pwc-archive/papers-with-abstracts huggingface dataset:
Online Learning with Gaussian Payoffs and Side Observations
Authors: Yifan Wu, András György, Csaba Szepesvári
Date: 2015-10-27 00:00:00
Proceeding: NeurIPS 2015 12

We consider a sequential learning problem with Gaussian payoffs and side
information: after selecting an action $i$, the learner receives information
about the payoff of every action $j$ in the form of Gaussian observations whose
mean is the same as the mean payoff, but the variance depends on the pair
$(i,j)$ (and may be infinite). The setup allows a more refined information
transfer from one action to another than previous partial monitoring setups,
including the recently introduced graph-structured feedback case. For the first
time in the literature, we provide non-asymptotic problem-dependent lower
bounds on the regret of any algorithm, which recover existing asymptotic
problem-dependent lower bounds and finite-time minimax lower bounds available
in the literature. We also provide algorithms that achieve the
problem-dependent lower bound (up to some universal constant factor) or the
minimax lower bounds (up to logarithmic factors).
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
Authors: Weijie Su, Stephen Boyd, Emmanuel J. Candes
Date: 2015-03-04 00:00:00

We derive a second-order ordinary differential equation (ODE) which is the
limit of Nesterov's accelerated gradient method. This ODE exhibits approximate
equivalence to Nesterov's scheme and thus can serve as a tool for analysis. We
show that the continuous time ODE allows for a better understanding of
Nesterov's scheme. As a byproduct, we obtain a family of schemes with similar
convergence rates. The ODE interpretation also suggests restarting Nesterov's
scheme leading to an algorithm, which can be rigorously proven to converge at a
linear rate whenever the objective is strongly convex.
Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties
Authors: Georg Poier, Konstantinos Roditakis, Samuel Schulter, Damien Michel, Horst Bischof, Antonis A. Argyros
Date: 2015-10-27 00:00:00
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation, valid

Model-based approaches to 3D hand tracking have been shown to perform well in
a wide range of scenarios. However, they require initialisation and cannot
recover easily from tracking failures that occur due to fast hand motions.
Data-driven approaches, on the other hand, can quickly deliver a solution, but
the results often suffer from lower accuracy or missing anatomical validity
compared to those obtained from model-based approaches. In this work we propose
a hybrid approach for hand pose estimation from a single depth image. First, a
learned regressor is employed to deliver multiple initial hypotheses for the 3D
position of each hand joint. Subsequently, the kinematic parameters of a 3D
hand model are found by deliberately exploiting the inherent uncertainty of the
inferred joint proposals. This way, the method provides anatomically valid and
accurate solutions without requiring manual initialisation or suffering from
track losses. Quantitative results on several standard datasets demonstrate
that the proposed method outperforms state-of-the-art representatives of the
model-based, data-driven and hybrid paradigms.
Increasing Behavioral Complexity for Evolved Virtual Creatures with the ESP Method
Authors: Dan Lessin, Don Fussell, Risto Miikkulainen, Sebastian Risi
Date: 2015-10-27 00:00:00
Tasks: Artificial Life

Since their introduction in 1994 (Sims), evolved virtual creatures (EVCs)
have employed the coevolution of morphology and control to produce high-impact
work in multiple fields, including graphics, evolutionary computation,
robotics, and artificial life. However, in contrast to fixed-morphology
creatures, there has been no clear increase in the behavioral complexity of
EVCs in those two decades. This paper describes a method for moving beyond this
limit, making use of high-level human input in the form of a syllabus of
intermediate learning tasks--along with mechanisms for preservation, reuse, and
combination of previously learned tasks. This method--named ESP for its three
components: encapsulation, syllabus, and pandemonium--is presented in two
complementary versions: Fast ESP, which constrains later morphological changes
to achieve linear growth in computation time as behavioral complexity is added,
and General ESP, which allows this restriction to be removed when sufficient
computational resources are available. Experiments demonstrate that the ESP
method allows evolved virtual creatures to reach new levels of behavioral
complexity in the co-evolution of morphology and control, approximately
doubling the previous state of the art.
Exclusive Sparsity Norm Minimization with Random Groups via Cone Projection
Authors: Yijun Huang, Ji Liu
Date: 2015-10-27 00:00:00
Tasks: feature selection, Multi-Task Learning

