pwc-archive/papers-with-abstracts huggingface dataset:
Structured Parallel Programming for Monte Carlo Tree Search
Authors: S. Ali Mirsoleimani, Aske Plaat, Jaap van den Herik, Jos Vermaseren
Date: 2017-04-02 00:00:00
Tasks: Management

In this paper, we present a new algorithm for parallel Monte Carlo tree
search (MCTS). It is based on the pipeline pattern and allows flexible
management of the control flow of the operations in parallel MCTS. The pipeline
pattern provides for the first structured parallel programming approach to
MCTS. Moreover, we propose a new lock-free tree data structure for parallel
MCTS which removes synchronization overhead. The Pipeline Pattern for Parallel
MCTS algorithm (called 3PMCTS), scales very well to higher numbers of cores
when compared to the existing methods.
Efficient Version-Space Reduction for Visual Tracking
Authors: Kourosh Meshgi, Shigeyuki Oba, Shin Ishii
Date: 2017-04-02 00:00:00
Tasks: Visual Tracking

Discrminative trackers, employ a classification approach to separate the
target from its background. To cope with variations of the target shape and
appearance, the classifier is updated online with different samples of the
target and the background. Sample selection, labeling and updating the
classifier is prone to various sources of errors that drift the tracker. We
introduce the use of an efficient version space shrinking strategy to reduce
the labeling errors and enhance its sampling strategy by measuring the
uncertainty of the tracker about the samples. The proposed tracker, utilize an
ensemble of classifiers that represents different hypotheses about the target,
diversify them using boosting to provide a larger and more consistent coverage
of the version-space and tune the classifiers' weights in voting. The proposed
system adjusts the model update rate by promoting the co-training of the
short-memory ensemble with a long-memory oracle. The proposed tracker
outperformed state-of-the-art trackers on different sequences bearing various
tracking challenges.
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Authors: Behzad Tabibian, Isabel Valera, Mehrdad Farajtabar, Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez
Date: 2016-10-24 00:00:00

Online knowledge repositories typically rely on their users or dedicated
editors to evaluate the reliability of their content. These evaluations can be
viewed as noisy measurements of both information reliability and information
source trustworthiness. Can we leverage these noisy evaluations, often biased,
to distill a robust, unbiased and interpretable measure of both notions?
  In this paper, we argue that the temporal traces left by these noisy
evaluations give cues on the reliability of the information and the
trustworthiness of the sources. Then, we propose a temporal point process
modeling framework that links these temporal traces to robust, unbiased and
interpretable notions of information reliability and source trustworthiness.
Furthermore, we develop an efficient convex optimization procedure to learn the
parameters of the model from historical traces. Experiments on real-world data
gathered from Wikipedia and Stack Overflow show that our modeling framework
accurately predicts evaluation events, provides an interpretable measure of
information reliability and source trustworthiness, and yields interesting
insights about real-world events.
The Stixel world: A medium-level representation of traffic scenes
Authors: Marius Cordts, Timo Rehfeld, Lukas Schneider, David Pfeiffer, Markus Enzweiler, Stefan Roth, Marc Pollefeys, Uwe Franke
Date: 2017-04-02 00:00:00
Tasks: Autonomous Vehicles, object-detection, Object Detection

Recent progress in advanced driver assistance systems and the race towards
autonomous vehicles is mainly driven by two factors: (1) increasingly
sophisticated algorithms that interpret the environment around the vehicle and
react accordingly, and (2) the continuous improvements of sensor technology
itself. In terms of cameras, these improvements typically include higher
spatial resolution, which as a consequence requires more data to be processed.
The trend to add multiple cameras to cover the entire surrounding of the
vehicle is not conducive in that matter. At the same time, an increasing number
of special purpose algorithms need access to the sensor input data to correctly
interpret the various complex situations that can occur, particularly in urban
traffic.
  By observing those trends, it becomes clear that a key challenge for vision
architectures in intelligent vehicles is to share computational resources. We
believe this challenge should be faced by introducing a representation of the
sensory data that provides compressed and structured access to all relevant
visual content of the scene. The Stixel World discussed in this paper is such a
representation. It is a medium-level model of the environment that is
specifically designed to compress information about obstacles by leveraging the
typical layout of outdoor traffic scenes. It has proven useful for a multitude
of automotive vision applications, including object detection, tracking,
segmentation, and mapping.
  In this paper, we summarize the ideas behind the model and generalize it to
take into account multiple dense input streams: the image itself, stereo depth
maps, and semantic class probability maps that can be generated, e.g., by CNNs.
Our generalization is embedded into a novel mathematical formulation for the
Stixel model. We further sketch how the free parameters of the model can be
learned using structured SVMs.
Potential Functions based Sampling Heuristic For Optimal Path Planning
Authors: Ahmed Hussain Qureshi, Yasar Ayaz
Date: 2017-04-02 00:00:00

