pwc-archive/papers-with-abstracts huggingface dataset:
A Sparse Representation of Complete Local Binary Pattern Histogram for Human Face Recognition
Authors: Mawloud Guermoui, Mohamed L. Mekhalfi
Date: 2016-05-31 00:00:00
Tasks: Face Recognition, General Classification

Human face recognition has been a long standing problem in computer vision
and pattern recognition. Facial analysis can be viewed as a two-fold problem,
namely (i) facial representation, and (ii) classification. So far, many face
representations have been proposed, a well-known method is the Local Binary
Pattern (LBP), which has witnessed a growing interest. In this respect, we
treat in this paper the issues of face representation as well as classification
in a novel manner. On the one hand, we use a variant to LBP, so-called Complete
Local Binary Pattern (CLBP), which differs from the basic LBP by coding a given
local region using a given central pixel and Sing_ Magnitude difference.
Subsequently, most of LBPbased descriptors use a fixed grid to code a given
facial image, which technique is, in most cases, not robust to pose variation
and misalignment. To cope with such issue, a representative Multi-Resolution
Histogram (MH) decomposition is adopted in our work. On the other hand, having
the histograms of the considered images extracted, we exploit their sparsity to
construct a so-called Sparse Representation Classifier (SRC) for further face
classification. Experimental results have been conducted on ORL face database,
and pointed out the superiority of our scheme over other popular
state-of-the-art techniques.
Model-driven Simulations for Deep Convolutional Neural Networks
Authors: V. S. R. Veeravasarapu, Constantin Rothkopf, Visvanathan Ramesh
Date: 2016-05-31 00:00:00
Tasks: Domain Adaptation, model, valid

The use of simulated virtual environments to train deep convolutional neural
networks (CNN) is a currently active practice to reduce the
(real)data-hungriness of the deep CNN models, especially in application domains
in which large scale real data and/or groundtruth acquisition is difficult or
laborious. Recent approaches have attempted to harness the capabilities of
existing video games, animated movies to provide training data with high
precision groundtruth. However, a stumbling block is in how one can certify
generalization of the learned models and their usefulness in real world data
sets. This opens up fundamental questions such as: What is the role of
photorealism of graphics simulations in training CNN models? Are the trained
models valid in reality? What are possible ways to reduce the performance bias?
In this work, we begin to address theses issues systematically in the context
of urban semantic understanding with CNNs. Towards this end, we (a) propose a
simple probabilistic urban scene model, (b) develop a parametric rendering tool
to synthesize the data with groundtruth, followed by (c) a systematic
exploration of the impact of level-of-realism on the generality of the trained
CNN model to real world; and domain adaptation concepts to minimize the
performance bias.
Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler
Authors: Gregory Grefenstette, Lawrence Muchemi
Date: 2016-05-31 00:00:00

Specialized dictionaries are used to understand concepts in specific domains,
especially where those concepts are not part of the general vocabulary, or
having meanings that differ from ordinary languages. The first step in creating
a specialized dictionary involves detecting the characteristic vocabulary of
the domain in question. Classical methods for detecting this vocabulary involve
gathering a domain corpus, calculating statistics on the terms found there, and
then comparing these statistics to a background or general language corpus.
Terms which are found significantly more often in the specialized corpus than
in the background corpus are candidates for the characteristic vocabulary of
the domain. Here we present two tools, a directed crawler, and a distributional
semantics package, that can be used together, circumventing the need of a
background corpus. Both tools are available on the web.
Modeling Photographic Composition via Triangles
Authors: Zihan Zhou, Siqiong He, Jia Li, James Z. Wang
Date: 2016-05-31 00:00:00
Tasks: Image Retrieval, Retrieval

