pwc-archive/papers-with-abstracts huggingface dataset:
Approximation and Heuristic Algorithms for Probabilistic Physical Search on General Graphs
Authors: Noam Hazon, Mira Gonen, Max Kleb
Date: 2015-09-27 00:00:00

We consider an agent seeking to obtain an item, potentially available at
different locations in a physical environment. The traveling costs between
locations are known in advance, but there is only probabilistic knowledge
regarding the possible prices of the item at any given location. Given such a
setting, the problem is to find a plan that maximizes the probability of
acquiring the good while minimizing both travel and purchase costs. Sample
applications include agents in search-and-rescue or exploration missions, e.g.,
a rover on Mars seeking to mine a specific mineral. These probabilistic
physical search problems have been previously studied, but we present the first
approximation and heuristic algorithms for solving such problems on general
graphs. We establish an interesting connection between these problems and
classical graph-search problems, which led us to provide the approximation
algorithms and hardness of approximation results for our settings. We further
suggest several heuristics for practical use, and demonstrate their
effectiveness with simulation on real graph structure and synthetic graphs.
Optimal Release Time Decision from Fuzzy Mathematical Programming Perspective
Authors: Arvind Kumar, Adarsh Anand, Pankaj Kumar Garg, Mohini Agarwal
Date: 2015-09-27 00:00:00
Tasks: Prediction

Demand for high software reliability requires rigorous testing followed by
requirement of robust modeling techniques for software quality prediction. On
one side, firms have to steadily manage the reliability by testing it
vigorously, the optimal release time determination is their biggest concern. In
past many models have been developed and much research has been devoted towards
assessment of release time of software. However, majority of the work deals in
crisp study. This paper addresses the problem of release time prediction using
fuzzy Logic. Here we have formulated a Fuzzy release time problem considering
the cost of testing under the impact of warranty period. Results show that
fuzzy model has good adaptability.
Learning to track for spatio-temporal action localization
Authors: Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid
Date: 2015-06-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Localization, Spatio-Temporal Action Localization, Temporal Action Localization, Temporal Localization

We propose an effective approach for spatio-temporal action localization in
realistic videos. The approach first detects proposals at the frame-level and
scores them with a combination of static and motion CNN features. It then
tracks high-scoring proposals throughout the video using a
tracking-by-detection approach. Our tracker relies simultaneously on
instance-level and class-level detectors. The tracks are scored using a
spatio-temporal motion histogram, a descriptor at the track level, in
combination with the CNN features. Finally, we perform temporal localization of
the action using a sliding-window approach at the track level. We present
experimental results for spatio-temporal localization on the UCF-Sports, J-HMDB
and UCF-101 action localization datasets, where our approach outperforms the
state of the art with a margin of 15%, 7% and 12% respectively in mAP.
Non-asymptotic Analysis of $\ell_1$-norm Support Vector Machines
Authors: Anton Kolleck, Jan Vybíral
Date: 2015-09-27 00:00:00
Tasks: Classification, General Classification

Support Vector Machines (SVM) with $\ell_1$ penalty became a standard tool in
analysis of highdimensional classification problems with sparsity constraints
in many applications including bioinformatics and signal processing. Although
SVM have been studied intensively in the literature, this paper has to our
knowledge first non-asymptotic results on the performance of $\ell_1$-SVM in
identification of sparse classifiers. We show that a $d$-dimensional $s$-sparse
classification vector can be (with high probability) well approximated from
only $O(s\log(d))$ Gaussian trials. The methods used in the proof include
concentration of measure and probability in Banach spaces.
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
Authors: Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi
Date: 2015-09-27 00:00:00
Proceeding: ICCV 2015 12
Tasks: Natural Language Understanding, Object Recognition, Semantic Segmentation, Visual Entailment