Many practical applications such as gene expression analysis, multi-task
learning, image recognition, signal processing, and medical data analysis
pursue a sparse solution for the feature selection purpose and particularly
favor the nonzeros \emph{evenly} distributed in different groups. The exclusive
sparsity norm has been widely used to serve to this purpose. However, it still
lacks systematical studies for exclusive sparsity norm optimization. This paper
offers two main contributions from the optimization perspective: 1) We provide
several efficient algorithms to solve exclusive sparsity norm minimization with
either smooth loss or hinge loss (non-smooth loss). All algorithms achieve the
optimal convergence rate $O(1/k^2)$ ($k$ is the iteration number). To the best
of our knowledge, this is the first time to guarantee such convergence rate for
the general exclusive sparsity norm minimization; 2) When the group information
is unavailable to define the exclusive sparsity norm, we propose to use the
random grouping scheme to construct groups and prove that if the number of
groups is appropriately chosen, the nonzeros (true features) would be grouped
in the ideal way with high probability. Empirical studies validate the
efficiency of proposed algorithms, and the effectiveness of random grouping
scheme on the proposed exclusive SVM formulation.
Standards for language resources in ISO -- Looking back at 13 fruitful years
Authors: Laurent Romary
Date: 2015-10-27 00:00:00
Tasks: Management

This paper provides an overview of the various projects carried out within
ISO committee TC 37/SC 4 dealing with the management of language (digital)
resources. On the basis of the technical experience gained in the committee and
the wider standardization landscape the paper identifies some possible trends
for the future.
Estimation Stability with Cross Validation (ESCV)
Authors: Chinghway Lim, Bin Yu
Date: 2013-03-13 00:00:00
Tasks: parameter estimation

Cross-validation (CV) is often used to select the regularization parameter in
high dimensional problems. However, when applied to the sparse modeling method
Lasso, CV leads to models that are unstable in high-dimensions, and
consequently not suited for reliable interpretation. In this paper, we propose
a model-free criterion ESCV based on a new estimation stability (ES) metric and
CV. Our proposed ESCV finds a locally ES-optimal model smaller than the CV
choice so that the it fits the data and also enjoys estimation stability
property. We demonstrate that ESCV is an effective alternative to CV at a
similar easily parallelizable computational cost. In particular, we compare the
two approaches with respect to several performance measures when applied to the
Lasso on both simulated and real data sets. For dependent predictors common in
practice, our main finding is that, ESCV cuts down false positive rates often
by a large margin, while sacrificing little of true positive rates. ESCV
usually outperforms CV in terms of parameter estimation while giving similar
performance as CV in terms of prediction. For the two real data sets from
neuroscience and cell biology, the models found by ESCV are less than half of
the model sizes by CV. Judged based on subject knowledge, they are more
plausible than those by CV as well. We also discuss some regularization
parameter alignment issues that come up in both approaches.
Multimodal Task-Driven Dictionary Learning for Image Classification
Authors: Soheil Bahrampour, Nasser M. Nasrabadi, Asok Ray, W. Kenneth Jenkins
Date: 2015-02-04 00:00:00
Tasks: Action Recognition, Classification, Dictionary Learning, Face Recognition, General Classification, image-classification, Image Classification, Temporal Action Localization