The capacity of automatically modeling photographic composition is valuable
for many real-world machine vision applications such as digital photography,
image retrieval, image understanding, and image aesthetics assessment. The
triangle technique is among those indispensable composition methods on which
professional photographers often rely. This paper proposes a system that can
identify prominent triangle arrangements in two major categories of
photographs: natural or urban scenes, and portraits. For the natural or urban
scene pictures, the focus is on the effect of linear perspective. For
portraits, we carefully examine the positioning of human subjects in a photo.
We show that line analysis is highly advantageous for modeling composition in
both categories. Based on the detected triangles, new mathematical descriptors
for composition are formulated and used to retrieve similar images. Leveraging
the rich source of high aesthetics photos online, similar approaches can
potentially be incorporated in future smart cameras to enhance a person's photo
composition skills.
Linear Classification of data with Support Vector Machines and Generalized Support Vector Machines
Authors: Xiaomin Qi, Sergei Silvestrov, Talat Nazir
Date: 2016-05-31 00:00:00
Tasks: General Classification

In this paper, we study the support vector machine and introduced the notion
of generalized support vector machine for classification of data. We show that
the problem of generalized support vector machine is equivalent to the problem
of generalized variational inequality and establish various results for the
existence of solutions. Moreover, we provide various examples to support our
results.
Semantic-Aware Depth Super-Resolution in Outdoor Scenes
Authors: Miaomiao Liu, Mathieu Salzmann, Xuming He
Date: 2016-05-31 00:00:00
Tasks: Super-Resolution

While depth sensors are becoming increasingly popular, their spatial
resolution often remains limited. Depth super-resolution therefore emerged as a
solution to this problem. Despite much progress, state-of-the-art techniques
suffer from two drawbacks: (i) they rely on the assumption that intensity edges
coincide with depth discontinuities, which, unfortunately, is only true in
controlled environments; and (ii) they typically exploit the availability of
high-resolution training depth maps, which can often not be acquired in
practice due to the sensors' limitations. By contrast, here, we introduce an
approach to performing depth super-resolution in more challenging conditions,
such as in outdoor scenes. To this end, we first propose to exploit semantic
information to better constrain the super-resolution process. In particular, we
design a co-sparse analysis model that learns filters from joint intensity,
depth and semantic information. Furthermore, we show how low-resolution
training depth maps can be employed in our learning strategy. We demonstrate
the benefits of our approach over state-of-the-art depth super-resolution
methods on two outdoor scene datasets.
Robust Deep-Learning-Based Road-Prediction for Augmented Reality Navigation Systems
Authors: Matthias Limmer, Julian Forster, Dennis Baudach, Florian Schüle, Roland Schweiger, Hendrik P. A. Lensch
Date: 2016-05-31 00:00:00

This paper proposes an approach that predicts the road course from camera
sensors leveraging deep learning techniques. Road pixels are identified by
training a multi-scale convolutional neural network on a large number of
full-scene-labeled night-time road images including adverse weather conditions.
A framework is presented that applies the proposed approach to longer distance
road course estimation, which is the basis for an augmented reality navigation
application. In this framework long range sensor data (radar) and data from a
map database are fused with short range sensor data (camera) to produce a
precise longitudinal and lateral localization and road course estimation. The
proposed approach reliably detects roads with and without lane markings and
thus increases the robustness and availability of road course estimations and
augmented reality navigation. Evaluations on an extensive set of high precision
ground truth data taken from a differential GPS and an inertial measurement
unit show that the proposed approach reaches state-of-the-art performance
without the limitation of requiring existing lane markings.
Psychologically based Virtual-Suspect for Interrogative Interview Training
Authors: Moshe Bitan, Galit Nahari, Zvi Nisin, Ariel Roth, Sarit Kraus
Date: 2016-05-31 00:00:00

In this paper, we present a Virtual-Suspect system which can be used to train
inexperienced law enforcement personnel in interrogation strategies. The system
supports different scenario configurations based on historical data. The
responses presented by the Virtual-Suspect are selected based on the
psychological state of the suspect, which can be configured as well.
Furthermore, each interrogator's statement affects the Virtual-Suspect's
current psychological state, which may lead the interrogation in different
directions. In addition, the model takes into account the context in which the
statements are made. Experiments with 24 subjects demonstrate that the
Virtual-Suspect's behavior is similar to that of a human who plays the role of
the suspect.
A Theory of Generative ConvNet
Authors: Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu
Date: 2016-02-10 00:00:00

