pwc-archive/papers-with-abstracts huggingface dataset:
Structure Preserving Large Imagery Reconstruction
Authors: Ju Shen, Jianjun Yang, Sami Taha-abusneineh, Bryson Payne, Markus Hitz
Date: 2014-09-13 00:00:00
Tasks: 3D Scene Reconstruction, Clustering, Image Clustering, Retrieval, Texture Synthesis

With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques.
An Approach to Reducing Annotation Costs for BioNLP
Authors: Michael Bloodgood, K. Vijay-Shanker
Date: 2014-09-12 00:00:00
Tasks: Active Learning, Binary Classification, General Classification

There is a broad range of BioNLP tasks for which active learning (AL) can
significantly reduce annotation costs and a specific AL algorithm we have
developed is particularly effective in reducing annotation costs for these
tasks. We have previously developed an AL algorithm called ClosestInitPA that
works best with tasks that have the following characteristics: redundancy in
training material, burdensome annotation costs, Support Vector Machines (SVMs)
work well for the task, and imbalanced datasets (i.e. when set up as a binary
classification problem, one class is substantially rarer than the other). Many
BioNLP tasks have these characteristics and thus our AL algorithm is a natural
approach to apply to BioNLP tasks.
Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm
Authors: M. Emre Celebi, Hassan A. Kingravi
Date: 2014-09-12 00:00:00
Tasks: Clustering

Over the past five decades, k-means has become the clustering algorithm of
choice in many application domains primarily due to its simplicity, time/space
efficiency, and invariance to the ordering of the data points. Unfortunately,
the algorithm's sensitivity to the initial selection of the cluster centers
remains to be its most serious drawback. Numerous initialization methods have
been proposed to address this drawback. Many of these methods, however, have
time complexity superlinear in the number of data points, which makes them
impractical for large data sets. On the other hand, linear methods are often
random and/or sensitive to the ordering of the data points. These methods are
generally unreliable in that the quality of their results is unpredictable.
Therefore, it is common practice to perform multiple runs of such methods and
take the output of the run that produces the best results. Such a practice,
however, greatly increases the computational requirements of the otherwise
highly efficient k-means algorithm. In this chapter, we investigate the
empirical performance of six linear, deterministic (non-random), and
order-invariant k-means initialization methods on a large and diverse
collection of data sets from the UCI Machine Learning Repository. The results
demonstrate that two relatively unknown hierarchical initialization methods due
to Su and Dy outperform the remaining four methods with respect to two
objective effectiveness criteria. In addition, a recent method due to Erisoglu
et al. performs surprisingly poorly.
Incorporating Semi-supervised Features into Discontinuous Easy-First Constituent Parsing
Authors: Yannick Versley
Date: 2014-09-12 00:00:00

This paper describes adaptations for EaFi, a parser for easy-first parsing of
discontinuous constituents, to adapt it to multiple languages as well as make
use of the unlabeled data that was provided as part of the SPMRL shared task
2014.
e-Valuate: A Two-player Game on Arithmetic Expressions -- An Update
Authors: Sarang Aravamuthan, Biswajit Ganguly
Date: 2012-02-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

e-Valuate is a game on arithmetic expressions. The players have contrasting
roles of maximizing and minimizing the given expression. The maximizer proposes
values and the minimizer substitutes them for variables of his choice. When the
expression is fully instantiated, its value is compared with a certain minimax
value that would result if the players played to their optimal strategies. The
winner is declared based on this comparison.
  We use a game tree to represent the state of the game and show how the
minimax value can be computed efficiently using backward induction and
alpha-beta pruning. The efficacy of alpha-beta pruning depends on the order in
which the nodes are evaluated. Further improvements can be obtained by using
transposition tables to prevent reevaluation of the same nodes. We propose a
heuristic for node ordering. We show how the use of the heuristic and
transposition tables lead to improved performance by comparing the number of
nodes pruned by each method.
  We describe some domain-specific variants of this game. The first is a graph
theoretic formulation wherein two players share a set of elements of a graph by
coloring a related set with each player looking to maximize his share. The set
being shared could be either the set of vertices, edges or faces (for a planar
graph). An application of this is the sharing of regions enclosed by a planar
graph where each player's aim is to maximize the area of his share. Another
variant is a tiling game where the players alternately place dominoes on a $8
\times 8$ checkerboard to construct a maximal partial tiling. We show that the
size of the tiling $x$ satisfies $22 \le x \le 32$ by proving that any maximal
partial tiling requires at least $22$ dominoes.
A Fast Quartet Tree Heuristic for Hierarchical Clustering
Authors: Rudi L. Cilibrasi, Paul M. B. Vitanyi
Date: 2014-09-12 00:00:00
Tasks: Clustering, global-optimization

