pwc-archive/papers-with-abstracts huggingface dataset:
An Account of Opinion Implicatures
Authors: Janyce Wiebe, Lingjia Deng
Date: 2014-04-23 00:00:00
Tasks: Implicatures, Sentence, Sentiment Analysis

While previous sentiment analysis research has concentrated on the
interpretation of explicitly stated opinions and attitudes, this work initiates
the computational study of a type of opinion implicature (i.e.,
opinion-oriented inference) in text. This paper described a rule-based
framework for representing and analyzing opinion implicatures which we hope
will contribute to deeper automatic interpretation of subjective language. In
the course of understanding implicatures, the system recognizes implicit
sentiments (and beliefs) toward various events and entities in the sentence,
often attributed to different sources (holders) and of mixed polarities; thus,
it produces a richer interpretation than is typical in opinion analysis.
Probabilistic graphs using coupled random variables
Authors: Kenric P. Nelson, Madalina Barbu, Brian J. Scannell
Date: 2014-04-23 00:00:00
Tasks: General Classification

Neural network design has utilized flexible nonlinear processes which can
mimic biological systems, but has suffered from a lack of traceability in the
resulting network. Graphical probabilistic models ground network design in
probabilistic reasoning, but the restrictions reduce the expressive capability
of each node making network designs complex. The ability to model coupled
random variables using the calculus of nonextensive statistical mechanics
provides a neural node design incorporating nonlinear coupling between input
states while maintaining the rigor of probabilistic reasoning. A generalization
of Bayes rule using the coupled product enables a single node to model
correlation between hundreds of random variables. A coupled Markov random field
is designed for the inferencing and classification of UCI's MLR 'Multiple
Features Data Set' such that thousands of linear correlation parameters can be
replaced with a single coupling parameter with just a (3%, 4%) percent
reduction in (classification, inference) performance.
Learning Human Pose Estimation Features with Convolutional Networks
Authors: Arjun Jain, Jonathan Tompson, Mykhaylo Andriluka, Graham W. Taylor, Christoph Bregler
Date: 2013-12-27 00:00:00
Tasks: Object Recognition, Pose Estimation, speech-recognition, Speech Recognition

This paper introduces a new architecture for human pose estimation using a
multi- layer convolutional network architecture and a modified learning
technique that learns low-level features and higher-level weak spatial models.
Unconstrained human pose estimation is one of the hardest problems in computer
vision, and our new architecture and learning schema shows significant
improvement over the current state-of-the-art results. The main contribution of
this paper is showing, for the first time, that a specific variation of deep
learning is able to outperform all existing traditional architectures on this
task. The paper also discusses several lessons learned while researching
alternatives, most notably, that it is possible to learn strong low-level
feature detectors on features that might even just cover a few pixels in the
image. Higher-level spatial models improve somewhat the overall result, but to
a much lesser extent then expected. Many researchers previously argued that the
kinematic structure and top-down information is crucial for this domain, but
with our purely bottom up, and weak spatial model, we could improve other more
complicated architectures that currently produce the best results. This mirrors
what many other researchers, like those in the speech recognition, object
recognition, and other domains have experienced.
Most Correlated Arms Identification
Authors: Che-Yu Liu, Sébastien Bubeck
Date: 2014-04-23 00:00:00

We study the problem of finding the most mutually correlated arms among many
arms. We show that adaptive arms sampling strategies can have significant
advantages over the non-adaptive uniform sampling strategy. Our proposed
algorithms rely on a novel correlation estimator. The use of this accurate
estimator allows us to get improved results for a wide range of problem
instances.
Representation Learning: A Review and New Perspectives
Authors: Yoshua Bengio, Aaron Courville, Pascal Vincent
Date: 2012-06-24 00:00:00
Tasks: Density Estimation, Representation Learning

The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning.
Contractive De-noising Auto-encoder
Authors: Fu-qiang Chen, Yan Wu, Guo-dong Zhao, Jun-ming Zhang, Ming Zhu, Jing Bai
Date: 2013-05-17 00:00:00

Auto-encoder is a special kind of neural network based on reconstruction.
De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to
the input by corrupting the original data first and then reconstructing the
original input by minimizing the reconstruction error function. And contractive
auto-encoder (CAE) is another kind of improved auto-encoder to learn robust
feature by introducing the Frobenius norm of the Jacobean matrix of the learned
feature with respect to the original input. In this paper, we combine
de-noising auto-encoder and contractive auto- encoder, and propose another
improved auto-encoder, contractive de-noising auto- encoder (CDAE), which is
robust to both the original input and the learned feature. We stack CDAE to
extract more abstract features and apply SVM for classification. The experiment
result on benchmark dataset MNIST shows that our proposed CDAE performed better
than both DAE and CAE, proving the effective of our method.
Codynamic Fitness Landscapes of Coevolutionary Minimal Substrates
Authors: Hendrik Richter
Date: 2014-04-23 00:00:00

