pwc-archive/papers-with-abstracts huggingface dataset:
Linear Thompson Sampling Revisited
Authors: Marc Abeille, Alessandro Lazaric
Date: 2016-11-20 00:00:00
Tasks: Thompson Sampling

We derive an alternative proof for the regret of Thompson sampling (\ts) in the stochastic linear bandit setting. While we obtain a regret bound of order $\widetilde{O}(d^{3/2}\sqrt{T})$ as in previous results, the proof sheds new light on the functioning of the \ts. We leverage on the structure of the problem to show how the regret is related to the sensitivity (i.e., the gradient) of the objective function and how selecting optimal arms associated to \textit{optimistic} parameters does control it. Thus we show that \ts can be seen as a generic randomized algorithm where the sampling distribution is designed to have a fixed probability of being optimistic, at the cost of an additional $\sqrt{d}$ regret factor compared to a UCB-like approach. Furthermore, we show that our proof can be readily applied to regularized linear optimization and generalized linear model problems.
A Study on the Extraction and Analysis of a Large Set of Eye Movement Features during Reading
Authors: Ioannis Rigas, Lee Friedman, Oleg Komogortsev
Date: 2017-03-27 00:00:00
Tasks: Descriptive

This work presents a study on the extraction and analysis of a set of 101
categories of eye movement features from three types of eye movement events:
fixations, saccades, and post-saccadic oscillations. The eye movements were
recorded during a reading task. For the categories of features with multiple
instances in a recording we extract corresponding feature subtypes by
calculating descriptive statistics on the distributions of these instances. A
unified framework of detailed descriptions and mathematical formulas are
provided for the extraction of the feature set. The analysis of feature values
is performed using a large database of eye movement recordings from a normative
population of 298 subjects. We demonstrate the central tendency and overall
variability of feature values over the experimental population, and more
importantly, we quantify the test-retest reliability (repeatability) of each
separate feature. The described methods and analysis can provide valuable tools
in fields exploring the eye movements, such as in behavioral studies, attention
and cognition research, medical research, biometric recognition, and
human-computer interaction.
A Dynamic Programming Solution to Bounded Dejittering Problems
Authors: Lukas F. Lang
Date: 2017-03-27 00:00:00

We propose a dynamic programming solution to image dejittering problems with
bounded displacements and obtain efficient algorithms for the removal of line
jitter, line pixel jitter, and pixel jitter.
Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection
Authors: Yimian Dai, Yiquan Wu
Date: 2017-03-27 00:00:00

Many state-of-the-art methods have been proposed for infrared small target
detection. They work well on the images with homogeneous backgrounds and
high-contrast targets. However, when facing highly heterogeneous backgrounds,
they would not perform very well, mainly due to: 1) the existence of strong
edges and other interfering components, 2) not utilizing the priors fully.
Inspired by this, we propose a novel method to exploit both local and non-local
priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT)
model to represent the image and preserve its spatial correlations. Exploiting
the target sparse prior and background non-local self-correlation prior, the
target-background separation is modeled as a robust low-rank tensor recovery
problem. Moreover, with the help of the structure tensor and reweighted idea,
we design an entry-wise local-structure-adaptive and sparsity enhancing weight
to replace the globally constant weighting parameter. The decomposition could
be achieved via the element-wise reweighted higher-order robust principal
component analysis with an additional convergence condition according to the
practical situation of target detection. Extensive experiments demonstrate that
our model outperforms the other state-of-the-arts, in particular for the images
with very dim targets and heavy clutters.
GPU Activity Prediction using Representation Learning
Authors: Aswin Raghavan, Mohamed Amer, Timothy Shields, David Zhang, Sek Chai
Date: 2017-03-27 00:00:00
Tasks: Activity Prediction, GPU, Prediction, Representation Learning

GPU activity prediction is an important and complex problem. This is due to
the high level of contention among thousands of parallel threads. This problem
was mostly addressed using heuristics. We propose a representation learning
approach to address this problem. We model any performance metric as a temporal
function of the executed instructions with the intuition that the flow of
instructions can be identified as distinct activities of the code. Our
experiments show high accuracy and non-trivial predictive power of
representation learning on a benchmark.
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"
Authors: Yuguang Liu, Martin D. Levine
Date: 2017-03-27 00:00:00
Tasks: Face Detection, Region Proposal, Robust Face Recognition

