD Parallel MCMC Sampling with Applications for Big-Data Bayesian Analytics
Authors: Alireza S. Mahani, Mansour T. A. Sharabiani
Date: 2013-10-06 00:00:00

Computational intensity and sequential nature of estimation techniques for
Bayesian methods in statistics and machine learning, combined with their
increasing applications for big data analytics, necessitate both the
identification of potential opportunities to parallelize techniques such as
MCMC sampling, and the development of general strategies for mapping such
parallel algorithms to modern CPUs in order to elicit the performance up the
compute-based and/or memory-based hardware limits. Two opportunities for
Single-Instruction Multiple-Data (SIMD) parallelization of MCMC sampling for
probabilistic graphical models are presented. In exchangeable models with many
observations such as Bayesian Generalized Linear Models, child-node
contributions to the conditional posterior of each node can be calculated
concurrently. In undirected graphs with discrete nodes, concurrent sampling of
conditionally-independent nodes can be transformed into a SIMD form.
High-performance libraries with multi-threading and vectorization capabilities
can be readily applied to such SIMD opportunities to gain decent speedup, while
a series of high-level source-code and runtime modifications provide further
performance boost by reducing parallelization overhead and increasing data
locality for NUMA architectures. For big-data Bayesian GLM graphs, the
end-result is a routine for evaluating the conditional posterior and its
gradient vector that is 5 times faster than a naive implementation using
(built-in) multi-threaded Intel MKL BLAS, and reaches within the striking
distance of the memory-bandwidth-induced hardware limit. The proposed
optimization strategies improve the scaling of performance with number of cores
and width of vector units (applicable to many-core SIMD processors such as
Intel Xeon Phi and GPUs), resulting in cost-effectiveness, energy efficiency,
and higher speed on multi-core x86 processors.
Im2Fit: Fast 3D Model Fitting and Anthropometrics using Single Consumer Depth Camera and Synthetic Data
Authors: Qiaosong Wang, Vignesh Jagadeesh, Bryan Ressler, Robinson Piramuthu
Date: 2014-10-03 00:00:00
Tasks: Virtual Try-on

Recent advances in consumer depth sensors have created many opportunities for
human body measurement and modeling. Estimation of 3D body shape is
particularly useful for fashion e-commerce applications such as virtual try-on
or fit personalization. In this paper, we propose a method for capturing
accurate human body shape and anthropometrics from a single consumer grade
depth sensor. We first generate a large dataset of synthetic 3D human body
models using real-world body size distributions. Next, we estimate key body
measurements from a single monocular depth image. We combine body measurement
estimates with local geometry features around key joint positions to form a
robust multi-dimensional feature vector. This allows us to conduct a fast
nearest-neighbor search to every sample in the dataset and return the closest
one. Compared to existing methods, our approach is able to predict accurate
full body parameters from a partial view using measurement parameters learned
from the synthetic dataset. Furthermore, our system is capable of generating 3D
human mesh models in real-time, which is significantly faster than methods
which attempt to model shape and pose deformations. To validate the efficiency
and applicability of our system, we collected a dataset that contains frontal
and back scans of 83 clothed people with ground truth height and weight.
Experiments on real-world dataset show that the proposed method can achieve
real-time performance with competing results achieving an average error of 1.9
cm in estimated measurements.
Affordances Provide a Fundamental Categorization Principle for Visual Scenes
Authors: Michelle R. Greene, Christopher Baldassano, Andre Esteva, Diane M. Beck, Li Fei-Fei
Date: 2014-11-19 00:00:00

How do we know that a kitchen is a kitchen by looking? Relatively little is
known about how we conceptualize and categorize different visual environments.
Traditional models of visual perception posit that scene categorization is
achieved through the recognition of a scene's objects, yet these models cannot
account for the mounting evidence that human observers are relatively
insensitive to the local details in an image. Psychologists have long theorized
that the affordances, or actionable possibilities of a stimulus are pivotal to
its perception. To what extent are scene categories created from similar
affordances? Using a large-scale experiment using hundreds of scene categories,
we show that the activities afforded by a visual scene provide a fundamental
categorization principle. Affordance-based similarity explained the majority of
the structure in the human scene categorization patterns, outperforming
alternative similarities based on objects or visual features. We all models
were combined, affordances provided the majority of the predictive power in the
combined model, and nearly half of the total explained variance is captured
only by affordances. These results challenge many existing models of high-level
visual perception, and provide immediately testable hypotheses for the
functional organization of the human perceptual system.
Visual Noise from Natural Scene Statistics Reveals Human Scene Category Representations
Authors: Michelle R. Greene, Abraham P. Botros, Diane M. Beck, Li Fei-Fei
Date: 2014-11-19 00:00:00