We introduce Segment-Phrase Table (SPT), a large collection of bijective
associations between textual phrases and their corresponding segmentations.
Leveraging recent progress in object recognition and natural language
semantics, we show how we can successfully build a high-quality segment-phrase
table using minimal human supervision. More importantly, we demonstrate the
unique value unleashed by this rich bimodal resource, for both vision as well
as natural language understanding. First, we show that fine-grained textual
labels facilitate contextual reasoning that helps in satisfying semantic
constraints across image segments. This feature enables us to achieve
state-of-the-art segmentation results on benchmark datasets. Next, we show that
the association of high-quality segmentations to textual phrases aids in richer
semantic understanding and reasoning of these textual phrases. Leveraging this
feature, we motivate the problem of visual entailment and visual paraphrasing,
and demonstrate its utility on a large dataset.
Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation
Authors: Tianfu Wu, Bo Li, Song-Chun Zhu
Date: 2015-01-29 00:00:00
Tasks: Viewpoint Estimation

This paper presents a method for learning And-Or models to represent context
and occlusion for car detection and viewpoint estimation. The learned And-Or
model represents car-to-car context and occlusion configurations at three
levels: (i) spatially-aligned cars, (ii) single car under different occlusion
configurations, and (iii) a small number of parts. The And-Or model embeds a
grammar for representing large structural and appearance variations in a
reconfigurable hierarchy. The learning process consists of two stages in a
weakly supervised way (i.e., only bounding boxes of single cars are annotated).
Firstly, the structure of the And-Or model is learned with three components:
(a) mining multi-car contextual patterns based on layouts of annotated single
car bounding boxes, (b) mining occlusion configurations between single cars,
and (c) learning different combinations of part visibility based on car 3D CAD
simulation. The And-Or model is organized in a directed and acyclic graph which
can be inferred by Dynamic Programming. Secondly, the model parameters (for
appearance, deformation and bias) are jointly trained using Weak-Label
Structural SVM. In experiments, we test our model on four car detection
datasets --- the KITTI dataset \cite{Geiger12}, the PASCAL VOC2007 car
dataset~\cite{pascal}, and two self-collected car datasets, namely the
Street-Parking car dataset and the Parking-Lot car dataset, and three datasets
for car viewpoint estimation --- the PASCAL VOC2006 car dataset~\cite{pascal},
the 3D car dataset~\cite{savarese}, and the PASCAL3D+ car
dataset~\cite{xiang_wacv14}. Compared with state-of-the-art variants of
deformable part-based models and other methods, our model achieves significant
improvement consistently on the four detection datasets, and comparable
performance on car viewpoint estimation.
End-to-End Text-Dependent Speaker Verification
Authors: Georg Heigold, Ignacio Moreno, Samy Bengio, Noam Shazeer
Date: 2015-09-27 00:00:00
Tasks: Speaker Verification, Text-Dependent Speaker Verification

In this paper we present a data-driven, integrated approach to speaker
verification, which maps a test utterance and a few reference utterances
directly to a single score for verification and jointly optimizes the system's
components using the same evaluation protocol and metric as at test time. Such
an approach will result in simple and efficient systems, requiring little
domain-specific knowledge and making few model assumptions. We implement the
idea by formulating the problem as a single neural network architecture,
including the estimation of a speaker model on only a few utterances, and
evaluate it on our internal "Ok Google" benchmark for text-dependent speaker
verification. The proposed approach appears to be very effective for big data
applications like ours that require highly accurate, easy-to-maintain systems
with a small footprint.
A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces
Authors: Yi Chen, Enyi Yao, Arindam Basu
Date: 2015-09-22 00:00:00
Tasks: Decoder, General Classification

