pwc-archive/papers-with-abstracts huggingface dataset:
On Vectorization of Deep Convolutional Neural Networks for Vision Tasks
Authors: Jimmy SJ. Ren, Li Xu
Date: 2015-01-29 00:00:00
Tasks: GPU

We recently have witnessed many ground-breaking results in machine learning
and computer vision, generated by using deep convolutional neural networks
(CNN). While the success mainly stems from the large volume of training data
and the deep network architectures, the vector processing hardware (e.g. GPU)
undisputedly plays a vital role in modern CNN implementations to support
massive computation. Though much attention was paid in the extent literature to
understand the algorithmic side of deep CNN, little research was dedicated to
the vectorization for scaling up CNNs. In this paper, we studied the
vectorization process of key building blocks in deep CNNs, in order to better
understand and facilitate parallel implementation. Key steps in training and
testing deep CNNs are abstracted as matrix and vector operators, upon which
parallelism can be easily achieved. We developed and compared six
implementations with various degrees of vectorization with which we illustrated
the impact of vectorization on the speed of model training and testing.
Besides, a unified CNN framework for both high-level and low-level vision tasks
is provided, along with a vectorized Matlab implementation with
state-of-the-art speed performance.
Stochastic Block Transition Models for Dynamic Networks
Authors: Kevin S. Xu
Date: 2014-11-19 00:00:00
Tasks: Stochastic Block Model

There has been great interest in recent years on statistical models for
dynamic networks. In this paper, I propose a stochastic block transition model
(SBTM) for dynamic networks that is inspired by the well-known stochastic block
model (SBM) for static networks and previous dynamic extensions of the SBM.
Unlike most existing dynamic network models, it does not make a hidden Markov
assumption on the edge-level dynamics, allowing the presence or absence of
edges to directly influence future edge probabilities while retaining the
interpretability of the SBM. I derive an approximate inference procedure for
the SBTM and demonstrate that it is significantly better at reproducing
durations of edges in real social network data.
The Beauty of Capturing Faces: Rating the Quality of Digital Portraits
Authors: Miriam Redi, Nikhil Rasiwasia, Gaurav Aggarwal, Alejandro Jaimes
Date: 2015-01-28 00:00:00

Digital portrait photographs are everywhere, and while the number of face
pictures keeps growing, not much work has been done to on automatic portrait
beauty assessment. In this paper, we design a specific framework to
automatically evaluate the beauty of digital portraits. To this end, we procure
a large dataset of face images annotated not only with aesthetic scores but
also with information about the traits of the subject portrayed. We design a
set of visual features based on portrait photography literature, and
extensively analyze their relation with portrait beauty, exposing interesting
findings about what makes a portrait beautiful. We find that the beauty of a
portrait is linked to its artistic value, and independent from age, race and
gender of the subject. We also show that a classifier trained with our features
to separate beautiful portraits from non-beautiful portraits outperforms
generic aesthetic classifiers.
Google distance between words
Authors: Bjørn Kjos-Hanssen, Alberto J. Evangelista
Date: 2009-01-27 00:00:00

Cilibrasi and Vitanyi have demonstrated that it is possible to extract the
meaning of words from the world-wide web. To achieve this, they rely on the
number of webpages that are found through a Google search containing a given
word and they associate the page count to the probability that the word appears
on a webpage. Thus, conditional probabilities allow them to correlate one word
with another word's meaning. Furthermore, they have developed a similarity
distance function that gauges how closely related a pair of words is. We
present a specific counterexample to the triangle inequality for this
similarity distance function.
An approach to multi-agent planning with incomplete information
Authors: Alejandro Torreño, Eva Onaindia, Óscar Sapena
Date: 2015-01-28 00:00:00

Multi-agent planning (MAP) approaches have been typically conceived for
independent or loosely-coupled problems to enhance the benefits of distributed
planning between autonomous agents as solving this type of problems require
less coordination between the agents' sub-plans. However, when it comes to
tightly-coupled agents' tasks, MAP has been relegated in favour of centralized
approaches and little work has been done in this direction. In this paper, we
present a general-purpose MAP capable to efficiently handle planning problems
with any level of coupling between agents. We propose a cooperative refinement
planning approach, built upon the partial-order planning paradigm, that allows
agents to work with incomplete information and to have incomplete views of the
world, i.e. being ignorant of other agents' information, as well as maintaining
their own private information. We show various experiments to compare the
performance of our system with a distributed CSP-based MAP approach over a
suite of problems.
FMAP: Distributed Cooperative Multi-Agent Planning
Authors: Alejandro Torreño, Eva Onaindia, Óscar Sapena
Date: 2015-01-28 00:00:00

