pwc-archive/papers-with-abstracts huggingface dataset:
3D Hand Pose Tracking and Estimation Using Stereo Matching
Authors: Jiawei Zhang, Jianbo Jiao, Mingliang Chen, Liangqiong Qu, Xiaobin Xu, Qingxiong Yang
Date: 2016-10-23 00:00:00
Tasks: Hand Segmentation, Pose Tracking, Stereo Matching, Stereo Matching Hand

3D hand pose tracking/estimation will be very important in the next
generation of human-computer interaction. Most of the currently available
algorithms rely on low-cost active depth sensors. However, these sensors can be
easily interfered by other active sources and require relatively high power
consumption. As a result, they are currently not suitable for outdoor
environments and mobile devices. This paper aims at tracking/estimating hand
poses using passive stereo which avoids these limitations. A benchmark with
18,000 stereo image pairs and 18,000 depth images captured from different
scenarios and the ground-truth 3D positions of palm and finger joints (obtained
from the manual label) is thus proposed. This paper demonstrates that the
performance of the state-of-the art tracking/estimation algorithms can be
maintained with most stereo matching algorithms on the proposed benchmark, as
long as the hand segmentation is correct. As a result, a novel stereo-based
hand segmentation algorithm specially designed for hand tracking/estimation is
proposed. The quantitative evaluation demonstrates that the proposed algorithm
is suitable for the state-of-the-art hand pose tracking/estimation algorithms
and the tracking quality is comparable to the use of active depth sensors under
different challenging scenarios.
Robust Face Alignment Using a Mixture of Invariant Experts
Authors: Oncel Tuzel, Tim K. Marks, Salil Tambe
Date: 2015-11-13 00:00:00
Tasks: Face Alignment, regression, Robust Face Alignment

Face alignment, which is the task of finding the locations of a set of facial
landmark points in an image of a face, is useful in widespread application
areas. Face alignment is particularly challenging when there are large
variations in pose (in-plane and out-of-plane rotations) and facial expression.
To address this issue, we propose a cascade in which each stage consists of a
mixture of regression experts. Each expert learns a customized regression model
that is specialized to a different subset of the joint space of pose and
expressions. The system is invariant to a predefined class of transformations
(e.g., affine), because the input is transformed to match each expert's
prototype shape before the regression is applied. We also present a method to
include deformation constraints within the discriminative alignment framework,
which makes our algorithm more robust. Our algorithm significantly outperforms
previous methods on publicly available face alignment datasets.
From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
Authors: Leopoldo Bertossi, Babak Salimi
Date: 2015-07-01 00:00:00

In this work we establish and investigate connections between causes for
query answers in databases, database repairs wrt. denial constraints, and
consistency-based diagnosis. The first two are relatively new research areas in
databases, and the third one is an established subject in knowledge
representation. We show how to obtain database repairs from causes, and the
other way around. Causality problems are formulated as diagnosis problems, and
the diagnoses provide causes and their responsibilities. The vast body of
research on database repairs can be applied to the newer problems of computing
actual causes for query answers and their responsibilities. These connections,
which are interesting per se, allow us, after a transition -inspired by
consistency-based diagnosis- to computational problems on hitting sets and
vertex covers in hypergraphs, to obtain several new algorithmic and complexity
results for database causality.
Chained Predictions Using Convolutional Neural Networks
Authors: Georgia Gkioxari, Alexander Toshev, Navdeep Jaitly
Date: 2016-05-08 00:00:00
Tasks: Pose Estimation

In this paper, we present an adaptation of the sequence-to-sequence model for
structured output prediction in vision tasks. In this model the output
variables for a given input are predicted sequentially using neural networks.
The prediction for each output variable depends not only on the input but also
on the previously predicted output variables. The model is applied to spatial
localization tasks and uses convolutional neural networks (CNNs) for processing
input images and a multi-scale deconvolutional architecture for making spatial
predictions at each time step. We explore the impact of weight sharing with a
recurrent connection matrix between consecutive predictions, and compare it to
a formulation where these weights are not tied. Untied weights are particularly
suited for problems with a fixed sized structure, where different classes of
output are predicted in different steps. We show that chained predictions
achieve top performing results on human pose estimation from single images and
videos.
Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
Authors: Yangbo He, Bin Yu
Date: 2016-10-23 00:00:00