Rapidly-exploring Random Tree Star(RRT*) is a recently proposed extension of
Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free,
asymptotically optimal path regardless of obstacle's geometry in a given
environment. However, one of the limitations in the RRT* algorithm is slow
convergence to optimal path solution. As a result, it consumes high memory as
well as time due to a large number of iterations utilised in achieving optimal
path solution. To overcome these limitations, we propose the Potential Function
Based-RRT* (P-RRT*) that incorporates the Artificial Potential Field Algorithm
in RRT*. The proposed algorithm allows a considerable decrease in the number of
iterations and thus leads to more efficient memory utilization and an
accelerated convergence rate. In order to illustrate the usefulness of the
proposed algorithm in terms of space execution and convergence rate, this paper
presents rigorous simulation based comparisons between the proposed techniques
and RRT* under different environmental conditions. Moreover, both algorithms
are also tested and compared under non-holonomic differential constraints.
A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
Authors: Shuang Ma, Jing Liu, Chang Wen Chen
Date: 2017-04-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Aesthetics Quality Assessment

Deep convolutional neural networks (CNN) have recently been shown to generate
promising results for aesthetics assessment. However, the performance of these
deep CNN methods is often compromised by the constraint that the neural network
only takes the fixed-size input. To accommodate this requirement, input images
need to be transformed via cropping, warping, or padding, which often alter
image composition, reduce image resolution, or cause image distortion. Thus the
aesthetics of the original images is impaired because of potential loss of fine
grained details and holistic image layout. However, such fine grained details
and holistic image layout is critical for evaluating an image's aesthetics. In
this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional
Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This
novel scheme is able to accept arbitrary sized images, and learn from both
fined grained details and holistic image layout simultaneously. To enable
training on these hybrid inputs, we extend the method by developing a dedicated
double-subnet neural network structure, i.e. a Multi-Patch subnet and a
Layout-Aware subnet. We further construct an aggregation layer to effectively
combine the hybrid features from these two subnets. Extensive experiments on
the large-scale aesthetics assessment benchmark (AVA) demonstrate significant
performance improvement over the state-of-the-art in photo aesthetic
assessment.
Filtering Tweets for Social Unrest
Authors: Alan Mishler, Kevin Wonus, Wendy Chambers, Michael Bloodgood
Date: 2017-02-20 00:00:00

Since the events of the Arab Spring, there has been increased interest in
using social media to anticipate social unrest. While efforts have been made
toward automated unrest prediction, we focus on filtering the vast volume of
tweets to identify tweets relevant to unrest, which can be provided to
downstream users for further analysis. We train a supervised classifier that is
able to label Arabic language tweets as relevant to unrest with high
reliability. We examine the relationship between training data size and
performance and investigate ways to optimize the model building process while
minimizing cost. We also explore how confidence thresholds can be set to
achieve desired levels of performance.
GRAM: Graph-based Attention Model for Healthcare Representation Learning
Authors: Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun
Date: 2016-11-21 00:00:00
Tasks: Deep Learning, Representation Learning

Deep learning methods exhibit promising performance for predictive modeling
in healthcare, but two important challenges remain: -Data insufficiency:Often
in healthcare predictive modeling, the sample size is insufficient for deep
learning methods to achieve satisfactory results. -Interpretation:The
representations learned by deep learning methods should align with medical
knowledge. To address these challenges, we propose a GRaph-based Attention
Model, GRAM that supplements electronic health records (EHR) with hierarchical
information inherent to medical ontologies. Based on the data volume and the
ontology structure, GRAM represents a medical concept as a combination of its
ancestors in the ontology via an attention mechanism. We compared predictive
performance (i.e. accuracy, data needs, interpretability) of GRAM to various
methods including the recurrent neural network (RNN) in two sequential
diagnoses prediction tasks and one heart failure prediction task. Compared to
the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely
observed in the training data and 3% improved area under the ROC curve for
predicting heart failure using an order of magnitude less training data.
Additionally, unlike other methods, the medical concept representations learned
by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits
intuitive attention behaviors by adaptively generalizing to higher level
concepts when facing data insufficiency at the lower level concepts.
Estimation and Inference on Nonlinear and Heterogeneous Effects
Authors: Marc Ratkovic, Dustin Tingley
Date: 2017-03-16 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference, regression