Dictionary learning algorithms have been successfully used for both
reconstructive and discriminative tasks, where an input signal is represented
with a sparse linear combination of dictionary atoms. While these methods are
mostly developed for single-modality scenarios, recent studies have
demonstrated the advantages of feature-level fusion based on the joint sparse
representation of the multimodal inputs. In this paper, we propose a multimodal
task-driven dictionary learning algorithm under the joint sparsity constraint
(prior) to enforce collaborations among multiple homogeneous/heterogeneous
sources of information. In this task-driven formulation, the multimodal
dictionaries are learned simultaneously with their corresponding classifiers.
The resulting multimodal dictionaries can generate discriminative latent
features (sparse codes) from the data that are optimized for a given task such
as binary or multiclass classification. Moreover, we present an extension of
the proposed formulation using a mixed joint and independent sparsity prior
which facilitates more flexible fusion of the modalities at feature level. The
efficacy of the proposed algorithms for multimodal classification is
illustrated on four different applications -- multimodal face recognition,
multi-view face recognition, multi-view action recognition, and multimodal
biometric recognition. It is also shown that, compared to the counterpart
reconstructive-based dictionary learning algorithms, the task-driven
formulations are more computationally efficient in the sense that they can be
equipped with more compact dictionaries and still achieve superior performance.
Redesigning pattern mining algorithms for supercomputers
Authors: Kazuki Yoshizoe, Aika Terada, Koji Tsuda
Date: 2015-10-27 00:00:00

Upcoming many core processors are expected to employ a distributed memory
architecture similar to currently available supercomputers, but parallel
pattern mining algorithms amenable to the architecture are not comprehensively
studied. We present a novel closed pattern mining algorithm with a
well-engineered communication protocol, and generalize it to find statistically
significant patterns from personal genome data. For distributing communication
evenly, it employs global load balancing with multiple stacks distributed on a
set of cores organized as a hypercube with random edges. Our algorithm achieved
up to 1175-fold speedup by using 1200 cores for solving a problem with 11,914
items and 697 transactions, while the naive approach of separating the search
space failed completely.
An exploration of parameter redundancy in deep networks with circulant projections
Authors: Yu Cheng, Felix X. Yu, Rogerio S. Feris, Sanjiv Kumar, Alok Choudhary, Shih-Fu Chang
Date: 2015-02-11 00:00:00
Proceeding: ICCV 2015 12

We explore the redundancy of parameters in deep neural networks by replacing
the conventional linear projection in fully-connected layers with the circulant
projection. The circulant structure substantially reduces memory footprint and
enables the use of the Fast Fourier Transform to speed up the computation.
Considering a fully-connected neural network layer with d input nodes, and d
output nodes, this method improves the time complexity from O(d^2) to O(dlogd)
and space complexity from O(d^2) to O(d). The space savings are particularly
important for modern deep convolutional neural network architectures, where
fully-connected layers typically contain more than 90% of the network
parameters. We further show that the gradient computation and optimization of
the circulant projections can be performed very efficiently. Our experiments on
three standard datasets show that the proposed approach achieves this
significant gain in storage and efficiency with minimal increase in error rate
compared to neural networks with unstructured projections.
Computational models: Bottom-up and top-down aspects
Authors: Laurent Itti, Ali Borji
Date: 2015-10-27 00:00:00

Computational models of visual attention have become popular over the past
decade, we believe primarily for two reasons: First, models make testable
predictions that can be explored by experimentalists as well as theoreticians,
second, models have practical and technological applications of interest to the
applied science and engineering communities. In this chapter, we take a
critical look at recent attention modeling efforts. We focus on {\em
computational models of attention} as defined by Tsotsos \& Rothenstein
\shortcite{Tsotsos_Rothenstein11}: Models which can process any visual stimulus
(typically, an image or video clip), which can possibly also be given some task
definition, and which make predictions that can be compared to human or animal
behavioral or physiological responses elicited by the same stimulus and task.
Thus, we here place less emphasis on abstract models, phenomenological models,
purely data-driven fitting or extrapolation models, or models specifically
designed for a single task or for a restricted class of stimuli. For
theoretical models, we refer the reader to a number of previous reviews that
address attention theories and models more generally
\cite{Itti_Koch01nrn,Paletta_etal05,Frintrop_etal10,Rothenstein_Tsotsos08,Gottlieb_Balan10,Toet11,Borji_Itti12pami}.
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Authors: Qinqing Zheng, Ryota Tomioka
Date: 2015-03-18 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Denoising