Our perceptions are guided both by the bottom-up information entering our
eyes, as well as our top-down expectations of what we will see. Although
bottom-up visual processing has been extensively studied, comparatively little
is known about top-down signals. Here, we describe REVEAL (Representations
Envisioned Via Evolutionary ALgorithm), a method for visualizing an observer's
internal representation of a complex, real-world scene, allowing us to, for the
first time, visualize the top-down information in an observer's mind. REVEAL
rests on two innovations for solving this high dimensional problem: visual
noise that samples from natural image statistics, and a computer algorithm that
collaborates with human observers to efficiently obtain a solution. In this
work, we visualize observers' internal representations of a visual scene
category (street) using an experiment in which the observer views the
naturalistic visual noise and collaborates with the algorithm to externalize
his internal representation. As no scene information was presented, observers
had to use their internal knowledge of the target, matching it with the visual
features in the noise. We matched reconstructed images with images of
real-world street scenes to enhance visualization. Critically, we show that the
visualized mental images can be used to predict rapid scene detection
performance, as each observer had faster and more accurate responses to
detecting real-world images that were the most similar to his reconstructed
street templates. These results show that it is possible to visualize
previously unobservable mental representations of real world stimuli. More
broadly, REVEAL provides a general method for objectively examining the content
of previously private, subjective mental experiences.
ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections
Authors: Bolei Zhou, Vignesh Jagadeesh, Robinson Piramuthu
Date: 2014-11-19 00:00:00
Proceeding: CVPR 2015 6
Tasks: object-detection, Object Detection, Scene Recognition

Discovering visual knowledge from weakly labeled data is crucial to scale up
computer vision recognition system, since it is expensive to obtain fully
labeled data for a large number of concept categories. In this paper, we
propose ConceptLearner, which is a scalable approach to discover visual
concepts from weakly labeled image collections. Thousands of visual concept
detectors are learned automatically, without human in the loop for additional
annotation. We show that these learned detectors could be applied to recognize
concepts at image-level and to detect concepts at image region-level
accurately. Under domain-specific supervision, we further evaluate the learned
concepts for scene recognition on SUN database and for object detection on
Pascal VOC 2007. ConceptLearner shows promising performance compared to fully
supervised and weakly supervised methods.
Compress and Control
Authors: Joel Veness, Marc G. Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins
Date: 2014-11-19 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

This paper describes a new information-theoretic policy evaluation technique
for reinforcement learning. This technique converts any compression or density
model into a corresponding estimate of value. Under appropriate stationarity
and ergodicity conditions, we show that the use of a sufficiently powerful
model gives rise to a consistent value function estimator. We also study the
behavior of this technique when applied to various Atari 2600 video games,
where the use of suboptimal modeling techniques is unavoidable. We consider
three fundamentally different models, all too limited to perfectly model the
dynamics of the system. Remarkably, we find that our technique provides
sufficiently accurate value estimates for effective on-policy control. We
conclude with a suggestive study highlighting the potential of our technique to
scale to large problems.
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
Authors: Li Wan, David Eigen, Rob Fergus
Date: 2014-11-19 00:00:00
Tasks: Object, object-detection, Object Detection

Deformable Parts Models and Convolutional Networks each have achieved notable
performance in object detection. Yet these two approaches find their strengths
in complementary areas: DPMs are well-versed in object composition, modeling
fine-grained spatial relationships between parts; likewise, ConvNets are adept
at producing powerful image features, having been discriminatively trained
directly on the pixels. In this paper, we propose a new model that combines
these two approaches, obtaining the advantages of each. We train this model
using a new structured loss function that considers all bounding boxes within
an image, rather than isolated object instances. This enables the non-maximal
suppression (NMS) operation, previously treated as a separate post-processing
stage, to be integrated into the model. This allows for discriminative training
of our combined Convnet + DPM + NMS model in end-to-end fashion. We evaluate
our system on PASCAL VOC 2007 and 2011 datasets, achieving competitive results
on both benchmarks.
Efficient Media Retrieval from Non-Cooperative Queries
Authors: Kevin Shih, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu
Date: 2014-11-19 00:00:00
Tasks: Optical Character Recognition (OCR), Retrieval, Text Matching

Text is ubiquitous in the artificial world and easily attainable when it
comes to book title and author names. Using the images from the book cover set
from the Stanford Mobile Visual Search dataset and additional book covers and
metadata from openlibrary.org, we construct a large scale book cover retrieval
dataset, complete with 100K distractor covers and title and author strings for
each. Because our query images are poorly conditioned for clean text
extraction, we propose a method for extracting a matching noisy and erroneous
OCR readings and matching it against clean author and book title strings in a
standard document look-up problem setup. Finally, we demonstrate how to use
this text-matching as a feature in conjunction with popular retrieval features
such as VLAD using a simple learning setup to achieve significant improvements
in retrieval accuracy over that of either VLAD or the text alone.
Sparse distributed localized gradient fused features of objects
Authors: Swathikiran Sudhakarana, Alex Pappachen James
Date: 2014-11-19 00:00:00
Tasks: Object, Object Recognition