Multiple regression has been the go-to method for data analysis for generations of scholars due to its transparency, interpretability, and desirable theoretical properties. However, the method's simplicity precludes the discovery of complex heterogeneities in the data. We introduce the Method of Direct Estimation and Inference (MDEI) that embraces these potential complexities, is interpretable, has desirable theoretical guarantees, and, unlike some existing methods, returns appropriate uncertainty estimates. The proposed method uses a machine learning regression methodology to estimate the observation-level effect of a treatment variable. Importantly, we introduce a robust approach to uncertainty estimates. We provide simulation evidence and an application illustrating the performance of the method.
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Authors: Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, Eric P. Xing
Date: 2017-04-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Classification, General Classification, Implicit Discourse Relation Classification, Relation, Relation Classification, Relation Network

Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.
A Bayes consistent 1-NN classifier
Authors: Aryeh Kontorovich, Roi Weiss
Date: 2014-07-01 00:00:00

We show that a simple modification of the 1-nearest neighbor classifier
yields a strongly Bayes consistent learner. Prior to this work, the only
strongly Bayes consistent proximity-based method was the k-nearest neighbor
classifier, for k growing appropriately with sample size. We will argue that a
margin-regularized 1-NN enjoys considerable statistical and algorithmic
advantages over the k-NN classifier. These include user-friendly finite-sample
error bounds, as well as time- and memory-efficient learning and test-point
evaluation algorithms with a principled speed-accuracy tradeoff. Encouraging
empirical results are reported.
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition
Authors: Manoel Horta Ribeiro, Bruno Teixeira, Antônio Otávio Fernandes, Wagner Meira Jr., Erickson R. Nascimento
Date: 2017-04-01 00:00:00
Tasks: Gesture Recognition

Many of the state-of-the-art algorithms for gesture recognition are based on
Conditional Random Fields (CRFs). Successful approaches, such as the
Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose
values are mapped to the values of the labels. In this paper we propose a novel
methodology to set the latent values according to the gesture complexity. We
use an heuristic that iterates through the samples associated with each label
value, stimating their complexity. We then use it to assign the latent values
to the label values. We evaluate our method on the task of recognizing human
gestures from video streams. The experiments were performed in binary datasets,
generated by grouping different labels. Our results demonstrate that our
approach outperforms the arbitrary one in many cases, increasing the accuracy
by up to 10%.
Sentiment Analysis of Citations Using Word2vec
Authors: Haixia Liu
Date: 2017-04-01 00:00:00
Tasks: Feature Engineering, General Classification, Sentence, Sentiment Analysis, Word Embeddings

Citation sentiment analysis is an important task in scientific paper
analysis. Existing machine learning techniques for citation sentiment analysis
are focusing on labor-intensive feature engineering, which requires large
annotated corpus. As an automatic feature extraction tool, word2vec has been
successfully applied to sentiment analysis of short texts. In this work, I
conducted empirical research with the question: how well does word2vec work on
the sentiment analysis of citations? The proposed method constructed sentence
vectors (sent2vec) by averaging the word embeddings, which were learned from
Anthology Collections (ACL-Embeddings). I also investigated polarity-specific
word embeddings (PS-Embeddings) for classifying positive and negative
citations. The sentence vectors formed a feature space, to which the examined
citation sentence was mapped to. Those features were input into classifiers
(support vector machines) for supervised classification. Using
10-cross-validation scheme, evaluation was conducted on a set of annotated
citations. The results showed that word embeddings are effective on classifying
positive and negative citations. However, hand-crafted features performed
better for the overall classification.
Clustering-based Source-aware Assessment of True Robustness for Learning Models
Authors: Ozsel Kilinc, Ismail Uysal
Date: 2017-04-01 00:00:00
Tasks: Clustering

We introduce a novel validation framework to measure the true robustness of
learning models for real-world applications by creating source-inclusive and
source-exclusive partitions in a dataset via clustering. We develop a
robustness metric derived from source-aware lower and upper bounds of model
accuracy even when data source labels are not readily available. We clearly
demonstrate that even on a well-explored dataset like MNIST, challenging
training scenarios can be constructed under the proposed assessment framework
for two separate yet equally important applications: i) more rigorous learning
model comparison and ii) dataset adequacy evaluation. In addition, our findings
not only promise a more complete identification of trade-offs between model
complexity, accuracy and robustness but can also help researchers optimize
their efforts in data collection by identifying the less robust and more
challenging class labels.
Multiple Instance Detection Network with Online Instance Classifier Refinement
Authors: Peng Tang, Xinggang Wang, Xiang Bai, Wenyu Liu
Date: 2017-04-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Multiple Instance Learning, Object, object-detection, Object Detection, Object Recognition, Weakly Supervised Object Detection