We show that a generative random field model, which we call generative
ConvNet, can be derived from the commonly used discriminative ConvNet, by
assuming a ConvNet for multi-category classification and assuming one of the
categories is a base category generated by a reference distribution. If we
further assume that the non-linearity in the ConvNet is Rectified Linear Unit
(ReLU) and the reference distribution is Gaussian white noise, then we obtain a
generative ConvNet model that is unique among energy-based models: The model is
piecewise Gaussian, and the means of the Gaussian pieces are defined by an
auto-encoder, where the filters in the bottom-up encoding become the basis
functions in the top-down decoding, and the binary activation variables
detected by the filters in the bottom-up convolution process become the
coefficients of the basis functions in the top-down deconvolution process. The
Langevin dynamics for sampling the generative ConvNet is driven by the
reconstruction error of this auto-encoder. The contrastive divergence learning
of the generative ConvNet reconstructs the training images by the auto-encoder.
The maximum likelihood learning algorithm can synthesize realistic natural
image patterns.
Interdependent Scheduling Games
Authors: Andres Abeliuk, Haris Aziz, Gerardo Berbeglia, Serge Gaspers, Petr Kalina, Nicholas Mattei, Dominik Peters, Paul Stursberg, Pascal Van Hentenryck, Toby Walsh
Date: 2016-05-31 00:00:00
Tasks: Scheduling

We propose a model of interdependent scheduling games in which each player
controls a set of services that they schedule independently. A player is free
to schedule his own services at any time; however, each of these services only
begins to accrue reward for the player when all predecessor services, which may
or may not be controlled by the same player, have been activated. This model,
where players have interdependent services, is motivated by the problems faced
in planning and coordinating large-scale infrastructures, e.g., restoring
electricity and gas to residents after a natural disaster or providing medical
care in a crisis when different agencies are responsible for the delivery of
staff, equipment, and medicine. We undertake a game-theoretic analysis of this
setting and in particular consider the issues of welfare maximization,
computing best responses, Nash dynamics, and existence and computation of Nash
equilibria.
A Neural Autoregressive Approach to Collaborative Filtering
Authors: Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou
Date: 2016-05-31 00:00:00
Tasks: Collaborative Filtering

This paper proposes CF-NADE, a neural autoregressive architecture for
collaborative filtering (CF) tasks, which is inspired by the Restricted
Boltzmann Machine (RBM) based CF model and the Neural Autoregressive
Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF
tasks. Then we propose to improve the model by sharing parameters between
different ratings. A factored version of CF-NADE is also proposed for better
scalability. Furthermore, we take the ordinal nature of the preferences into
consideration and propose an ordinal cost to optimize CF-NADE, which shows
superior performance. Finally, CF-NADE can be extended to a deep model, with
only moderately increased computational complexity. Experimental results show
that CF-NADE with a single hidden layer beats all previous state-of-the-art
methods on MovieLens 1M, MovieLens 10M, and Netflix datasets, and adding more
hidden layers can further improve the performance.
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
Authors: Victor Picheny, Robert B. Gramacy, Stefan M. Wild, Sebastien Le Digabel
Date: 2016-05-31 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Optimization

An augmented Lagrangian (AL) can convert a constrained optimization problem
into a sequence of simpler (e.g., unconstrained) problems, which are then
usually solved with local solvers. Recently, surrogate-based Bayesian
optimization (BO) sub-solvers have been successfully deployed in the AL
framework for a more global search in the presence of inequality constraints;
however, a drawback was that expected improvement (EI) evaluations relied on
Monte Carlo. Here we introduce an alternative slack variable AL, and show that
in this formulation the EI may be evaluated with library routines. The slack
variables furthermore facilitate equality as well as inequality constraints,
and mixtures thereof. We show how our new slack "ALBO" compares favorably to
the original. Its superiority over conventional alternatives is reinforced on
several mixed constraint examples.
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables
Authors: Hui Shen, Dehua Li, Hong Wu, Zhaoxiang Zang
Date: 2016-05-31 00:00:00
Tasks: Denoising, Variable Selection