The Minimum Quartet Tree Cost problem is to construct an optimal weight tree
from the $3{n \choose 4}$ weighted quartet topologies on $n$ objects, where
optimality means that the summed weight of the embedded quartet topologies is
optimal (so it can be the case that the optimal tree embeds all quartets as
nonoptimal topologies). We present a Monte Carlo heuristic, based on randomized
hill climbing, for approximating the optimal weight tree, given the quartet
topology weights. The method repeatedly transforms a dendrogram, with all
objects involved as leaves, achieving a monotonic approximation to the exact
single globally optimal tree. The problem and the solution heuristic has been
extensively used for general hierarchical clustering of nontree-like
(non-phylogeny) data in various domains and across domains with heterogeneous
data. We also present a greatly improved heuristic, reducing the running time
by a factor of order a thousand to ten thousand. All this is implemented and
available, as part of the CompLearn package. We compare performance and running
time of the original and improved versions with those of UPGMA, BioNJ, and NJ,
as implemented in the SplitsTree package on genomic data for which the latter
are optimized.
  Keywords: Data and knowledge visualization, Pattern
matching--Clustering--Algorithms/Similarity measures, Hierarchical clustering,
Global optimization, Quartet tree, Randomized hill-climbing,
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
Authors: Sang-Yun Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam
Date: 2014-09-12 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: BIG-bench Machine Learning, Model Selection

Sparse high dimensional graphical model selection is a popular topic in
contemporary machine learning. To this end, various useful approaches have been
proposed in the context of $\ell_1$-penalized estimation in the Gaussian
framework. Though many of these inverse covariance estimation approaches are
demonstrably scalable and have leveraged recent advances in convex
optimization, they still depend on the Gaussian functional form. To address
this gap, a convex pseudo-likelihood based partial correlation graph estimation
method (CONCORD) has been recently proposed. This method uses coordinate-wise
minimization of a regression based pseudo-likelihood, and has been shown to
have robust model selection properties in comparison with the Gaussian
approach. In direct contrast to the parallel work in the Gaussian setting
however, this new convex pseudo-likelihood framework has not leveraged the
extensive array of methods that have been proposed in the machine learning
literature for convex optimization. In this paper, we address this crucial gap
by proposing two proximal gradient methods (CONCORD-ISTA and CONCORD-FISTA) for
performing $\ell_1$-regularized inverse covariance matrix estimation in the
pseudo-likelihood framework. We present timing comparisons with coordinate-wise
minimization and demonstrate that our approach yields tremendous payoffs for
$\ell_1$-penalized partial correlation graph estimation outside the Gaussian
setting, thus yielding the fastest and most scalable approach for such
problems. We undertake a theoretical analysis of our approach and rigorously
demonstrate convergence, and also derive rates thereof.
Probabilistic Selection in AgentSpeak(L)
Authors: Francisco Coelho, Vitor Nogueira
Date: 2014-09-12 00:00:00
Tasks: BIG-bench Machine Learning

Agent programming is mostly a symbolic discipline and, as such, draws little
benefits from probabilistic areas as machine learning and graphical models.
However, the greatest objective of agent research is the achievement of
autonomy in dynamical and complex environments --- a goal that implies
embracing uncertainty and therefore the entailed representations, algorithms
and techniques. This paper proposes an innovative and conflict free two layer
approach to agent programming that uses already established methods and tools
from both symbolic and probabilistic artificial intelligence. Moreover, this
framework is illustrated by means of a widely used agent programming example,
GoldMiners.
Time-domain multiscale shape identification in electro-sensing
Authors: Habib Ammari, Han Wang
Date: 2014-09-12 00:00:00

This paper presents premier and innovative time-domain multi-scale method for
shape identification in electro-sensing using pulse-type signals. The method is
based on transform-invariant shape descriptors computed from filtered
polarization tensors at multi-scales. The proposed algorithm enjoys a
remarkable noise robustness even with far-field measurements at very limited
angle of view. It opens a door for pulsed imaging using echolocation and
induction data.
An OvS-MultiObjective Algorithm Approach for Lane Reversal Problem
Authors: Enrique Gabriel Baquela, Ana Carolina Olivera
Date: 2014-09-12 00:00:00