The sizes of Markov equivalence classes of directed acyclic graphs play
important roles in measuring the uncertainty and complexity in causal learning.
A Markov equivalence class can be represented by an essential graph and its
undirected subgraphs determine the size of the class. In this paper, we develop
a method to derive the formulas for counting the sizes of Markov equivalence
classes. We first introduce a new concept of core graph. The size of a Markov
equivalence class of interest is a polynomial of the number of vertices given
its core graph. Then, we discuss the recursive and explicit formula of the
polynomial, and provide an algorithm to derive the size formula via symbolic
computation for any given core graph. The proposed size formula derivation
sheds light on the relationships between the size of a Markov equivalence class
and its representation graph, and makes size counting efficient, even when the
essential graphs contain non-sparse undirected subgraphs.
Simpler PAC-Bayesian Bounds for Hostile Data
Authors: Pierre Alquier, Benjamin Guedj
Date: 2016-10-23 00:00:00

PAC-Bayesian learning bounds are of the utmost interest to the learning community. Their role is to connect the generalization ability of an aggregation distribution $\rho$ to its empirical risk and to its Kullback-Leibler divergence with respect to some prior distribution $\pi$. Unfortunately, most of the available bounds typically rely on heavy assumptions such as boundedness and independence of the observations. This paper aims at relaxing these constraints and provides PAC-Bayesian learning bounds that hold for dependent, heavy-tailed observations (hereafter referred to as \emph{hostile data}). In these bounds the Kullack-Leibler divergence is replaced with a general version of Csisz\'ar's $f$-divergence. We prove a general PAC-Bayesian bound, and show how to use it in various hostile settings.
Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach
Authors: Ehsan Zahedinejad, Joydip Ghosh, Barry C. Sanders
Date: 2015-11-28 00:00:00
Tasks: BIG-bench Machine Learning

Three-qubit quantum gates are key ingredients for quantum error correction
and quantum information processing. We generate quantum-control procedures to
design three types of three-qubit gates, namely Toffoli, Controlled-Not-Not and
Fredkin gates. The design procedures are applicable to a system comprising
three nearest-neighbor-coupled superconducting artificial atoms. For each
three-qubit gate, the numerical simulation of the proposed scheme achieves
99.9% fidelity, which is an accepted threshold fidelity for fault-tolerant
quantum computing. We test our procedure in the presence of decoherence-induced
noise as well as show its robustness against random external noise generated by
the control electronics. The three-qubit gates are designed via the machine
learning algorithm called Subspace-Selective Self-Adaptive Differential
Evolution (SuSSADE).
How to be Fair and Diverse?
Authors: L. Elisa Celis, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi
Date: 2016-10-23 00:00:00
Tasks: BIG-bench Machine Learning, Data Summarization, Decision Making, Diversity, Fairness

Due to the recent cases of algorithmic bias in data-driven decision-making,
machine learning methods are being put under the microscope in order to
understand the root cause of these biases and how to correct them. Here, we
consider a basic algorithmic task that is central in machine learning:
subsampling from a large data set. Subsamples are used both as an end-goal in
data summarization (where fairness could either be a legal, political or moral
requirement) and to train algorithms (where biases in the samples are often a
source of bias in the resulting model). Consequently, there is a growing effort
to modify either the subsampling methods or the algorithms themselves in order
to ensure fairness. However, in doing so, a question that seems to be
overlooked is whether it is possible to produce fair subsamples that are also
adequately representative of the feature space of the data set - an important
and classic requirement in machine learning. Can diversity and fairness be
simultaneously ensured? We start by noting that, in some applications,
guaranteeing one does not necessarily guarantee the other, and a new approach
is required. Subsequently, we present an algorithmic framework which allows us
to produce both fair and diverse samples. Our experimental results on an image
summarization task show marked improvements in fairness without compromising
feature diversity by much, giving us the best of both the worlds.
Stochastic inference with spiking neurons in the high-conductance state
Authors: Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier
Date: 2016-10-23 00:00:00
Tasks: Bayesian Inference, Vocal Bursts Intensity Prediction