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed
multi-agent planning method that integrates planning and coordination. Although
FMAP is specifically aimed at solving problems that require cooperation among
agents, the flexibility of the domain-independent planning model allows FMAP to
tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore
the plan space by building up refinement plans through a complete and flexible
forward-chaining partial-order planner. The search is guided by $h_{DTG}$, a
novel heuristic function that is based on the concepts of Domain Transition
Graph and frontier state and is optimized to evaluate plans in distributed
environments. Agents in FMAP apply an advanced privacy model that allows them
to adequately keep private information while communicating only the data of the
refinement plans that is relevant to each of the participating agents.
Experimental results show that FMAP is a general-purpose approach that
efficiently solves tightly-coupled domains that have specialized agents and
cooperative goals as well as loosely-coupled problems. Specifically, the
empirical evaluation shows that FMAP outperforms current MAP systems at solving
complex planning tasks that are adapted from the International Planning
Competition benchmarks.
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?
Authors: Xiao-Lei Zhang
Date: 2014-12-03 00:00:00
Tasks: Dimensionality Reduction

In (\cite{zhang2014nonlinear,zhang2014nonlinear2}), we have viewed machine
learning as a coding and dimensionality reduction problem, and further proposed
a simple unsupervised dimensionality reduction method, entitled deep
distributed random samplings (DDRS). In this paper, we further extend it to
supervised learning incrementally. The key idea here is to incorporate label
information into the coding process by reformulating that each center in DDRS
has multiple output units indicating which class the center belongs to. The
supervised learning method seems somewhat similar with random forests
(\cite{breiman2001random}), here we emphasize their differences as follows. (i)
Each layer of our method considers the relationship between part of the data
points in training data with all training data points, while random forests
focus on building each decision tree on only part of training data points
independently. (ii) Our method builds gradually-narrowed network by sampling
less and less data points, while random forests builds gradually-narrowed
network by merging subclasses. (iii) Our method is trained more straightforward
from bottom layer to top layer, while random forests build each tree from top
layer to bottom layer by splitting. (iv) Our method encodes output targets
implicitly in sparse codes, while random forests encode output targets by
remembering the class attributes of the activated nodes. Therefore, our method
is a simpler, more straightforward, and maybe a better alternative choice,
though both methods use two very basic elements---randomization and nearest
neighbor optimization---as the core. This preprint is used to protect the
incremental idea from (\cite{zhang2014nonlinear,zhang2014nonlinear2}). Full
empirical evaluation will be announced carefully later.
A Discrete Tchebichef Transform Approximation for Image and Video Coding
Authors: P. A. M. Oliveira, R. J. Cintra, F. M. Bayer, S. Kulasekera, A. Madanayake
Date: 2015-01-28 00:00:00

In this paper, we introduce a low-complexity approximation for the discrete
Tchebichef transform (DTT). The proposed forward and inverse transforms are
multiplication-free and require a reduced number of additions and bit-shifting
operations. Numerical compression simulations demonstrate the efficiency of the
proposed transform for image and video coding. Furthermore, Xilinx Virtex-6
FPGA based hardware realization shows 44.9% reduction in dynamic power
consumption and 64.7% lower area when compared to the literature.
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Authors: Kevin Vu, John Snyder, Li Li, Matthias Rupp, Brandon F. Chen, Tarek Khelif, Klaus-Robert Müller, Kieron Burke
Date: 2015-01-16 00:00:00
Tasks: BIG-bench Machine Learning, regression

Accurate approximations to density functionals have recently been obtained
via machine learning (ML). By applying ML to a simple function of one variable
without any random sampling, we extract the qualitative dependence of errors on
hyperparameters. We find universal features of the behavior in extreme limits,
including both very small and very large length scales, and the noise-free
limit. We show how such features arise in ML models of density functionals.
Enhanced Mixtures of Part Model for Human Pose Estimation
Authors: Wenjuan Gong, Yongzhen Huang, Jordi Gonzalez, and Liang Wang
Date: 2015-01-22 00:00:00
Tasks: 2D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

Mixture of parts model has been successfully applied to 2D human pose
estimation problem either as explicitly trained body part model or as latent
variables for the whole human body model. Mixture of parts model usually
utilize tree structure for representing relations between body parts. Tree
structures facilitate training and referencing of the model but could not deal
with double counting problems, which hinder its applications in 3D pose
estimation. While most of work targeted to solve these problems tend to modify
the tree models or the optimization target. We incorporate other cues from
input features. For example, in surveillance environments, human silhouettes
can be extracted relative easily although not flawlessly. In this condition, we
can combine extracted human blobs with histogram of gradient feature, which is
commonly used in mixture of parts model for training body part templates. The
method can be easily extend to other candidate features under our generalized
framework. We show 2D body part detection results on a public available
dataset: HumanEva dataset. Furthermore, a 2D to 3D pose estimator is trained
with Gaussian process regression model and 2D body part detections from the
proposed method is fed to the estimator, thus 3D poses are predictable given
new 2D body part detections. We also show results of 3D pose estimation on
HumanEva dataset.
A Distance-Based Decision in the Credal Level
Authors: Amira Essaid, Arnaud Martin, Grégory Smits, Boutheina Ben Yaghlane
Date: 2015-01-28 00:00:00
Tasks: Decision Making