Of late, weakly supervised object detection is with great importance in
object recognition. Based on deep learning, weakly supervised detectors have
achieved many promising results. However, compared with fully supervised
detection, it is more challenging to train deep network based detectors in a
weakly supervised manner. Here we formulate weakly supervised detection as a
Multiple Instance Learning (MIL) problem, where instance classifiers (object
detectors) are put into the network as hidden nodes. We propose a novel online
instance classifier refinement algorithm to integrate MIL and the instance
classifier refinement procedure into a single deep network, and train the
network end-to-end with only image-level supervision, i.e., without object
location information. More precisely, instance labels inferred from weak
supervision are propagated to their spatially overlapped instances to refine
instance classifier online. The iterative instance classifier refinement
procedure is implemented using multiple streams in deep network, where each
stream supervises its latter stream. Weakly supervised object detection
experiments are carried out on the challenging PASCAL VOC 2007 and 2012
benchmarks. We obtain 47% mAP on VOC 2007 that significantly outperforms the
previous state-of-the-art.
sWSI: A Low-cost and Commercial-quality Whole Slide Imaging System on Android and iOS Smartphones
Authors: Shuoxin Ma, Tan Wang
Date: 2017-04-01 00:00:00

In this paper, scalable Whole Slide Imaging (sWSI), a novel high-throughput,
cost-effective and robust whole slide imaging system on both Android and iOS
platforms is introduced and analyzed. With sWSI, most mainstream smartphone
connected to a optical eyepiece of any manually controlled microscope can be
automatically controlled to capture sequences of mega-pixel fields of views
that are synthesized into giga-pixel virtual slides. Remote servers carry out
the majority of computation asynchronously to support clients running at
satisfying frame rates without sacrificing image quality nor robustness. A
typical 15x15mm sample can be digitized in 30 seconds with 4X or in 3 minutes
with 10X object magnification, costing under $1. The virtual slide quality is
considered comparable to existing high-end scanners thus satisfying for
clinical usage by surveyed pathologies. The scan procedure with features such
as supporting magnification up to 100x, recoding z-stacks,
specimen-type-neutral and giving real-time feedback, is deemed
work-flow-friendly and reliable.
Psychological and Personality Profiles of Political Extremists
Authors: Meysam Alizadeh, Ingmar Weber, Claudio Cioffi-Revilla, Santo Fortunato, Michael Macy
Date: 2017-04-01 00:00:00

Global recruitment into radical Islamic movements has spurred renewed
interest in the appeal of political extremism. Is the appeal a rational
response to material conditions or is it the expression of psychological and
personality disorders associated with aggressive behavior, intolerance,
conspiratorial imagination, and paranoia? Empirical answers using surveys have
been limited by lack of access to extremist groups, while field studies have
lacked psychological measures and failed to compare extremists with contrast
groups. We revisit the debate over the appeal of extremism in the U.S. context
by comparing publicly available Twitter messages written by over 355,000
political extremist followers with messages written by non-extremist U.S.
users. Analysis of text-based psychological indicators supports the moral
foundation theory which identifies emotion as a critical factor in determining
political orientation of individuals. Extremist followers also differ from
others in four of the Big Five personality traits.
Errors-in-variables models with dependent measurements
Authors: Mark Rudelson, Shuheng Zhou
Date: 2016-11-15 00:00:00

Suppose that we observe $y \in \mathbb{R}^n$ and $X \in \mathbb{R}^{n \times
m}$ in the following errors-in-variables model: \begin{eqnarray*} y & = & X_0
\beta^* +\epsilon \\ X & = & X_0 + W, \end{eqnarray*} where $X_0$ is an $n
\times m$ design matrix with independent subgaussian row vectors, $\epsilon \in
\mathbb{R}^n$ is a noise vector and $W$ is a mean zero $n \times m$ random
noise matrix with independent subgaussian column vectors, independent of $X_0$
and $\epsilon$. This model is significantly different from those analyzed in
the literature in the sense that we allow the measurement error for each
covariate to be a dependent vector across its $n$ observations. Such error
structures appear in the science literature when modeling the trial-to-trial
fluctuations in response strength shared across a set of neurons.
  Under sparsity and restrictive eigenvalue type of conditions, we show that
one is able to recover a sparse vector $\beta^* \in \mathbb{R}^m$ from the
model given a single observation matrix $X$ and the response vector $y$. We
establish consistency in estimating $\beta^*$ and obtain the rates of
convergence in the $\ell_q$ norm, where $q = 1, 2$. We show error bounds which
approach that of the regular Lasso and the Dantzig selector in case the errors
in $W$ are tending to 0. We analyze the convergence rates of the gradient
descent methods for solving the nonconvex programs and show that the composite
gradient descent algorithm is guaranteed to converge at a geometric rate to a
neighborhood of the global minimizers: the size of the neighborhood is bounded
by the statistical error in the $\ell_2$ norm. Our analysis reveals interesting
connections between computational and statistical efficiency and the
concentration of measure phenomenon in random matrix theory. We provide
simulation evidence illuminating the theoretical predictions.