pwc-archive/papers-with-abstracts huggingface dataset:
Multi-view 3D Models from Single Images with a Convolutional Network
Authors: Maxim Tatarchenko, Alexey Dosovitskiy, Thomas Brox
Date: 2015-11-20 00:00:00
Tasks: Object

We present a convolutional network capable of inferring a 3D representation
of a previously unseen object given a single image of this object. Concretely,
the network can predict an RGB image and a depth map of the object as seen from
an arbitrary view. Several of these depth maps fused together give a full point
cloud of the object. The point cloud can in turn be transformed into a surface
mesh. The network is trained on renderings of synthetic 3D models of cars and
chairs. It successfully deals with objects on cluttered background and
generates reasonable predictions for real images of cars.
Semantically Guided Depth Upsampling
Authors: Nick Schneider, Lukas Schneider, Peter Pinggera, Uwe Franke, Marc Pollefeys, Christoph Stiller
Date: 2016-08-02 00:00:00
Tasks: Edge Detection, Scene Labeling

We present a novel method for accurate and efficient up- sampling of sparse
depth data, guided by high-resolution imagery. Our approach goes beyond the use
of intensity cues only and additionally exploits object boundary cues through
structured edge detection and semantic scene labeling for guidance. Both cues
are combined within a geodesic distance measure that allows for
boundary-preserving depth in- terpolation while utilizing local context. We
model the observed scene structure by locally planar elements and formulate the
upsampling task as a global energy minimization problem. Our method determines
glob- ally consistent solutions and preserves fine details and sharp depth
bound- aries. In our experiments on several public datasets at different levels
of application, we demonstrate superior performance of our approach over the
state-of-the-art, even for very sparse measurements.
Image Restoration with Locally Selected Class-Adapted Models
Authors: Afonso M. Teodoro, José M. Bioucas-Dias, Mário A. T. Figueiredo
Date: 2016-05-23 00:00:00
Tasks: Deblurring, Denoising, Image Restoration

State-of-the-art algorithms for imaging inverse problems (namely deblurring
and reconstruction) are typically iterative, involving a denoising operation as
one of its steps. Using a state-of-the-art denoising method in this context is
not trivial, and is the focus of current work. Recently, we have proposed to
use a class-adapted denoiser (patch-based using Gaussian mixture models) in a
so-called plug-and-play scheme, wherein a state-of-the-art denoiser is plugged
into an iterative algorithm, leading to results that outperform the best
general-purpose algorithms, when applied to an image of a known class (e.g.
faces, text, brain MRI). In this paper, we extend that approach to handle
situations where the image being processed is from one of a collection of
possible classes or, more importantly, contains regions of different classes.
More specifically, we propose a method to locally select one of a set of
class-adapted Gaussian mixture patch priors, previously estimated from clean
images of those classes. Our approach may be seen as simultaneously performing
segmentation and restoration, thus contributing to bridging the gap between
image restoration/reconstruction and analysis.
Context Discovery for Model Learning in Partially Observable Environments
Authors: Nikolas J. Hemion
Date: 2016-08-02 00:00:00

The ability to learn a model is essential for the success of autonomous
agents. Unfortunately, learning a model is difficult in partially observable
environments, where latent environmental factors influence what the agent
observes. In the absence of a supervisory training signal, autonomous agents
therefore require a mechanism to autonomously discover these environmental
factors, or sensorimotor contexts.
  This paper presents a method to discover sensorimotor contexts in partially
observable environments, by constructing a hierarchical transition model. The
method is evaluated in a simulation experiment, in which a robot learns that
different rooms are characterized by different objects that are found in them.
The Configurable SAT Solver Challenge (CSSC)
Authors: Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger Hoos, Kevin Leyton-Brown
Date: 2015-05-05 00:00:00