Belief function theory provides a flexible way to combine information
provided by different sources. This combination is usually followed by a
decision making which can be handled by a range of decision rules. Some rules
help to choose the most likely hypothesis. Others allow that a decision is made
on a set of hypotheses. In [6], we proposed a decision rule based on a distance
measure. First, in this paper, we aim to demonstrate that our proposed decision
rule is a particular case of the rule proposed in [4]. Second, we give
experiments showing that our rule is able to decide on a set of hypotheses.
Some experiments are handled on a set of mass functions generated randomly,
others on real databases.
Survey:Natural Language Parsing For Indian Languages
Authors: Monika T. Makwana, Deepak C. Vegda
Date: 2015-01-28 00:00:00
Tasks: Machine Translation, Sentence, Survey, Translation

Syntactic parsing is a necessary task which is required for NLP applications
including machine translation. It is a challenging task to develop a
qualitative parser for morphological rich and agglutinative languages.
Syntactic analysis is used to understand the grammatical structure of a natural
language sentence. It outputs all the grammatical information of each word and
its constituent. Also issues related to it help us to understand the language
in a more detailed way. This literature survey is groundwork to understand the
different parser development for Indian languages and various approaches that
are used to develop such tools and techniques. This paper provides a survey of
research papers from well known journals and conferences.
Feature Sampling Strategies for Action Recognition
Authors: Youjie Zhou, Hongkai Yu, Song Wang
Date: 2015-01-28 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Although dense local spatial-temporal features with bag-of-features
representation achieve state-of-the-art performance for action recognition, the
huge feature number and feature size prevent current methods from scaling up to
real size problems. In this work, we investigate different types of feature
sampling strategies for action recognition, namely dense sampling, uniformly
random sampling and selective sampling. We propose two effective selective
sampling methods using object proposal techniques. Experiments conducted on a
large video dataset show that we are able to achieve better average recognition
accuracy using 25% less features, through one of proposed selective sampling
methods, and even remain comparable accuracy while discarding 70% features.
A Probabilistic Least-Mean-Squares Filter
Authors: Jesus Fernandez-Bes, Víctor Elvira, Steven Van Vaerenbergh
Date: 2015-01-27 00:00:00
Tasks: BIG-bench Machine Learning

We introduce a probabilistic approach to the LMS filter. By means of an
efficient approximation, this approach provides an adaptable step-size LMS
algorithm together with a measure of uncertainty about the estimation. In
addition, the proposed approximation preserves the linear complexity of the
standard LMS. Numerical results show the improved performance of the algorithm
with respect to standard LMS and state-of-the-art algorithms with similar
complexity. The goal of this work, therefore, is to open the door to bring some
more Bayesian machine learning techniques to adaptive filtering.
Microscopic Advances with Large-Scale Learning: Stochastic Optimization for Cryo-EM
Authors: Ali Punjani, Marcus A. Brubaker
Date: 2015-01-19 00:00:00
Tasks: Density Estimation, Stochastic Optimization

Determining the 3D structures of biological molecules is a key problem for
both biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising
technique for structure estimation which relies heavily on computational
methods to reconstruct 3D structures from 2D images. This paper introduces the
challenging Cryo-EM density estimation problem as a novel application for
stochastic optimization techniques. Structure discovery is formulated as MAP
estimation in a probabilistic latent-variable model, resulting in an
optimization problem to which an array of seven stochastic optimization methods
are applied. The methods are tested on both real and synthetic data, with some
methods recovering reasonable structures in less than one epoch from a random
initialization. Complex quasi-Newton methods are found to converge more slowly
than simple gradient-based methods, but all stochastic methods are found to
converge to similar optima. This method represents a major improvement over
existing methods as it is significantly faster and is able to converge from a
random initialization.
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
Authors: Fangjian Guo, Charles Blundell, Hanna Wallach, Katherine Heller
Date: 2014-11-11 00:00:00
Tasks: Bayesian Inference