Coevolutionary minimal substrates are simple and abstract models that allow
studying the relationships and codynamics between objective and subjective
fitness. Using these models an approach is presented for defining and analyzing
fitness landscapes of coevolutionary problems. We devise similarity measures of
codynamic fitness landscapes and experimentally study minimal substrates of
test--based and compositional problems for both cooperative and competitive
interaction.
Find my mug: Efficient object search with a mobile robot using semantic segmentation
Authors: Daniel Wolf, Markus Bajones, Johann Prankl, Markus Vincze
Date: 2014-04-23 00:00:00
Tasks: Segmentation, Semantic Segmentation

In this paper, we propose an efficient semantic segmentation framework for
indoor scenes, tailored to the application on a mobile robot. Semantic
segmentation can help robots to gain a reasonable understanding of their
environment, but to reach this goal, the algorithms not only need to be
accurate, but also fast and robust. Therefore, we developed an optimized 3D
point cloud processing framework based on a Randomized Decision Forest,
achieving competitive results at sufficiently high frame rates. We evaluate the
capabilities of our method on the popular NYU depth dataset and our own data
and demonstrate its feasibility by deploying it on a mobile service robot, for
which we could optimize an object search procedure using our results.
Nested Graph Words for Object Recognition
Authors: Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret
Date: 2011-06-14 00:00:00
Tasks: Image Retrieval, Object, Object Recognition, Retrieval

In this paper, we propose a new, scalable approach for the task of object
based image search or object recognition. Despite the very large literature
existing on the scalability issues in CBIR in the sense of retrieval
approaches, the scalability of media and scalability of features remain an
issue. In our work we tackle the problem of scalability and structural
organization of features. The proposed features are nested local graphs built
upon sets of SURF feature points with Delaunay triangulation. A
Bag-of-Visual-Words (BoVW) framework is applied on these graphs, giving birth
to a Bag-of-Graph-Words representation. The nested nature of the descriptors
consists in scaling from trivial Delaunay graphs - isolated feature points - by
increasing the number of nodes layer by layer up to graphs with maximal number
of nodes. For each layer of graphs its proper visual dictionary is built. The
experiments conducted on the SIVAL data set reveal that the graph features at
different layers exhibit complementary performances on the same content. The
nested approach, the combination of all existing layers, yields significant
improvement of the object recognition performance compared to single level
approaches.
Modeling multi-stage decision optimization problems
Authors: Ronald Hochreiter
Date: 2014-04-23 00:00:00
Tasks: Management

Multi-stage optimization under uncertainty techniques can be used to solve
long-term management problems. Although many optimization modeling language
extensions as well as computational environments have been proposed, the
acceptance of this technique is generally low, due to the inherent complexity
of the modeling and solution process. In this paper a simplification to
annotate multi-stage decision problems under uncertainty is presented - this
simplification contrasts with the common approach to create an extension on top
of an existing optimization modeling language. This leads to the definition of
meta models, which can be instanced in various programming languages. An
example using the statistical computing language R is shown.
Heuristic Ternary Error-Correcting Output Codes Via Weight Optimization and Layered Clustering-Based Approach
Authors: Xiao-Lei Zhang
Date: 2013-03-08 00:00:00
Tasks: Clustering, General Classification, Genre classification, Music Genre Classification, valid

One important classifier ensemble for multiclass classification problems is
Error-Correcting Output Codes (ECOCs). It bridges multiclass problems and
binary-class classifiers by decomposing multiclass problems to a serial
binary-class problems. In this paper, we present a heuristic ternary code,
named Weight Optimization and Layered Clustering-based ECOC (WOLC-ECOC). It
starts with an arbitrary valid ECOC and iterates the following two steps until
the training risk converges. The first step, named Layered Clustering based
ECOC (LC-ECOC), constructs multiple strong classifiers on the most confusing
binary-class problem. The second step adds the new classifiers to ECOC by a
novel Optimized Weighted (OW) decoding algorithm, where the optimization
problem of the decoding is solved by the cutting plane algorithm. Technically,
LC-ECOC makes the heuristic training process not blocked by some difficult
binary-class problem. OW decoding guarantees the non-increase of the training
risk for ensuring a small code length. Results on 14 UCI datasets and a music
genre classification problem demonstrate the effectiveness of WOLC-ECOC.
An Adversarial Interpretation of Information-Theoretic Bounded Rationality
Authors: Pedro A. Ortega, Daniel D. Lee
Date: 2014-04-22 00:00:00
Tasks: Decision Making