It is well known that different solution strategies work well for different
types of instances of hard combinatorial problems. As a consequence, most
solvers for the propositional satisfiability problem (SAT) expose parameters
that allow them to be customized to a particular family of instances. In the
international SAT competition series, these parameters are ignored: solvers are
run using a single default parameter setting (supplied by the authors) for all
benchmark instances in a given track. While this competition format rewards
solvers with robust default settings, it does not reflect the situation faced
by a practitioner who only cares about performance on one particular
application and can invest some time into tuning solver parameters for this
application. The new Configurable SAT Solver Competition (CSSC) compares
solvers in this latter setting, scoring each solver by the performance it
achieved after a fully automated configuration step. This article describes the
CSSC in more detail, and reports the results obtained in its two instantiations
so far, CSSC 2013 and 2014.
Combining Answer Set Programming and Domain Heuristics for Solving Hard Industrial Problems (Application Paper)
Authors: Carmine Dodaro, Philip Gasteiger, Nicola Leone, Benjamin Musitsch, Francesco Ricca, Kostyantyn Shchekotykhin
Date: 2016-08-02 00:00:00

Answer Set Programming (ASP) is a popular logic programming paradigm that has
been applied for solving a variety of complex problems. Among the most
challenging real-world applications of ASP are two industrial problems defined
by Siemens: the Partner Units Problem (PUP) and the Combined Configuration
Problem (CCP). The hardest instances of PUP and CCP are out of reach for
state-of-the-art ASP solvers. Experiments show that the performance of ASP
solvers could be significantly improved by embedding domain-specific
heuristics, but a proper effective integration of such criteria in
off-the-shelf ASP implementations is not obvious. In this paper the combination
of ASP and domain-specific heuristics is studied with the goal of effectively
solving real-world problem instances of PUP and CCP. As a byproduct of this
activity, the ASP solver WASP was extended with an interface that eases
embedding new external heuristics in the solver. The evaluation shows that our
domain-heuristic-driven ASP solver finds solutions for all the real-world
instances of PUP and CCP ever provided by Siemens. This paper is under
consideration for acceptance in TPLP.
A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique
Authors: Rui Wang, Hai Zhao, Sabine Ploux, Bao-liang Lu, Masao Utiyama, Eiichiro Sumita
Date: 2016-07-29 00:00:00
Tasks: Dimensionality Reduction, Translation

Most of the existing methods for bilingual word embedding only consider
shallow context or simple co-occurrence information. In this paper, we propose
a latent bilingual sense unit (Bilingual Sense Clique, BSC), which is derived
from a maximum complete sub-graph of pointwise mutual information based graph
over bilingual corpus. In this way, we treat source and target words equally
and a separated bilingual projection processing that have to be used in most
existing works is not necessary any more. Several dimension reduction methods
are evaluated to summarize the BSC-word relationship. The proposed method is
evaluated on bilingual lexicon translation tasks and empirical results show
that bilingual sense embedding methods outperform existing bilingual word
embedding methods.
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks
Authors: Timothy La Fond, Jennifer Neville, Brian Gallagher
Date: 2016-08-02 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis, Two-sample testing

An important task in network analysis is the detection of anomalous events in
a network time series. These events could merely be times of interest in the
network timeline or they could be examples of malicious activity or network
malfunction. Hypothesis testing using network statistics to summarize the
behavior of the network provides a robust framework for the anomaly detection
decision process. Unfortunately, choosing network statistics that are dependent
on confounding factors like the total number of nodes or edges can lead to
incorrect conclusions (e.g., false positives and false negatives). In this
dissertation we describe the challenges that face anomaly detection in dynamic
network streams regarding confounding factors. We also provide two solutions to
avoiding error due to confounding factors: the first is a randomization testing
method that controls for confounding factors, and the second is a set of
size-consistent network statistics which avoid confounding due to the most
common factors, edge count and node count.
Can we trust the bootstrap in high-dimension?
Authors: Noureddine El Karoui, Elizabeth Purdom
Date: 2016-08-02 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