We consider the problem of recovering a low-rank tensor from its noisy
observation. Previous work has shown a recovery guarantee with signal to noise
ratio $O(n^{\lceil K/2 \rceil /2})$ for recovering a $K$th order rank one
tensor of size $n\times \cdots \times n$ by recursive unfolding. In this paper,
we first improve this bound to $O(n^{K/4})$ by a much simpler approach, but
with a more careful analysis. Then we propose a new norm called the subspace
norm, which is based on the Kronecker products of factors obtained by the
proposed simple estimator. The imposed Kronecker structure allows us to show a
nearly ideal $O(\sqrt{n}+\sqrt{H^{K-1}})$ bound, in which the parameter $H$
controls the blend from the non-convex estimator to mode-wise nuclear norm
minimization. Furthermore, we empirically demonstrate that the subspace norm
achieves the nearly ideal denoising performance even with $H=O(1)$.
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
Authors: Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama
Date: 2015-10-26 00:00:00
Proceeding: NeurIPS 2015 12

We study the problem of reducing test-time acquisition costs in
classification systems. Our goal is to learn decision rules that adaptively
select sensors for each example as necessary to make a confident prediction. We
model our system as a directed acyclic graph (DAG) where internal nodes
correspond to sensor subsets and decision functions at each node choose whether
to acquire a new sensor or classify using the available measurements. This
problem can be naturally posed as an empirical risk minimization over training
data. Rather than jointly optimizing such a highly coupled and non-convex
problem over all decision nodes, we propose an efficient algorithm motivated by
dynamic programming. We learn node policies in the DAG by reducing the global
objective to a series of cost sensitive learning problems. Our approach is
computationally efficient and has proven guarantees of convergence to the
optimal system for a fixed architecture. In addition, we present an extension
to map other budgeted learning problems with large number of sensors to our DAG
architecture and demonstrate empirical performance exceeding state-of-the-art
algorithms for data composed of both few and many sensors.
Parser for Abstract Meaning Representation using Learning to Search
Authors: Sudha Rao, Yogarshi Vyas, Hal Daume III, Philip Resnik
Date: 2015-10-26 00:00:00
Tasks: Abstract Meaning Representation

We develop a novel technique to parse English sentences into Abstract Meaning
Representation (AMR) using SEARN, a Learning to Search approach, by modeling
the concept and the relation learning in a unified framework. We evaluate our
parser on multiple datasets from varied domains and show an absolute
improvement of 2% to 6% over the state-of-the-art. Additionally we show that
using the most frequent concept gives us a baseline that is stronger than the
state-of-the-art for concept prediction. We plan to release our parser for
public use.
Parallelizing MCMC with Random Partition Trees
Authors: Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
Date: 2015-06-10 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Bayesian Inference

The modern scale of data has brought new challenges to Bayesian inference. In
particular, conventional MCMC algorithms are computationally very expensive for
large data sets. A promising approach to solve this problem is embarrassingly
parallel MCMC (EP-MCMC), which first partitions the data into multiple subsets
and runs independent sampling algorithms on each subset. The subset posterior
draws are then aggregated via some combining rules to obtain the final
approximation. Existing EP-MCMC algorithms are limited by approximation
accuracy and difficulty in resampling. In this article, we propose a new
EP-MCMC algorithm PART that solves these problems. The new algorithm applies
random partition trees to combine the subset posterior draws, which is
distribution-free, easy to resample from and can adapt to multiple scales. We
provide theoretical justification and extensive experiments illustrating
empirical performance.
Generalized Regressive Motion: a Visual Cue to Collision
Authors: Krzysztof Chalupka, Michael Dickinson, Pietro Perona
Date: 2015-10-26 00:00:00

Brains and sensory systems evolved to guide motion. Central to this task is
controlling the approach to stationary obstacles and detecting moving
organisms. Looming has been proposed as the main monocular visual cue for
detecting the approach of other animals and avoiding collisions with stationary
obstacles. Elegant neural mechanisms for looming detection have been found in
the brain of insects and vertebrates. However, looming has not been analyzed in
the context of collisions between two moving animals. We propose an alternative
strategy, Generalized Regressive Motion (GRM), which is consistent with
recently observed behavior in fruit flies. Geometric analysis proves that GRM
is a reliable cue to collision among conspecifics, whereas agent-based modeling
suggests that GRM is a better cue than looming as a means to detect approach,
prevent collisions and maintain mobility.
Aggregating Deep Convolutional Features for Image Retrieval
Authors: Artem Babenko, Victor Lempitsky
Date: 2015-10-26 00:00:00
Tasks: image-classification, Image Classification, Image Retrieval, Retrieval