Recently, there has been a growing interest in modeling planning with
information constraints. Accordingly, an agent maximizes a regularized expected
utility known as the free energy, where the regularizer is given by the
information divergence from a prior to a posterior policy. While this approach
can be justified in various ways, including from statistical mechanics and
information theory, it is still unclear how it relates to decision-making
against adversarial environments. This connection has previously been suggested
in work relating the free energy to risk-sensitive control and to extensive
form games. Here, we show that a single-agent free energy optimization is
equivalent to a game between the agent and an imaginary adversary. The
adversary can, by paying an exponential penalty, generate costs that diminish
the decision maker's payoffs. It turns out that the optimal strategy of the
adversary consists in choosing costs so as to render the decision maker
indifferent among its choices, which is a definining property of a Nash
equilibrium, thus tightening the connection between free energy optimization
and game theory.
Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference
Authors: Mathias Niepert, Guy Van Den Broeck
Date: 2014-01-07 00:00:00

Exchangeability is a central notion in statistics and probability theory. The
assumption that an infinite sequence of data points is exchangeable is at the
core of Bayesian statistics. However, finite exchangeability as a statistical
property that renders probabilistic inference tractable is less
well-understood. We develop a theory of finite exchangeability and its relation
to tractable probabilistic inference. The theory is complementary to that of
independence and conditional independence. We show that tractable inference in
probabilistic models with high treewidth and millions of variables can be
understood using the notion of finite (partial) exchangeability. We also show
that existing lifted inference algorithms implicitly utilize a combination of
conditional independence and partial exchangeability.
A Formal Analysis of Required Cooperation in Multi-agent Planning
Authors: Yu Zhang, Subbarao Kambhampati
Date: 2014-04-22 00:00:00

Research on multi-agent planning has been popular in recent years. While
previous research has been motivated by the understanding that, through
cooperation, multi-agent systems can achieve tasks that are unachievable by
single-agent systems, there are no formal characterizations of situations where
cooperation is required to achieve a goal, thus warranting the application of
multi-agent systems. In this paper, we provide such a formal discussion from
the planning aspect. We first show that determining whether there is required
cooperation (RC) is intractable is general. Then, by dividing the problems that
require cooperation (referred to as RC problems) into two classes -- problems
with heterogeneous and homogeneous agents, we aim to identify all the
conditions that can cause RC in these two classes. We establish that when none
of these identified conditions hold, the problem is single-agent solvable.
Furthermore, with a few assumptions, we provide an upper bound on the minimum
number of agents required for RC problems with homogeneous agents. This study
not only provides new insights into multi-agent planning, but also has many
applications. For example, in human-robot teaming, when a robot cannot achieve
a task, it may be due to RC. In such cases, the human teammate should be
informed and, consequently, coordinate with other available robots for a
solution.
Large Margin Image Set Representation and Classification
Authors: Jim Jing-Yan Wang, Majed Alzahrani, Xin Gao
Date: 2014-04-22 00:00:00
Tasks: Classification, Face Recognition, General Classification

In this paper, we propose a novel image set representation and classification
method by maximizing the margin of image sets. The margin of an image set is
defined as the difference of the distance to its nearest image set from
different classes and the distance to its nearest image set of the same class.
By modeling the image sets by using both their image samples and their affine
hull models, and maximizing the margins of the images sets, the image set
representation parameter learning problem is formulated as an minimization
problem, which is further optimized by an expectation -maximization (EM)
strategy with accelerated proximal gradient (APG) optimization in an iterative
algorithm. To classify a given test image set, we assign it to the class which
could provide the largest margin. Experiments on two applications of
video-sequence-based face recognition demonstrate that the proposed method
significantly outperforms state-of-the-art image set classification methods in
terms of both effectiveness and efficiency.
A Structural Query System for Han Characters
Authors: Matthew Skala
Date: 2014-04-22 00:00:00
Tasks: Translation

The IDSgrep structural query system for Han character dictionaries is
presented. This system includes a data model and syntax for describing the
spatial structure of Han characters using Extended Ideographic Description
Sequences (EIDSes) based on the Unicode IDS syntax; a language for querying
EIDS databases, designed to suit the needs of font developers and foreign
language learners; a bit vector index inspired by Bloom filters for faster
query operations; a freely available implementation; and format translation
from popular third-party IDS and XML character databases. Experimental results
are included, with a comparison to other software used for similar
applications.
Discrete Restricted Boltzmann Machines
Authors: Guido Montufar, Jason Morton
Date: 2013-01-15 00:00:00