Currently, state-of-the-art motor intention decoding algorithms in
brain-machine interfaces are mostly implemented on a PC and consume significant
amount of power. A machine learning co-processor in 0.35um CMOS for motor
intention decoding in brain-machine interfaces is presented in this paper.
Using Extreme Learning Machine algorithm and low-power analog processing, it
achieves an energy efficiency of 290 GMACs/W at a classification rate of 50 Hz.
The learning in second stage and corresponding digitally stored coefficients
are used to increase robustness of the core analog processor. The chip is
verified with neural data recorded in monkey finger movements experiment,
achieving a decoding accuracy of 99.3% for movement type. The same co-processor
is also used to decode time of movement from asynchronous neural spikes. With
time-delayed feature dimension enhancement, the classification accuracy can be
increased by 5% with limited number of input channels. Further, a sparsity
promoting training scheme enables reduction of number of programmable weights
by ~2X.
Deep Trans-layer Unsupervised Networks for Representation Learning
Authors: Wentao Zhu, Jun Miao, Laiyun Qing, Xilin Chen
Date: 2015-09-27 00:00:00
Tasks: Face Verification, Object Recognition, Representation Learning

Learning features from massive unlabelled data is a vast prevalent topic for
high-level tasks in many machine learning applications. The recent great
improvements on benchmark data sets achieved by increasingly complex
unsupervised learning methods and deep learning models with lots of parameters
usually requires many tedious tricks and much expertise to tune. However,
filters learned by these complex architectures are quite similar to standard
hand-crafted features visually. In this paper, unsupervised learning methods,
such as PCA or auto-encoder, are employed as the building block to learn filter
banks at each layer. The lower layer responses are transferred to the last
layer (trans-layer) to form a more complete representation retaining more
information. In addition, some beneficial methods such as local contrast
normalization and whitening are added to the proposed deep trans-layer networks
to further boost performance. The trans-layer representations are followed by
block histograms with binary encoder schema to learn translation and rotation
invariant representations, which are utilized to do high-level tasks such as
recognition and classification. Compared to traditional deep learning methods,
the implemented feature learning method has much less parameters and is
validated in several typical experiments, such as digit recognition on MNIST
and MNIST variations, object recognition on Caltech 101 dataset and face
verification on LFW dataset. The deep trans-layer unsupervised learning
achieves 99.45% accuracy on MNIST dataset, 67.11% accuracy on 15 samples per
class and 75.98% accuracy on 30 samples per class on Caltech 101 dataset,
87.10% on LFW dataset.
DeepBox: Learning Objectness with Convolutional Networks
Authors: Wei-cheng Kuo, Bharath Hariharan, Jitendra Malik
Date: 2015-05-08 00:00:00
Proceeding: ICCV 2015 12

Existing object proposal approaches use primarily bottom-up cues to rank
proposals, while we believe that objectness is in fact a high level construct.
We argue for a data-driven, semantic approach for ranking object proposals. Our
framework, which we call DeepBox, uses convolutional neural networks (CNNs) to
rerank proposals from a bottom-up method. We use a novel four-layer CNN
architecture that is as good as much larger networks on the task of evaluating
objectness while being much faster. We show that DeepBox significantly improves
over the bottom-up ranking, achieving the same recall with 500 proposals as
achieved by bottom-up methods with 2000. This improvement generalizes to
categories the CNN has never seen before and leads to a 4.5-point gain in
detection mAP. Our implementation achieves this performance while running at
260 ms per image.
Warping Peirce Quincuncial Panoramas
Authors: Chamberlain Fong, Brian K. Vogel
Date: 2010-11-14 00:00:00

The Peirce quincuncial projection is a mapping of the surface of a sphere to
the interior of a square. It is a conformal map except for four points on the
equator. These points of non-conformality cause significant artifacts in
photographic applications. In this paper, we propose an algorithm and
user-interface to mitigate these artifacts. Moreover, in order to facilitate an
interactive user-interface, we present a fast algorithm for calculating the
Peirce quincuncial projection of spherical imagery. We then promote the Peirce
quincuncial projection as a viable alternative to the more popular
stereographic projection in some scenarios.
Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes
Authors: Anders Ellern Bilgrau, Carel F. W. Peeters, Poul Svante Eriksen, Martin Bøgsted, Wessel N. van Wieringen
Date: 2015-09-26 00:00:00