The sparse, hierarchical, and modular processing of natural signals is
related to the ability of humans to recognize objects with high accuracy. In
this study, we report a sparse feature processing and encoding method, which
improved the recognition performance of an automated object recognition system.
Randomly distributed localized gradient enhanced features were selected before
employing aggregate functions for representation, where we used a modular and
hierarchical approach to detect the object features. These object features were
combined with a minimum distance classifier, thereby obtaining object
recognition system accuracies of 93% using the Amsterdam library of object
images (ALOI) database, 92% using the Columbia object image library (COIL)-100
database, and 69% using the PASCAL visual object challenge 2007 database. The
object recognition performance was shown to be robust to variations in noise,
object scaling, and object shifts. Finally, a comparison with eight existing
object recognition methods indicated that our new method improved the
recognition accuracy by 10% with ALOI, 8% with the COIL-100 database, and 10%
with the PASCAL visual object challenge 2007 database.
Large-Margin Classification with Multiple Decision Rules
Authors: Patrick K. Kimes, D. Neil Hayes, J. S. Marron, Yufeng Liu
Date: 2014-11-19 00:00:00
Tasks: Binary Classification, Classification, General Classification

Binary classification is a common statistical learning problem in which a
model is estimated on a set of covariates for some outcome indicating the
membership of one of two classes. In the literature, there exists a distinction
between hard and soft classification. In soft classification, the conditional
class probability is modeled as a function of the covariates. In contrast, hard
classification methods only target the optimal prediction boundary. While hard
and soft classification methods have been studied extensively, not much work
has been done to compare the actual tasks of hard and soft classification. In
this paper we propose a spectrum of statistical learning problems which span
the hard and soft classification tasks based on fitting multiple decision rules
to the data. By doing so, we reveal a novel collection of learning tasks of
increasing complexity. We study the problems using the framework of
large-margin classifiers and a class of piecewise linear convex surrogates, for
which we derive statistical properties and a corresponding sub-gradient descent
algorithm. We conclude by applying our approach to simulation settings and a
magnetic resonance imaging (MRI) dataset from the Alzheimer's Disease
Neuroimaging Initiative (ADNI) study.
Zero-Aliasing Correlation Filters for Object Recognition
Authors: Joseph A. Fernandez, Vishnu Naresh Boddeti, Andres Rodriguez, B. V. K. Vijaya Kumar
Date: 2014-11-10 00:00:00
Tasks: Object, Object Localization, Object Recognition

Correlation filters (CFs) are a class of classifiers that are attractive for
object localization and tracking applications. Traditionally, CFs have been
designed in the frequency domain using the discrete Fourier transform (DFT),
where correlation is efficiently implemented. However, existing CF designs do
not account for the fact that the multiplication of two DFTs in the frequency
domain corresponds to a circular correlation in the time/spatial domain.
Because this was previously unaccounted for, prior CF designs are not truly
optimal, as their optimization criteria do not accurately quantify their
optimization intention. In this paper, we introduce new zero-aliasing
constraints that completely eliminate this aliasing problem by ensuring that
the optimization criterion for a given CF corresponds to a linear correlation
rather than a circular correlation. This means that previous CF designs can be
significantly improved by this reformulation. We demonstrate the benefits of
this new CF design approach with several important CFs. We present experimental
results on diverse data sets and present solutions to the computational
challenges associated with computing these CFs. Code for the CFs described in
this paper and their respective zero-aliasing versions is available at
http://vishnu.boddeti.net/projects/correlation-filters.html
Learning nonparametric differential equations with operator-valued kernels and gradient matching
Authors: Markus Heinonen, Florence d'Alché-Buc
Date: 2014-11-19 00:00:00
Tasks: regression

Modeling dynamical systems with ordinary differential equations implies a
mechanistic view of the process underlying the dynamics. However in many cases,
this knowledge is not available. To overcome this issue, we introduce a general
framework for nonparametric ODE models using penalized regression in
Reproducing Kernel Hilbert Spaces (RKHS) based on operator-valued kernels.
Moreover, we extend the scope of gradient matching approaches to nonparametric
ODE. A smooth estimate of the solution ODE is built to provide an approximation
of the derivative of the ODE solution which is in turn used to learn the
nonparametric ODE model. This approach benefits from the flexibility of
penalized regression in RKHS allowing for ridge or (structured) sparse
regression as well. Very good results are shown on 3 different ODE systems.
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Authors: Qian Wang, Jiaxing Zhang, Sen Song, Zheng Zhang
Date: 2014-11-19 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: feature selection, General Classification