Auto-encoders are often used as building blocks of deep network classifier to
learn feature extractors, but task-irrelevant information in the input data may
lead to bad extractors and result in poor generalization performance of the
network. In this paper,via dropping the task-irrelevant input variables the
performance of auto-encoders can be obviously improved .Specifically, an
importance-based variable selection method is proposed to aim at finding the
task-irrelevant input variables and dropping them.It firstly estimates
importance of each variable,and then drops the variables with importance value
lower than a threshold. In order to obtain better performance, the method can
be employed for each layer of stacked auto-encoders. Experimental results show
that when combined with our method the stacked denoising auto-encoders achieves
significantly improved performance on three challenging datasets.
Latent Bi-constraint SVM for Video-based Object Recognition
Authors: Yang Liu, Minh Hoai, Mang Shao, Tae-Kyun Kim
Date: 2016-05-31 00:00:00
Tasks: Object, Object Recognition

We address the task of recognizing objects from video input. This important
problem is relatively unexplored, compared with image-based object recognition.
To this end, we make the following contributions. First, we introduce two
comprehensive datasets for video-based object recognition. Second, we propose
Latent Bi-constraint SVM (LBSVM), a maximum-margin framework for video-based
object recognition. LBSVM is based on Structured-Output SVM, but extends it to
handle noisy video data and ensure consistency of the output decision
throughout time. We apply LBSVM to recognize office objects and museum
sculptures, and we demonstrate its benefits over image-based, set-based, and
other video-based object recognition.
Quantitative Analysis of Saliency Models
Authors: Flora Ponjou Tasse, Jiří Kosinka, Neil Anthony Dodgson
Date: 2016-05-31 00:00:00
Tasks: Saliency Detection

Previous saliency detection research required the reader to evaluate
performance qualitatively, based on renderings of saliency maps on a few
shapes. This qualitative approach meant it was unclear which saliency models
were better, or how well they compared to human perception. This paper provides
a quantitative evaluation framework that addresses this issue. In the first
quantitative analysis of 3D computational saliency models, we evaluate four
computational saliency models and two baseline models against ground-truth
saliency collected in previous work.
A Novel Fault Classification Scheme Based on Least Square SVM
Authors: Harishchandra Dubey, A. K. Tiwari, Nandita, P. K. Ray, S. R. Mohanty, Nand Kishor
Date: 2016-05-30 00:00:00
Tasks: Classification, General Classification

This paper presents a novel approach for fault classification and section
identification in a series compensated transmission line based on least square
support vector machine. The current signal corresponding to one-fourth of the
post fault cycle is used as input to proposed modular LS-SVM classifier. The
proposed scheme uses four binary classifier; three for selection of three
phases and fourth for ground detection. The proposed classification scheme is
found to be accurate and reliable in presence of noise as well. The simulation
results validate the efficacy of proposed scheme for accurate classification of
fault in a series compensated transmission line.
Blind Modulation Classification based on MLP and PNN
Authors: Harishchandra Dubey, Nandita, Ashutosh Kumar Tiwari
Date: 2016-05-30 00:00:00
Tasks: Classification, General Classification

In this work, a pattern recognition system is investigated for blind
automatic classification of digitally modulated communication signals. The
proposed technique is able to discriminate the type of modulation scheme which
is eventually used for demodulation followed by information extraction. The
proposed system is composed of two subsystems namely feature extraction
sub-system (FESS) and classifier sub-system (CSS). The FESS consists of
continuous wavelet transform (CWT) for feature generation and principal
component analysis (PCA) for selection of the feature subset which is rich in
discriminatory information. The CSS uses the selected features to accurately
classify the modulation class of the received signal. The proposed technique
uses probabilistic neural network (PNN) and multilayer perceptron forward
neural network (MLPFN) for comparative study of their recognition ability. PNN
have been found to perform better in terms of classification accuracy as well
as testing and training time than MLPFN. The proposed approach is robust to
presence of phase offset and additive Gaussian noise.
Evaluating Crowdsourcing Participants in the Absence of Ground-Truth
Authors: Ramanathan Subramanian, Romer Rosales, Glenn Fung, Jennifer Dy
Date: 2016-05-30 00:00:00