The highly variable dynamics of neocortical circuits observed in vivo have
been hypothesized to represent a signature of ongoing stochastic inference but
stand in apparent contrast to the deterministic response of neurons measured in
vitro. Based on a propagation of the membrane autocorrelation across spike
bursts, we provide an analytical derivation of the neural activation function
that holds for a large parameter space, including the high-conductance state.
On this basis, we show how an ensemble of leaky integrate-and-fire neurons with
conductance-based synapses embedded in a spiking environment can attain the
correct firing statistics for sampling from a well-defined target distribution.
For recurrent networks, we examine convergence toward stationarity in computer
simulations and demonstrate sample-based Bayesian inference in a mixed
graphical model. This points to a new computational role of high-conductance
states and establishes a rigorous link between deterministic neuron models and
functional stochastic dynamics on the network level.
Real-time Halfway Domain Reconstruction of Motion and Geometry
Authors: Lucas Thies, Michael Zollhöfer, Christian Richardt, Christian Theobalt, Günther Greiner
Date: 2016-10-23 00:00:00
Tasks: 3D geometry

We present a novel approach for real-time joint reconstruction of 3D scene
motion and geometry from binocular stereo videos. Our approach is based on a
novel variational halfway-domain scene flow formulation, which allows us to
obtain highly accurate spatiotemporal reconstructions of shape and motion. We
solve the underlying optimization problem at real-time frame rates using a
novel data-parallel robust non-linear optimization strategy. Fast convergence
and large displacement flows are achieved by employing a novel hierarchy that
stores delta flows between hierarchy levels. High performance is obtained by
the introduction of a coarser warp grid that decouples the number of unknowns
from the input resolution of the images. We demonstrate our approach in a live
setup that is based on two commodity webcams, as well as on publicly available
video data. Our extensive experiments and evaluations show that our approach
produces high-quality dense reconstructions of 3D geometry and scene flow at
real-time frame rates, and compares favorably to the state of the art.
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
Authors: Yiping Song, Rui Yan, Xiang Li, Dongyan Zhao, Ming Zhang
Date: 2016-10-23 00:00:00
Tasks: Reranking, Retrieval

Open-domain human-computer conversation has attracted much attention in the
field of NLP. Contrary to rule- or template-based domain-specific dialog
systems, open-domain conversation usually requires data-driven approaches,
which can be roughly divided into two categories: retrieval-based and
generation-based systems. Retrieval systems search a user-issued utterance
(called a query) in a large database, and return a reply that best matches the
query. Generative approaches, typically based on recurrent neural networks
(RNNs), can synthesize new replies, but they suffer from the problem of
generating short, meaningless utterances. In this paper, we propose a novel
ensemble of retrieval-based and generation-based dialog systems in the open
domain. In our approach, the retrieved candidate, in addition to the original
query, is fed to an RNN-based reply generator, so that the neural model is
aware of more information. The generated reply is then fed back as a new
candidate for post-reranking. Experimental results show that such ensemble
outperforms each single part of it by a large margin.
Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
Authors: Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Date: 2016-07-26 00:00:00

We introduce a framework for model learning and planning in stochastic
domains with continuous state and action spaces and non-Gaussian transition
models. It is efficient because (1) local models are estimated only when the
planner requires them; (2) the planner focuses on the most relevant states to
the current planning problem; and (3) the planner focuses on the most
informative and/or high-value actions. Our theoretical analysis shows the
validity and asymptotic optimality of the proposed approach. Empirically, we
demonstrate the effectiveness of our algorithm on a simulated multi-modal
pushing problem.
Recurrent Memory Array Structures
Authors: Kamil Rocki
Date: 2016-07-11 00:00:00
Tasks: Language Modelling