Attentional Neural Network is a new framework that integrates top-down
cognitive bias and bottom-up feature extraction in one coherent architecture.
The top-down influence is especially effective when dealing with high noise or
difficult segmentation problems. Our system is modular and extensible. It is
also easy to train and cheap to run, and yet can accommodate complex behaviors.
We obtain classification accuracy better than or competitive with state of art
results on the MNIST variation dataset, and successfully disentangle overlaid
digits with high success rates. We view such a general purpose framework as an
essential foundation for a larger system emulating the cognitive abilities of
the whole brain.
Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling
Authors: Xingguo Li, Jarvis Haupt
Date: 2014-07-01 00:00:00
Tasks: Collaborative Filtering

This paper examines the problem of locating outlier columns in a large,
otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and
inference approach and establish theoretical guarantees for its performance;
our results show that accurate outlier identification is achievable using very
few linear summaries of the original data matrix -- as few as the squared rank
of the low-rank component plus the number of outliers, times constant and
logarithmic factors. We demonstrate the performance of our approach
experimentally in two stylized applications, one motivated by robust
collaborative filtering tasks, and the other by saliency map estimation tasks
arising in computer vision and automated surveillance, and also investigate
extensions to settings where the data are noisy, or possibly incomplete.
Scalable Topical Phrase Mining from Text Corpora
Authors: Ahmed El-Kishky, Yanglei Song, Chi Wang, Clare Voss, Jiawei Han
Date: 2014-06-24 00:00:00
Tasks: Articles, Topic Models

While most topic modeling algorithms model text corpora with unigrams, human
interpretation often relies on inherent grouping of terms into phrases. As
such, we consider the problem of discovering topical phrases of mixed lengths.
Existing work either performs post processing to the inference results of
unigram-based topic models, or utilizes complex n-gram-discovery topic models.
These methods generally produce low-quality topical phrases or suffer from poor
scalability on even moderately-sized datasets. We propose a different approach
that is both computationally efficient and effective. Our solution combines a
novel phrase mining framework to segment a document into single and multi-word
phrases, and a new topic model that operates on the induced document partition.
Our approach discovers high quality topical phrases with negligible extra cost
to the bag-of-words topic model in a variety of datasets including research
publication titles, abstracts, reviews, and news articles.
Model of Interaction between Learning and Evolution
Authors: Vladimir G. Red'ko
Date: 2014-11-18 00:00:00
Tasks: model

The model of interaction between learning and evolutionary optimization is
designed and investigated. The evolving population of modeled organisms is
considered. The mechanism of the genetic assimilation of the acquired features
during a number of generations of Darwinian evolution is studied. It is shown
that the genetic assimilation takes place as follows: phenotypes of modeled
organisms move towards the optimum at learning; then the selection takes place;
genotypes of selected organisms also move towards the optimum. The hiding
effect is also studied; this effect means that strong learning can inhibit the
evolutionary search for the optimal genotype. The mechanism of influence of the
learning load on the interaction between learning and evolution is analyzed. It
is shown that the learning load can lead to a significant acceleration of
evolution.
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material
Authors: Matteo Pirotta, Simone Parisi, Marcello Restelli
Date: 2014-06-13 00:00:00
Tasks: Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This document contains supplementary material for the paper "Multi-objective
Reinforcement Learning with Continuous Pareto Frontier Approximation",
published at the Twenty-Ninth AAAI Conference on Artificial Intelligence
(AAAI-15). The paper is about learning a continuous approximation of the Pareto
frontier in Multi-Objective Markov Decision Problems (MOMDPs). We propose a
policy-based approach that exploits gradient information to generate solutions
close to the Pareto ones. Differently from previous policy-gradient
multi-objective algorithms, where n optimization routines are use to have n
solutions, our approach performs a single gradient-ascent run that at each step
generates an improved continuous approximation of the Pareto frontier. The idea
is to exploit a gradient-based approach to optimize the parameters of a
function that defines a manifold in the policy parameter space so that the
corresponding image in the objective space gets as close as possible to the
Pareto frontier. Besides deriving how to compute and estimate such gradient, we
will also discuss the non-trivial issue of defining a metric to assess the
quality of the candidate Pareto frontiers. Finally, the properties of the
proposed approach are empirically evaluated on two interesting MOMDPs.
A Unified View of Large-scale Zero-sum Equilibrium Computation
Authors: Kevin Waugh, J. Andrew Bagnell
Date: 2014-11-18 00:00:00

The task of computing approximate Nash equilibria in large zero-sum
extensive-form games has received a tremendous amount of attention due mainly
to the Annual Computer Poker Competition. Immediately after its inception, two
competing and seemingly different approaches emerged---one an application of
no-regret online learning, the other a sophisticated gradient method applied to
a convex-concave saddle-point formulation. Since then, both approaches have
grown in relative isolation with advancements on one side not effecting the
other. In this paper, we rectify this by dissecting and, in a sense, unify the
two views.