Given a supervised/semi-supervised learning scenario where multiple
annotators are available, we consider the problem of identification of
adversarial or unreliable annotators.
Hierarchical Variational Models
Authors: Rajesh Ranganath, Dustin Tran, David M. Blei
Date: 2015-11-07 00:00:00
Tasks: Computational Efficiency, Variational Inference

Black box variational inference allows researchers to easily prototype and
evaluate an array of models. Recent advances allow such algorithms to scale to
high dimensions. However, a central question remains: How to specify an
expressive variational distribution that maintains efficient computation? To
address this, we develop hierarchical variational models (HVMs). HVMs augment a
variational approximation with a prior on its parameters, which allows it to
capture complex structure for both discrete and continuous latent variables.
The algorithm we develop is black box, can be used for any HVM, and has the
same computational efficiency as the original approximation. We study HVMs on a
variety of deep discrete latent variable models. HVMs generalize other
expressive variational distributions and maintains higher fidelity to the
posterior.
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data
Authors: Krzysztof Chalupka, Tobias Bischoff, Pietro Perona, Frederick Eberhardt
Date: 2016-05-30 00:00:00
Tasks: Clustering

We show that the climate phenomena of El Nino and La Nina arise naturally as
states of macro-variables when our recent causal feature learning framework
(Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind
(ZW) and sea surface temperatures (SST) taken over the equatorial band of the
Pacific Ocean. The method identifies these unusual climate states on the basis
of the relation between ZW and SST patterns without any input about past
occurrences of El Nino or La Nina. The simpler alternatives of (i) clustering
the SST fields while disregarding their relationship with ZW patterns, or (ii)
clustering the joint ZW-SST patterns, do not discover El Nino. We discuss the
degree to which our method supports a causal interpretation and use a
low-dimensional toy example to explain its success over other clustering
approaches. Finally, we propose a new robust and scalable alternative to our
original algorithm (Chalupka 2016), which circumvents the need for
high-dimensional density learning.
Laser light-field fusion for wide-field lensfree on-chip phase contrast nanoscopy
Authors: Farnoud Kazemzadeh, Alexander Wong
Date: 2016-04-27 00:00:00

Wide-field lensfree on-chip microscopy, which leverages holography principles
to capture interferometric light-field encodings without lenses, is an emerging
imaging modality with widespread interest given the large field-of-view
compared to lens-based techniques. In this study, we introduce the idea of
laser light-field fusion for lensfree on-chip phase contrast nanoscopy, where
interferometric laser light-field encodings acquired using an on-chip setup
with laser pulsations at different wavelengths are fused to produce marker-free
phase contrast images of superior quality with resolving power more than five
times below the pixel pitch of the sensor array and more than 40% beyond the
diffraction limit. As a proof of concept, we demonstrate, for the first time, a
wide-field lensfree on-chip instrument successfully detecting 300 nm particles,
resulting in a numerical aperture of 1.1, across a large field-of-view of
$\sim$ 30 mm$^2$ without any specialized or intricate sample preparation, or
the use of synthetic aperture- or shift-based techniques.
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Authors: Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet K. Dokania, Simon Lacoste-Julien
Date: 2016-05-30 00:00:00
Tasks: Structured Prediction

In this paper, we propose several improvements on the block-coordinate
Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to
optimize the structured support vector machine (SSVM) objective in the context
of structured prediction, though it has wider applications. The key intuition
behind our improvements is that the estimates of block gaps maintained by BCFW
reveal the block suboptimality that can be used as an adaptive criterion.
First, we sample objects at each iteration of BCFW in an adaptive non-uniform
way via gapbased sampling. Second, we incorporate pairwise and away-step
variants of Frank-Wolfe into the block-coordinate setting. Third, we cache
oracle calls with a cache-hit criterion based on the block gaps. Fourth, we
provide the first method to compute an approximate regularization path for
SSVM. Finally, we provide an exhaustive empirical evaluation of all our methods
on four structured prediction datasets.