The following report introduces ideas augmenting standard Long Short Term
Memory (LSTM) architecture with multiple memory cells per hidden unit in order
to improve its generalization capabilities. It considers both deterministic and
stochastic variants of memory operation. It is shown that the nondeterministic
Array-LSTM approach improves state-of-the-art performance on character level
text prediction achieving 1.402 BPC on enwik8 dataset. Furthermore, this report
estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9
and enwik10 datasets respectively.
Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning
Authors: Yu Wang, Haofu Liao, Yang Feng, Xiangyang Xu, Jiebo Luo
Date: 2016-10-06 00:00:00
Tasks: All, Attribute, Marketing

We study to what extend Chinese, Japanese and Korean faces can be classified
and which facial attributes offer the most important cues. First, we propose a
novel way of obtaining large numbers of facial images with nationality labels.
Then we train state-of-the-art neural networks with these labeled images. We
are able to achieve an accuracy of 75.03% in the classification task, with
chances being 33.33% and human accuracy 38.89% . Further, we train multiple
facial attribute classifiers to identify the most distinctive features for each
group. We find that Chinese, Japanese and Koreans do exhibit substantial
differences in certain attributes, such as bangs, smiling, and bushy eyebrows.
Along the way, we uncover several gender-related cross-country patterns as
well. Our work, which complements existing APIs such as Microsoft Cognitive
Services and Face++, could find potential applications in tourism, e-commerce,
social media marketing, criminal justice and even counter-terrorism.
Fast and Reliable Parameter Estimation from Nonlinear Observations
Authors: Samet Oymak, Mahdi Soltanolkotabi
Date: 2016-10-23 00:00:00
Tasks: parameter estimation

In this paper we study the problem of recovering a structured but unknown
parameter ${\bf{\theta}}^*$ from $n$ nonlinear observations of the form
$y_i=f(\langle {\bf{x}}_i,{\bf{\theta}}^*\rangle)$ for $i=1,2,\ldots,n$. We
develop a framework for characterizing time-data tradeoffs for a variety of
parameter estimation algorithms when the nonlinear function $f$ is unknown.
This framework includes many popular heuristics such as projected/proximal
gradient descent and stochastic schemes. For example, we show that a projected
gradient descent scheme converges at a linear rate to a reliable solution with
a near minimal number of samples. We provide a sharp characterization of the
convergence rate of such algorithms as a function of sample size, amount of
a-prior knowledge available about the parameter and a measure of the
nonlinearity of the function $f$. These results provide a precise understanding
of the various tradeoffs involved between statistical and computational
resources as well as a-prior side information available for such nonlinear
parameter estimation problems.
Independent Component Analysis by Entropy Maximization with Kernels
Authors: Zois Boukouvalas, Rami Mowakeaa, Geng-Shen Fu, Tulay Adali
Date: 2016-10-22 00:00:00
Tasks: blind source separation