We consider the performance of the bootstrap in high-dimensions for the
setting of linear regression, where $p<n$ but $p/n$ is not close to zero. We
consider ordinary least-squares as well as robust regression methods and adopt
a minimalist performance requirement: can the bootstrap give us good confidence
intervals for a single coordinate of $\beta$? (where $\beta$ is the true
regression vector).
  We show through a mix of numerical and theoretical work that the bootstrap is
fraught with problems. Both of the most commonly used methods of bootstrapping
for regression -- residual bootstrap and pairs bootstrap -- give very poor
inference on $\beta$ as the ratio $p/n$ grows. We find that the residuals
bootstrap tend to give anti-conservative estimates (inflated Type I error),
while the pairs bootstrap gives very conservative estimates (severe loss of
power) as the ratio $p/n$ grows. We also show that the jackknife resampling
technique for estimating the variance of $\hat{\beta}$ severely overestimates
the variance in high dimensions.
  We contribute alternative bootstrap procedures based on our theoretical
results that mitigate these problems. However, the corrections depend on
assumptions regarding the underlying data-generation model, suggesting that in
high-dimensions it may be difficult to have universal, robust bootstrapping
techniques.
LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning
Authors: Chengxi Ye, Chen Zhao, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos
Date: 2016-05-09 00:00:00
Tasks: CPU, Deep Learning, GPU

LightNet is a lightweight, versatile and purely Matlab-based deep learning
framework. The idea underlying its design is to provide an easy-to-understand,
easy-to-use and efficient computational platform for deep learning research.
The implemented framework supports major deep learning architectures such as
Multilayer Perceptron Networks (MLP), Convolutional Neural Networks (CNN) and
Recurrent Neural Networks (RNN). The framework also supports both CPU and GPU
computation, and the switch between them is straightforward. Different
applications in computer vision, natural language processing and robotics are
demonstrated as experiments.
Online Nonnegative Matrix Factorization with General Divergences
Authors: Renbo Zhao, Vincent Y. F. Tan, Huan Xu
Date: 2016-07-30 00:00:00
Tasks: Computational Efficiency, Denoising, Image Denoising, Shadow Removal

We develop a unified and systematic framework for performing online
nonnegative matrix factorization under a wide variety of important divergences.
The online nature of our algorithm makes it particularly amenable to
large-scale data. We prove that the sequence of learned dictionaries converges
almost surely to the set of critical points of the expected loss function. We
do so by leveraging the theory of stochastic approximations and projected
dynamical systems. This result substantially generalizes the previous results
obtained only for the squared-$\ell_2$ loss. Moreover, the novel techniques
involved in our analysis open new avenues for analyzing similar matrix
factorization problems. The computational efficiency and the quality of the
learned dictionary of our algorithm are verified empirically on both synthetic
and real datasets. In particular, on the tasks of topic learning, shadow
removal and image denoising, our algorithm achieves superior trade-offs between
the quality of learned dictionary and running time over the batch and other
online NMF algorithms.
Spatio-temporal Co-Occurrence Characterizations for Human Action Classification
Authors: Aznul Qalid Md Sabri, Jacques Boonaert, Erma Rahayu Mohd Faizal Abdullah, Ali Mohammed Mansoor
Date: 2016-08-02 00:00:00
Tasks: Action Classification, Classification, General Classification

The human action classification task is a widely researched topic and is
still an open problem. Many state-of-the-arts approaches involve the usage of
bag-of-video-words with spatio-temporal local features to construct
characterizations for human actions. In order to improve beyond this standard
approach, we investigate the usage of co-occurrences between local features. We
propose the usage of co-occurrences information to characterize human actions.
A trade-off factor is used to define an optimal trade-off between vocabulary
size and classification rate. Next, a spatio-temporal co-occurrence technique
is applied to extract co-occurrence information between labeled local features.
Novel characterizations for human actions are then constructed. These include a
vector quantized correlogram-elements vector, a highly discriminative PCA
(Principal Components Analysis) co-occurrence vector and a Haralick texture
vector. Multi-channel kernel SVM (support vector machine) is utilized for
classification. For evaluation, the well known KTH as well as the challenging
UCF-Sports action datasets are used. We obtained state-of-the-arts
classification performance. We also demonstrated that we are able to fully
utilize co-occurrence information, and improve the standard bag-of-video-words
approach.
Global Vertices and the Noising Paradox
Authors: Konstantinos A. Raftopoulos, Stefanos D. Kollias, Marin Ferecatu
Date: 2016-08-02 00:00:00