Several recent works have shown that image descriptors produced by deep
convolutional neural networks provide state-of-the-art performance for image
classification and retrieval problems. It has also been shown that the
activations from the convolutional layers can be interpreted as local features
describing particular image regions. These local features can be aggregated
using aggregation approaches developed for local features (e.g. Fisher
vectors), thus providing new powerful global descriptors.
  In this paper we investigate possible ways to aggregate local deep features
to produce compact global descriptors for image retrieval. First, we show that
deep features and traditional hand-engineered features have quite different
distributions of pairwise similarities, hence existing aggregation methods have
to be carefully re-evaluated. Such re-evaluation reveals that in contrast to
shallow features, the simple aggregation method based on sum pooling provides
arguably the best performance for deep convolutional features. This method is
efficient, has few parameters, and bears little risk of overfitting when e.g.
learning the PCA matrix. Overall, the new compact global descriptor improves
the state-of-the-art on four common benchmarks considerably.
A Markov Random Field and Active Contour Image Segmentation Model for Animal Spots Patterns
Authors: Alexander Gómez, German Díez, Jhony Giraldo, Augusto Salazar, Juan M. Daza
Date: 2015-10-26 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Non-intrusive biometrics of animals using images allows to analyze phenotypic
populations and individuals with patterns like stripes and spots without
affecting the studied subjects. However, non-intrusive biometrics demand a well
trained subject or the development of computer vision algorithms that ease the
identification task. In this work, an analysis of classic segmentation
approaches that require a supervised tuning of their parameters such as
threshold, adaptive threshold, histogram equalization, and saturation
correction is presented. In contrast, a general unsupervised algorithm using
Markov Random Fields (MRF) for segmentation of spots patterns is proposed.
Active contours are used to boost results using MRF output as seeds. As study
subject the Diploglossus millepunctatus lizard is used. The proposed method
achieved a maximum efficiency of $91.11\%$.
A Parallel algorithm for $\mathcal{X}$-Armed bandits
Authors: Cheng Chen, Shuang Liu, Zhihua Zhang, Wu-Jun Li
Date: 2015-10-26 00:00:00

The target of $\mathcal{X}$-armed bandit problem is to find the global
maximum of an unknown stochastic function $f$, given a finite budget of $n$
evaluations. Recently, $\mathcal{X}$-armed bandits have been widely used in
many situations. Many of these applications need to deal with large-scale data
sets. To deal with these large-scale data sets, we study a distributed setting
of $\mathcal{X}$-armed bandits, where $m$ players collaborate to find the
maximum of the unknown function. We develop a novel anytime distributed
$\mathcal{X}$-armed bandit algorithm. Compared with prior work on
$\mathcal{X}$-armed bandits, our algorithm uses a quite different searching
strategy so as to fit distributed learning scenarios. Our theoretical analysis
shows that our distributed algorithm is $m$ times faster than the classical
single-player algorithm. Moreover, the number of communication rounds of our
algorithm is only logarithmic in $mn$. The numerical results show that our
method can make effective use of every players to minimize the loss. Thus, our
distributed approach is attractive and useful.
Object Oriented Analysis using Natural Language Processing concepts: A Review
Authors: Abinash Tripathy, Santanu Kumar Rath
Date: 2015-10-26 00:00:00
Tasks: Form

The Software Development Life Cycle (SDLC) starts with eliciting requirements
of the customers in the form of Software Requirement Specification (SRS). SRS
document needed for software development is mostly written in Natural
Language(NL) convenient for the client. From the SRS document only, the class
name, its attributes and the functions incorporated in the body of the class
are traced based on pre-knowledge of analyst. The paper intends to present a
review on Object Oriented (OO) analysis using Natural Language Processing (NLP)
techniques. This analysis can be manual where domain expert helps to generate
the required diagram or automated system, where the system generates the
required diagram, from the input in the form of SRS.