The lane reversal has proven to be a useful method to mitigate traffic
congestion during rush hour or in case of specific events that affect high
traffic volumes. In this work we propose a methodology that is placed within
optimization via Simulation, by means of which a multi-objective genetic
algorithm and simulations of traffic are used to determine the configuration of
ideal lane reversal.
An inertial forward-backward algorithm for monotone inclusions
Authors: Dirk A. Lorenz, Thomas Pock
Date: 2014-03-14 00:00:00

In this paper, we propose an inertial forward backward splitting algorithm to
compute a zero of the sum of two monotone operators, with one of the two
operators being co-coercive. The algorithm is inspired by the accelerated
gradient method of Nesterov, but can be applied to a much larger class of
problems including convex-concave saddle point problems and general monotone
inclusions. We prove convergence of the algorithm in a Hilbert space setting
and show that several recently proposed first-order methods can be obtained as
special cases of the general algorithm. Numerical results show that the
proposed algorithm converges faster than existing methods, while keeping the
computational cost of each iteration basically unchanged.
On Minimax Optimal Offline Policy Evaluation
Authors: Lihong Li, Remi Munos, Csaba Szepesvari
Date: 2014-09-12 00:00:00
Tasks: Multi-Armed Bandits, Off-policy evaluation

This paper studies the off-policy evaluation problem, where one aims to
estimate the value of a target policy based on a sample of observations
collected by another policy. We first consider the multi-armed bandit case,
establish a minimax risk lower bound, and analyze the risk of two standard
estimators. It is shown, and verified in simulation, that one is minimax
optimal up to a constant, while another can be arbitrarily worse, despite its
empirical success and popularity. The results are applied to related problems
in contextual bandits and fixed-horizon Markov decision processes, and are also
related to semi-supervised learning.
A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systems
Authors: Ebru Aydin Gol, Ezio Bartocci, Calin Belta
Date: 2014-09-12 00:00:00

We propose a technique to detect and generate patterns in a network of
locally interacting dynamical systems. Central to our approach is a novel
spatial superposition logic, whose semantics is defined over the quad-tree of a
partitioned image. We show that formulas in this logic can be efficiently
learned from positive and negative examples of several types of patterns. We
also demonstrate that pattern detection, which is implemented as a model
checking algorithm, performs very well for test data sets different from the
learning sets. We define a quantitative semantics for the logic and integrate
the model checking algorithm with particle swarm optimization in a
computational framework for synthesis of parameters leading to desired patterns
in reaction-diffusion systems.
Parsimonious Topic Models with Salient Word Discovery
Authors: Hossein Soleimani, David J. Miller
Date: 2014-01-22 00:00:00
Tasks: Topic Models

We propose a parsimonious topic model for text corpora. In related models
such as Latent Dirichlet Allocation (LDA), all words are modeled
topic-specifically, even though many words occur with similar frequencies
across different topics. Our modeling determines salient words for each topic,
which have topic-specific probabilities, with the rest explained by a universal
shared model. Further, in LDA all topics are in principle present in every
document. By contrast our model gives sparse topic representation, determining
the (small) subset of relevant topics for each document. We derive a Bayesian
Information Criterion (BIC), balancing model complexity and goodness of fit.
Here, interestingly, we identify an effective sample size and corresponding
penalty specific to each parameter type in our model. We minimize BIC to
jointly determine our entire model -- the topic-specific words,
document-specific topics, all model parameter values, {\it and} the total
number of topics -- in a wholly unsupervised fashion. Results on three text
corpora and an image dataset show that our model achieves higher test set
likelihood and better agreement with ground-truth class labels, compared to LDA
and to a model designed to incorporate sparsity.
Analyzing the Language of Food on Social Media
Authors: Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle, Dane Bell
Date: 2014-09-08 00:00:00