We describe discrete restricted Boltzmann machines: probabilistic graphical
models with bipartite interactions between visible and hidden discrete
variables. Examples are binary restricted Boltzmann machines and discrete naive
Bayes models. We detail the inference functions and distributed representations
arising in these models in terms of configurations of projected products of
simplices and normal fans of products of simplices. We bound the number of
hidden variables, depending on the cardinalities of their state spaces, for
which these models can approximate any probability distribution on their
visible states to any given accuracy. In addition, we use algebraic methods and
coding theory to compute their dimension.
Blind Image Deblurring by Spectral Properties of Convolution Operators
Authors: Guangcan Liu, Shiyu Chang, Yi Ma
Date: 2012-09-10 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

In this paper, we study the problem of recovering a sharp version of a given
blurry image when the blur kernel is unknown. Previous methods often introduce
an image-independent regularizer (such as Gaussian or sparse priors) on the
desired blur kernel. We shall show that the blurry image itself encodes rich
information about the blur kernel. Such information can be found through
analyzing and comparing how the spectrum of an image as a convolution operator
changes before and after blurring. Our analysis leads to an effective convex
regularizer on the blur kernel which depends only on the given blurry image. We
show that the minimizer of this regularizer guarantees to give good
approximation to the blur kernel if the original image is sharp enough. By
combining this powerful regularizer with conventional image deblurring
techniques, we show how we could significantly improve the deblurring results
through simulations and experiments on real images. In addition, our analysis
and experiments help explaining a widely accepted doctrine; that is, the edges
are good features for deblurring.
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
Authors: Quan Wang, Dijia Wu, Le Lu, Meizhu Liu, Kim L. Boyer, Shaohua Kevin Zhou
Date: 2013-07-11 00:00:00
Tasks: 3D Medical Imaging Segmentation, Segmentation

The automatic segmentation of human knee cartilage from 3D MR images is a
useful yet challenging task due to the thin sheet structure of the cartilage
with diffuse boundaries and inhomogeneous intensities. In this paper, we
present an iterative multi-class learning method to segment the femoral, tibial
and patellar cartilage simultaneously, which effectively exploits the spatial
contextual constraints between bone and cartilage, and also between different
cartilages. First, based on the fact that the cartilage grows in only certain
area of the corresponding bone surface, we extract the distance features of not
only to the surface of the bone, but more informatively, to the densely
registered anatomical landmarks on the bone surface. Second, we introduce a set
of iterative discriminative classifiers that at each iteration, probability
comparison features are constructed from the class confidence maps derived by
previously learned classifiers. These features automatically embed the semantic
context information between different cartilages of interest. Validated on a
total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the
proposed approach demonstrates high robustness and accuracy of segmentation in
comparison with existing state-of-the-art MR cartilage segmentation methods.
Hybrid Genetic Algorithm for Cloud Computing Applications
Authors: Saeed Javanmardi, Mohammad Shojafar, Danilo Amendola, Nicola Cordeschi, Hongbo Liu, Ajith Abraham
Date: 2014-04-22 00:00:00
Tasks: Cloud Computing, Scheduling

In this paper with the aid of genetic algorithm and fuzzy theory, we present
a hybrid job scheduling approach, which considers the load balancing of the
system and reduces total execution time and execution cost. We try to modify
the standard Genetic algorithm and to reduce the iteration of creating
population with the aid of fuzzy theory. The main goal of this research is to
assign the jobs to the resources with considering the VM MIPS and length of
jobs. The new algorithm assigns the jobs to the resources with considering the
job length and resources capacities. We evaluate the performance of our
approach with some famous cloud scheduling models. The results of the
experiments show the efficiency of the proposed approach in term of execution
time, execution cost and average Degree of Imbalance (DI).
Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses
Authors: Tanmay Sinha
Date: 2014-04-22 00:00:00

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for
e-learning by providing students with global exposure and opportunities for
connecting and interacting with millions of people all around the world. Very
often, students work as teams to effectively accomplish course related tasks.
However, due to lack of face to face interaction, it becomes difficult for MOOC
students to collaborate. Additionally, the instructor also faces challenges in
manually organizing students into teams because students flock to these MOOCs
in huge numbers. Thus, the proposed research is aimed at developing a robust
methodology for dynamic team formation in MOOCs, the theoretical framework for
which is grounded at the confluence of organizational team theory, social
network analysis and machine learning. A prerequisite for such an undertaking
is that we understand the fact that, each and every informal tie established
among students offers the opportunities to influence and be influenced.
Therefore, we aim to extract value from the inherent connectedness of students
in the MOOC. These connections carry with them radical implications for the way
students understand each other in the networked learning community. Our
approach will enable course instructors to automatically group students in
teams that have fairly balanced social connections with their peers, well
defined in terms of appropriately selected qualitative and quantitative network
metrics.