We present the Bayesian Echo Chamber, a new Bayesian generative model for
social interaction data. By modeling the evolution of people's language usage
over time, this model discovers latent influence relationships between them.
Unlike previous work on inferring influence, which has primarily focused on
simple temporal dynamics evidenced via turn-taking behavior, our model captures
more nuanced influence relationships, evidenced via linguistic accommodation
patterns in interaction content. The model, which is based on a discrete analog
of the multivariate Hawkes process, permits a fully Bayesian inference
algorithm. We validate our model's ability to discover latent influence
patterns using transcripts of arguments heard by the US Supreme Court and the
movie "12 Angry Men." We showcase our model's capabilities by using it to infer
latent influence patterns from Federal Open Market Committee meeting
transcripts, demonstrating state-of-the-art performance at uncovering social
dynamics in group discussions.
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
Authors: Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters
Date: 2015-01-27 00:00:00
Tasks: Causal Inference, Probabilistic Programming

We describe a method to perform functional operations on probability
distributions of random variables. The method uses reproducing kernel Hilbert
space representations of probability distributions, and it is applicable to all
operations which can be applied to points drawn from the respective
distributions. We refer to our approach as {\em kernel probabilistic
programming}. We illustrate it on synthetic data, and show how it can be used
for nonparametric structural equation models, with an application to causal
inference.
A Cheap System for Vehicle Speed Detection
Authors: Chaim Ginzburg, Amit Raphael, Daphna Weinshall
Date: 2015-01-27 00:00:00

The reliable detection of speed of moving vehicles is considered key to
traffic law enforcement in most countries, and is seen by many as an important
tool to reduce the number of traffic accidents and fatalities. Many automatic
systems and different methods are employed in different countries, but as a
rule they tend to be expensive and/or labor intensive, often employing outdated
technology due to the long development time. Here we describe a speed detection
system that relies on simple everyday equipment - a laptop and a consumer web
camera. Our method is based on tracking the license plates of cars, which gives
the relative movement of the cars in the image. This image displacement is
translated to actual motion by using the method of projection to a reference
plane, where the reference plane is the road itself. However, since license
plates do not touch the road, we must compensate for the entailed distortion in
speed measurement. We show how to compute the compensation factor using
knowledge of the license plate standard dimensions. Consequently our system
computes the true speed of moving vehicles fast and accurately. We show
promising results on videos obtained in a number of scenes and with different
car models.
Teaching Deep Convolutional Neural Networks to Play Go
Authors: Christopher Clark, Amos Storkey
Date: 2014-12-10 00:00:00
Tasks: Game of Go

Mastering the game of Go has remained a long standing challenge to the field
of AI. Modern computer Go systems rely on processing millions of possible
future positions to play well, but intuitively a stronger and more 'humanlike'
way to play the game would be to rely on pattern recognition abilities rather
then brute force computation. Following this sentiment, we train deep
convolutional neural networks to play Go by training them to predict the moves
made by expert Go players. To solve this problem we introduce a number of novel
techniques, including a method of tying weights in the network to 'hard code'
symmetries that are expect to exist in the target function, and demonstrate in
an ablation study they considerably improve performance. Our final networks are
able to achieve move prediction accuracies of 41.1% and 44.4% on two different
Go datasets, surpassing previous state of the art on this task by significant
margins. Additionally, while previous move prediction programs have not yielded
strong Go playing programs, we show that the networks trained in this work
acquired high levels of skill. Our convolutional neural networks can
consistently defeat the well known Go program GNU Go, indicating it is state of
the art among programs that do not use Monte Carlo Tree Search. It is also able
to win some games against state of the art Go playing program Fuego while using
a fraction of the play time. This success at playing Go indicates high level
principles of the game were learned.
Parametric Image Segmentation of Humans with Structural Shape Priors
Authors: Alin-Ionut Popa, Cristian Sminchisescu
Date: 2015-01-27 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The figure-ground segmentation of humans in images captured in natural
environments is an outstanding open problem due to the presence of complex
backgrounds, articulation, varying body proportions, partial views and
viewpoint changes. In this work we propose class-specific segmentation models
that leverage parametric max-flow image segmentation and a large dataset of
human shapes. Our contributions are as follows: (1) formulation of a
sub-modular energy model that combines class-specific structural constraints
and data-driven shape priors, within a parametric max-flow optimization
methodology that systematically computes all breakpoints of the model in
polynomial time; (2) design of a data-driven class-specific fusion methodology,
based on matching against a large training set of exemplar human shapes
(100,000 in our experiments), that allows the shape prior to be constructed
on-the-fly, for arbitrary viewpoints and partial views. (3) demonstration of
state of the art results, in two challenging datasets, H3D and MPII (where
figure-ground segmentation annotations have been added by us), where we
substantially improve on the first ranked hypothesis estimates of mid-level
segmentation methods, by 20%, with hypothesis set sizes that are up to one
order of magnitude smaller.