We consider the problem of jointly estimating multiple inverse covariance matrices from high-dimensional data consisting of distinct classes. An $\ell_2$-penalized maximum likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other $\ell_2$-penalized estimators as special cases. In addition, the approach allows specification of target matrices through which prior knowledge may be incorporated and which can stabilize the estimation procedure in high-dimensional settings. The result is a targeted fused ridge estimator that is of use when the precision matrices of the constituent classes are believed to chiefly share the same structure while potentially differing in a number of locations of interest. It has many applications in (multi)factorial study designs. We focus on the graphical interpretation of precision matrices with the proposed estimator then serving as a basis for integrative or meta-analytic Gaussian graphical modeling. Situations are considered in which the classes are defined by data sets and subtypes of diseases. The performance of the proposed estimator in the graphical modeling setting is assessed through extensive simulation experiments. Its practical usability is illustrated by the differential network modeling of 12 large-scale gene expression data sets of diffuse large B-cell lymphoma subtypes. The estimator and its related procedures are incorporated into the R-package rags2ridges.
Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring
Authors: Yogesh Girdhar, Gregory Dudek
Date: 2015-09-26 00:00:00

This paper presents a novel approach to modeling curiosity in a mobile robot,
which is useful for monitoring and adaptive data collection tasks, especially
in the context of long term autonomous missions where pre-programmed missions
are likely to have limited utility. We use a realtime topic modeling technique
to build a semantic perception model of the environment, using which, we plan a
path through the locations in the world with high semantic information content.
The life-long learning behavior of the proposed perception model makes it
suitable for long-term exploration missions. We validate the approach using
simulated exploration experiments using aerial and underwater data, and
demonstrate an implementation on the Aqua underwater robot in a variety of
scenarios. We find that the proposed exploration paths that are biased towards
locations with high topic perplexity, produce better terrain models with high
discriminative power. Moreover, we show that the proposed algorithm implemented
on Aqua robot is able to do tasks such as coral reef inspection, diver
following, and sea floor exploration, without any prior training or
preparation.
A Bayesian Approach to Sparse plus Low rank Network Identification
Authors: Mattia Zorzi, Alessandro Chiuso
Date: 2015-03-25 00:00:00
Tasks: Network Identification, regression, Time Series, Time Series Analysis

We consider the problem of modeling multivariate time series with
parsimonious dynamical models which can be represented as sparse dynamic
Bayesian networks with few latent nodes. This structure translates into a
sparse plus low rank model. In this paper, we propose a Gaussian regression
approach to identify such a model.
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Authors: Donggeun Yoo, Sunggyun Park, Joon-Young Lee, Anthony S. Paek, In So Kweon
Date: 2015-06-25 00:00:00
Proceeding: ICCV 2015 12
Tasks: General Classification, Human Detection, Object, object-detection, Object Detection, regression

We present a novel detection method using a deep convolutional neural network
(CNN), named AttentionNet. We cast an object detection problem as an iterative
classification problem, which is the most suitable form of a CNN. AttentionNet
provides quantized weak directions pointing a target object and the ensemble of
iterative predictions from AttentionNet converges to an accurate object
boundary box. Since AttentionNet is a unified network for object detection, it
detects objects without any separated models from the object proposal to the
post bounding-box regression. We evaluate AttentionNet by a human detection
task and achieve the state-of-the-art performance of 65% (AP) on PASCAL VOC
2007/2012 with an 8-layered architecture only.
A Revisit of Infinite Population Models for Evolutionary Algorithms on Continuous Optimization Problems
Authors: Bo Song, Victor O. K. Li
Date: 2015-09-26 00:00:00
Tasks: Evolutionary Algorithms