A theoretical and experimental analysis related to the identification of
vertices of unknown shapes is presented. Shapes are seen as real functions of
their closed boundary. Unlike traditional approaches, which see curvature as
the rate of change of the tangent to the curve, an alternative global
perspective of curvature is examined providing insight into the process of
noise-enabled vertex localization. The analysis leads to a paradox, that
certain vertices can be localized better in the presence of noise. The concept
of noising is thus considered and a relevant global method for localizing
"Global Vertices" is investigated. Theoretical analysis reveals that induced
noise can help localizing certain vertices if combined with global descriptors.
Experiments with noise and a comparison to localized methods validate the
theoretical results.
Can Active Learning Experience Be Transferred?
Authors: Hong-Min Chu, Hsuan-Tien Lin
Date: 2016-08-02 00:00:00
Tasks: Active Learning

Active learning is an important machine learning problem in reducing the
human labeling effort. Current active learning strategies are designed from
human knowledge, and are applied on each dataset in an immutable manner. In
other words, experience about the usefulness of strategies cannot be updated
and transferred to improve active learning on other datasets. This paper
initiates a pioneering study on whether active learning experience can be
transferred. We first propose a novel active learning model that linearly
aggregates existing strategies. The linear weights can then be used to
represent the active learning experience. We equip the model with the popular
linear upper- confidence-bound (LinUCB) algorithm for contextual bandit to
update the weights. Finally, we extend our model to transfer the experience
across datasets with the technique of biased regularization. Empirical studies
demonstrate that the learned experience not only is competitive with existing
strategies on most single datasets, but also can be transferred across datasets
to improve the performance on future learning tasks.
A Non-Parametric Learning Approach to Identify Online Human Trafficking
Authors: Hamidreza Alvari, Paulo Shakarian, J. E. Kelly Snyder
Date: 2016-07-29 00:00:00

Human trafficking is among the most challenging law enforcement problems
which demands persistent fight against from all over the globe. In this study,
we leverage readily available data from the website "Backpage"-- used for
classified advertisement-- to discern potential patterns of human trafficking
activities which manifest online and identify most likely trafficking related
advertisements. Due to the lack of ground truth, we rely on two human analysts
--one human trafficking victim survivor and one from law enforcement, for
hand-labeling the small portion of the crawled data. We then present a
semi-supervised learning approach that is trained on the available labeled and
unlabeled data and evaluated on unseen data with further verification of
experts.
A Web-based Tool for Identifying Strategic Intervention Points in Complex Systems
Authors: Sotiris Moschoyiannis, Nicholas Elia, Alexandra S. Penn, David J. B. Lloyd, Chris Knight
Date: 2016-08-02 00:00:00
Tasks: Decision Making

Steering a complex system towards a desired outcome is a challenging task.
The lack of clarity on the system's exact architecture and the often scarce
scientific data upon which to base the operationalisation of the dynamic rules
that underpin the interactions between participant entities are two
contributing factors. We describe an analytical approach that builds on Fuzzy
Cognitive Mapping (FCM) to address the latter and represent the system as a
complex network. We apply results from network controllability to address the
former and determine minimal control configurations - subsets of factors, or
system levers, which comprise points for strategic intervention in steering the
system. We have implemented the combination of these techniques in an
analytical tool that runs in the browser, and generates all minimal control
configurations of a complex network. We demonstrate our approach by reporting
on our experience of working alongside industrial, local-government, and NGO
stakeholders in the Humber region, UK. Our results are applied to the
decision-making process involved in the transition of the region to a bio-based
economy.
Learning Transferable Policies for Monocular Reactive MAV Control
Authors: Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert
Date: 2016-08-01 00:00:00