Independent component analysis (ICA) is the most popular method for blind
source separation (BSS) with a diverse set of applications, such as biomedical
signal processing, video and image analysis, and communications. Maximum
likelihood (ML), an optimal theoretical framework for ICA, requires knowledge
of the true underlying probability density function (PDF) of the latent
sources, which, in many applications, is unknown. ICA algorithms cast in the ML
framework often deviate from its theoretical optimality properties due to poor
estimation of the source PDF. Therefore, accurate estimation of source PDFs is
critical in order to avoid model mismatch and poor ICA performance. In this
paper, we propose a new and efficient ICA algorithm based on entropy
maximization with kernels, (ICA-EMK), which uses both global and local
measuring functions as constraints to dynamically estimate the PDF of the
sources with reasonable complexity. In addition, the new algorithm performs
optimization with respect to each of the cost function gradient directions
separately, enabling parallel implementations on multi-core computers. We
demonstrate the superior performance of ICA-EMK over competing ICA algorithms
using simulated as well as real-world data.
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles
Authors: Dominik Meyer, Johannes Feldmaier, Hao Shen
Date: 2016-10-22 00:00:00
Tasks: Autonomous Vehicles, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work, we investigate the application of Reinforcement Learning to two
well known decision dilemmas, namely Newcomb's Problem and Prisoner's Dilemma.
These problems are exemplary for dilemmas that autonomous agents are faced with
when interacting with humans. Furthermore, we argue that a Newcomb-like
formulation is more adequate in the human-machine interaction case and
demonstrate empirically that the unmodified Reinforcement Learning algorithms
end up with the well known maximum expected utility solution.
The Bayesian Linear Information Filtering Problem
Authors: Bangrui Chen, Peter I. Frazier
Date: 2016-05-30 00:00:00
Tasks: Articles, Decision Making, Sequential Decision Making

We present a Bayesian sequential decision-making formulation of the
information filtering problem, in which an algorithm presents items (news
articles, scientific papers, tweets) arriving in a stream, and learns relevance
from user feedback on presented items. We model user preferences using a
Bayesian linear model, similar in spirit to a Bayesian linear bandit. We
compute a computational upper bound on the value of the optimal policy, which
allows computing an optimality gap for implementable policies. We then use this
analysis as motivation in introducing a pair of new Decompose-Then-Decide (DTD)
heuristic policies, DTD-Dynamic-Programming (DTD-DP) and
DTD-Upper-Confidence-Bound (DTD-UCB). We compare DTD-DP and DTD-UCB against
several benchmarks on real and simulated data, demonstrating significant
improvement, and show that the achieved performance is close to the upper
bound.
Optimization on Submanifolds of Convolution Kernels in CNNs
Authors: Mete Ozay, Takayuki Okatani
Date: 2016-10-22 00:00:00
Tasks: General Classification, image-classification, Image Classification

Kernel normalization methods have been employed to improve robustness of
optimization methods to reparametrization of convolution kernels, covariate
shift, and to accelerate training of Convolutional Neural Networks (CNNs).
However, our understanding of theoretical properties of these methods has
lagged behind their success in applications. We develop a geometric framework
to elucidate underlying mechanisms of a diverse range of kernel normalization
methods. Our framework enables us to expound and identify geometry of space of
normalized kernels. We analyze and delineate how state-of-the-art kernel
normalization methods affect the geometry of search spaces of the stochastic
gradient descent (SGD) algorithms in CNNs. Following our theoretical results,
we propose a SGD algorithm with assurance of almost sure convergence of the
methods to a solution at single minimum of classification loss of CNNs.
Experimental results show that the proposed method achieves state-of-the-art
performance for major image classification benchmarks with CNNs.
Ranking of classification algorithms in terms of mean-standard deviation using A-TOPSIS
Authors: Andre G. C. Pacheco, Renato A. Krohling
Date: 2016-10-22 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

In classification problems when multiples algorithms are applied to different
benchmarks a difficult issue arises, i.e., how can we rank the algorithms? In
machine learning it is common run the algorithms several times and then a
statistic is calculated in terms of means and standard deviations. In order to
compare the performance of the algorithms, it is very common to employ
statistical tests. However, these tests may also present limitations, since
they consider only the means and not the standard deviations of the obtained
results. In this paper, we present the so called A-TOPSIS, based on TOPSIS
(Technique for Order Preference by Similarity to Ideal Solution), to solve the
problem of ranking and comparing classification algorithms in terms of means
and standard deviations. We use two case studies to illustrate the A-TOPSIS for
ranking classification algorithms and the results show the suitability of
A-TOPSIS to rank the algorithms. The presented approach is general and can be
applied to compare the performance of stochastic algorithms in machine
learning. Finally, to encourage researchers to use the A-TOPSIS for ranking
algorithms we also presented in this work an easy-to-use A-TOPSIS web
framework.