Infinite population models are important tools for studying population
dynamics of evolutionary algorithms. They describe how the distributions of
populations change between consecutive generations. In general, infinite
population models are derived from Markov chains by exploiting symmetries
between individuals in the population and analyzing the limit as the population
size goes to infinity. In this paper, we study the theoretical foundations of
infinite population models of evolutionary algorithms on continuous
optimization problems. First, we show that the convergence proofs in a widely
cited study were in fact problematic and incomplete. We further show that the
modeling assumption of exchangeability of individuals cannot yield the
transition equation. Then, in order to analyze infinite population models, we
build an analytical framework based on convergence in distribution of random
elements which take values in the metric space of infinite sequences. The
framework is concise and mathematically rigorous. It also provides an
infrastructure for studying the convergence of the stacking of operators and of
iterating the algorithm which previous studies failed to address. Finally, we
use the framework to prove the convergence of infinite population models for
the mutation operator and the $k$-ary recombination operator. We show that
these operators can provide accurate predictions for real population dynamics
as the population size goes to infinity, provided that the initial population
is identically and independently distributed.
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Authors: Chenyi Chen, Ari Seff, Alain Kornhauser, Jianxiong Xiao
Date: 2015-05-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Autonomous Driving

Today, there are two major paradigms for vision-based autonomous driving
systems: mediated perception approaches that parse an entire scene to make a
driving decision, and behavior reflex approaches that directly map an input
image to a driving action by a regressor. In this paper, we propose a third
paradigm: a direct perception approach to estimate the affordance for driving.
We propose to map an input image to a small number of key perception indicators
that directly relate to the affordance of a road/traffic state for driving. Our
representation provides a set of compact yet complete descriptions of the scene
to enable a simple controller to drive autonomously. Falling in between the two
extremes of mediated perception and behavior reflex, we argue that our direct
perception representation provides the right level of abstraction. To
demonstrate this, we train a deep Convolutional Neural Network using recording
from 12 hours of human driving in a video game and show that our model can work
well to drive a car in a very diverse set of virtual environments. We also
train a model for car distance estimation on the KITTI dataset. Results show
that our direct perception approach can generalize well to real driving images.
Source code and data are available on our project website.
A prototype Malayalam to Sign Language Automatic Translator
Authors: Jestin Joy, Kannan Balakrishnan
Date: 2014-12-23 00:00:00
Tasks: Sign Language Translation, Translation

Sign language, which is a medium of communication for deaf people, uses
manual communication and body language to convey meaning, as opposed to using
sound. This paper presents a prototype Malayalam text to sign language
translation system. The proposed system takes Malayalam text as input and
generates corresponding Sign Language. Output animation is rendered using a
computer generated model. This system will help to disseminate information to
the deaf people in public utility places like railways, banks, hospitals etc.
This will also act as an educational tool in learning Sign Language.
Super-Resolution Off the Grid
Authors: Qingqing Huang, Sham M. Kakade
Date: 2015-09-26 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Astronomy, Super-Resolution

Super-resolution is the problem of recovering a superposition of point
sources using bandlimited measurements, which may be corrupted with noise. This
signal processing problem arises in numerous imaging problems, ranging from
astronomy to biology to spectroscopy, where it is common to take (coarse)
Fourier measurements of an object. Of particular interest is in obtaining
estimation procedures which are robust to noise, with the following desirable
statistical and computational properties: we seek to use coarse Fourier
measurements (bounded by some cutoff frequency); we hope to take a
(quantifiably) small number of measurements; we desire our algorithm to run
quickly.
  Suppose we have k point sources in d dimensions, where the points are
separated by at least \Delta from each other (in Euclidean distance). This work
provides an algorithm with the following favorable guarantees: - The algorithm
uses Fourier measurements, whose frequencies are bounded by O(1/\Delta) (up to
log factors). Previous algorithms require a cutoff frequency which may be as
large as {\Omega}( d/\Delta). - The number of measurements taken by and the
computational complexity of our algorithm are bounded by a polynomial in both
the number of points k and the dimension d, with no dependence on the
separation \Delta. In contrast, previous algorithms depended inverse
polynomially on the minimal separation and exponentially on the dimension for
both of these quantities.
  Our estimation procedure itself is simple: we take random bandlimited
measurements (as opposed to taking an exponential number of measurements on the
hyper-grid). Furthermore, our analysis and algorithm are elementary (based on
concentration bounds for sampling and the singular value decomposition).