We investigate the predictive power behind the language of food on social
media. We collect a corpus of over three million food-related posts from
Twitter and demonstrate that many latent population characteristics can be
directly predicted from this data: overweight rate, diabetes rate, political
leaning, and home geographical location of authors. For all tasks, our
language-based models significantly outperform the majority-class baselines.
Performance is further improved with more complex natural language processing,
such as topic modeling. We analyze which textual features have most predictive
power for these datasets, providing insight into the connections between the
language of food, geographic locale, and community characteristics. Lastly, we
design and implement an online system for real-time query and visualization of
the dataset. Visualization tools, such as geo-referenced heatmaps,
semantics-preserving wordclouds and temporal histograms, allow us to discover
more complex, global patterns mirrored in the language of food.
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
Authors: Wanli Ouyang, Ping Luo, Xingyu Zeng, Shi Qiu, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Yuanjun Xiong, Chen Qian, Zhenyao Zhu, Ruohui Wang, Chen-Change Loy, Xiaogang Wang, Xiaoou Tang
Date: 2014-09-11 00:00:00
Tasks: Diversity, Object, object-detection, Object Detection

In this paper, we propose multi-stage and deformable deep convolutional
neural networks for object detection. This new deep learning object detection
diagram has innovations in multiple aspects. In the proposed new deep
architecture, a new deformation constrained pooling (def-pooling) layer models
the deformation of object parts with geometric constraint and penalty. With the
proposed multi-stage training strategy, multiple classifiers are jointly
optimized to process samples at different difficulty levels. A new pre-training
strategy is proposed to learn feature representations more suitable for the
object detection task and with good generalization capability. By changing the
net structures, training strategies, adding and removing some key components in
the detection pipeline, a set of models with large diversity are obtained,
which significantly improves the effectiveness of modeling averaging. The
proposed approach ranked \#2 in ILSVRC 2014. It improves the mean averaged
precision obtained by RCNN, which is the state-of-the-art of object detection,
from $31\%$ to $45\%$. Detailed component-wise analysis is also provided
through extensive experimental evaluation.
Consensus-Based Modelling using Distributed Feature Construction
Authors: Haimonti Dutta, Ashwin Srinivasan
Date: 2014-09-11 00:00:00
Tasks: Inductive logic programming

A particularly successful role for Inductive Logic Programming (ILP) is as a
tool for discovering useful relational features for subsequent use in a
predictive model. Conceptually, the case for using ILP to construct relational
features rests on treating these features as functions, the automated discovery
of which necessarily requires some form of first-order learning. Practically,
there are now several reports in the literature that suggest that augmenting
any existing features with ILP-discovered relational features can substantially
improve the predictive power of a model. While the approach is straightforward
enough, much still needs to be done to scale it up to explore more fully the
space of possible features that can be constructed by an ILP system. This is in
principle, infinite and in practice, extremely large. Applications have been
confined to heuristic or random selections from this space. In this paper, we
address this computational difficulty by allowing features to be constructed in
a distributed manner. That is, there is a network of computational units, each
of which employs an ILP engine to construct some small number of features and
then builds a (local) model. We then employ a consensus-based algorithm, in
which neighboring nodes share information to update local models. For a
category of models (those with convex loss functions), it can be shown that the
algorithm will result in all nodes converging to a consensus model. In
practice, it may be slow to achieve this convergence. Nevertheless, our results
on synthetic and real datasets that suggests that in relatively short time the
"best" node in the network reaches a model whose predictive accuracy is
comparable to that obtained using more computational effort in a
non-distributed setting (the best node is identified as the one whose weights
converge first).
Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition
Authors: Hindayati Mustafidah, Sri Hartati, Retantyo Wardoyo, Agus Harjoko
Date: 2014-09-11 00:00:00

There are several training algorithms for backpropagation method in neural
network. Not all of these algorithms have the same accuracy level demonstrated
through the percentage level of suitability in recognizing patterns in the
data. In this research tested 12 training algorithms specifically in recognize
data patterns of test validity. The basic network parameters used are the
maximum allowable epoch = 1000, target error = 10-3, and learning rate = 0.05.
Of the twelve training algorithms each performed 20 times looping. The test
results obtained that the percentage rate of the great match is trainlm
algorithm with alpha 5% have adequate levels of suitability of 87.5% at the
level of significance of 0.000. This means the most appropriate training
algorithm in recognizing the the data pattern of test validity is the trainlm
algorithm.
Building Program Vector Representations for Deep Learning
Authors: Lili Mou, Ge Li, Yuxuan Liu, Hao Peng, Zhi Jin, Yan Xu, Lu Zhang
Date: 2014-09-11 00:00:00
Tasks: Deep Learning, Representation Learning

Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.