The ability to transfer knowledge gained in previous tasks into new contexts
is one of the most important mechanisms of human learning. Despite this,
adapting autonomous behavior to be reused in partially similar settings is
still an open problem in current robotics research. In this paper, we take a
small step in this direction and propose a generic framework for learning
transferable motion policies. Our goal is to solve a learning problem in a
target domain by utilizing the training data in a different but related source
domain. We present this in the context of an autonomous MAV flight using
monocular reactive control, and demonstrate the efficacy of our proposed
approach through extensive real-world flight experiments in outdoor cluttered
environments.
Blind, Greedy, and Random: Ordinal Approximation Algorithms for Matching and Clustering
Authors: Elliot Anshelevich, Shreyas Sekar
Date: 2015-12-17 00:00:00
Tasks: Clustering

We study Matching and other related problems in a partial information setting
where the agents' utilities for being matched to other agents are hidden and
the mechanism only has access to ordinal preference information. Our model is
motivated by the fact that in many settings, agents cannot express the
numerical values of their utility for different outcomes, but are still able to
rank the outcomes in their order of preference. Specifically, we study problems
where the ground truth exists in the form of a weighted graph, and look to
design algorithms that approximate the true optimum matching using only the
preference orderings for each agent (induced by the hidden weights) as input.
If no restrictions are placed on the weights, then one cannot hope to do better
than the simple greedy algorithm, which yields a half optimal matching. Perhaps
surprisingly, we show that by imposing a little structure on the weights, we
can improve upon the trivial algorithm significantly: we design a
1.6-approximation algorithm for instances where the hidden weights obey the
metric inequality. Using our algorithms for matching as a black-box, we also
design new approximation algorithms for other closely related problems: these
include a a 3.2-approximation for the problem of clustering agents into equal
sized partitions, a 4-approximation algorithm for Densest k-subgraph, and a
2.14-approximation algorithm for Max TSP. These results are the first
non-trivial ordinal approximation algorithms for such problems, and indicate
that we can design robust algorithms even when we are agnostic to the precise
agent utilities.
Structured prediction models for RNN based sequence labeling in clinical text
Authors: Abhyuday Jagannatha, Hong Yu
Date: 2016-08-01 00:00:00
Proceeding: EMNLP 2016 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Structured Prediction

Sequence labeling is a widely used method for named entity recognition and
information extraction from unstructured natural language data. In clinical
domain one major application of sequence labeling involves extraction of
medical entities such as medication, indication, and side-effects from
Electronic Health Record narratives. Sequence labeling in this domain, presents
its own set of challenges and objectives. In this work we experimented with
various CRF based structured learning models with Recurrent Neural Networks. We
extend the previously studied LSTM-CRF models with explicit modeling of
pairwise potentials. We also propose an approximate version of skip-chain CRF
inference with RNN potentials. We use these methodologies for structured
prediction in order to improve the exact phrase detection of various medical
entities.
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition
Authors: Priyadarshini Panda, Kaushik Roy
Date: 2016-08-01 00:00:00
Tasks: image-classification, Image Classification, Overall - Test

One of the key challenges in machine learning is to design a computationally
efficient multi-class classifier while maintaining the output accuracy and
performance. In this paper, we present a tree-based classifier: Attention Tree
(ATree) for large-scale image classification that uses recursive Adaboost
training to construct a visual attention hierarchy. The proposed attention
model is inspired from the biological 'selective tuning mechanism for cortical
visual processing'. We exploit the inherent feature similarity across images in
datasets to identify the input variability and use recursive optimization
procedure, to determine data partitioning at each node, thereby, learning the
attention hierarchy. A set of binary classifiers is organized on top of the
learnt hierarchy to minimize the overall test-time complexity. The attention
model maximizes the margins for the binary classifiers for optimal decision
boundary modelling, leading to better performance at minimal complexity. The
proposed framework has been evaluated on both Caltech-256 and SUN datasets and
achieves accuracy improvement over state-of-the-art tree-based methods at
significantly lower computational cost.