Large-scale variations still pose a challenge in unconstrained face
detection. To the best of our knowledge, no current face detection algorithm
can detect a face as large as 800 x 800 pixels while simultaneously detecting
another one as small as 8 x 8 pixels within a single image with equally high
accuracy. We propose a two-stage cascaded face detection framework, Multi-Path
Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a
deep neural network with a classic learning strategy, to tackle this challenge.
The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes
faces at three different scales. It simultaneously utilizes three parallel
outputs of the convolutional feature maps to predict multi-scale candidate face
regions. The "atrous" convolution trick (convolution with up-sampled filters)
and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN
to further boost its performance. The second stage is a Boosted Forests
classifier, which utilizes deep facial features pooled from inside the
candidate face regions as well as deep contextual features pooled from a larger
region surrounding the candidate face regions. This step is included to further
remove hard negative samples. Experiments show that this approach achieves
state-of-the-art face detection performance on the WIDER FACE dataset "hard"
partition, outperforming the former best result by 9.6% for the Average
Precision.
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
Authors: Li-Fang Cheng, Gregory Darnell, Bianca Dumitrascu, Corey Chivers, Michael E Draugelis, Kai Li, Barbara E. Engelhardt
Date: 2017-03-27 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis, Time Series Prediction

In the scenario of real-time monitoring of hospital patients, high-quality
inference of patients' health status using all information available from
clinical covariates and lab tests is essential to enable successful medical
interventions and improve patient outcomes. Developing a computational
framework that can learn from observational large-scale electronic health
records (EHRs) and make accurate real-time predictions is a critical step. In
this work, we develop and explore a Bayesian nonparametric model based on
Gaussian process (GP) regression for hospital patient monitoring. We propose
MedGP, a statistical framework that incorporates 24 clinical and lab covariates
and supports a rich reference data set from which relationships between
observed covariates may be inferred and exploited for high-quality inference of
patient state over time. To do this, we develop a highly structured sparse GP
kernel to enable tractable computation over tens of thousands of time points
while estimating correlations among clinical covariates, patients, and
periodicity in patient observations. MedGP has a number of benefits over
current methods, including (i) not requiring an alignment of the time series
data, (ii) quantifying confidence regions in the predictions, (iii) exploiting
a vast and rich database of patients, and (iv) inferring interpretable
relationships among clinical covariates. We evaluate and compare results from
MedGP on the task of online prediction for three patient subgroups from two
medical data sets across 8,043 patients. We found MedGP improves online
prediction over baseline methods for nearly all covariates across different
disease subgroups and studies. The publicly available code is at
https://github.com/bee-hive/MedGP.
Multiple Object Tracking with Kernelized Correlation Filters in Urban Mixed Traffic
Authors: Yuebin Yang, Guillaume-Alexandre Bilodeau
Date: 2016-11-08 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking, Visual Object Tracking

Recently, the Kernelized Correlation Filters tracker (KCF) achieved
competitive performance and robustness in visual object tracking. On the other
hand, visual trackers are not typically used in multiple object tracking. In
this paper, we investigate how a robust visual tracker like KCF can improve
multiple object tracking. Since KCF is a fast tracker, many can be used in
parallel and still result in fast tracking. We build a multiple object tracking
system based on KCF and background subtraction. Background subtraction is
applied to extract moving objects and get their scale and size in combination
with KCF outputs, while KCF is used for data association and to handle
fragmentation and occlusion problems. As a result, KCF and background
subtraction help each other to take tracking decision at every frame. Sometimes
KCF outputs are the most trustworthy (e.g. during occlusion), while in some
other case, it is the background subtraction outputs. To validate the
effectiveness of our system, the algorithm is demonstrated on four urban video
recordings from a standard dataset. Results show that our method is competitive
with state-of-the-art trackers even if we use a much simpler data association
step.
Active Convolution: Learning the Shape of Convolution for Image Classification
Authors: Yunho Jeon, Junmo Kim
Date: 2017-03-27 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, General Classification, image-classification, Image Classification

In recent years, deep learning has achieved great success in many computer
vision applications. Convolutional neural networks (CNNs) have lately emerged
as a major approach to image classification. Most research on CNNs thus far has
focused on developing architectures such as the Inception and residual
networks. The convolution layer is the core of the CNN, but few studies have
addressed the convolution unit itself. In this paper, we introduce a
convolution unit called the active convolution unit (ACU). A new convolution
has no fixed shape, because of which we can define any form of convolution. Its
shape can be learned through backpropagation during training. Our proposed unit
has a few advantages. First, the ACU is a generalization of convolution; it can
define not only all conventional convolutions, but also convolutions with
fractional pixel coordinates. We can freely change the shape of the
convolution, which provides greater freedom to form CNN structures. Second, the
shape of the convolution is learned while training and there is no need to tune
it by hand. Third, the ACU can learn better than a conventional unit, where we
obtained the improvement simply by changing the conventional convolution to an
ACU. We tested our proposed method on plain and residual networks, and the
results showed significant improvement using our method on various datasets and
architectures in comparison with the baseline.
Bootstrapping a Lexicon for Emotional Arousal in Software Engineering
Authors: Mika V. Mäntylä, Nicole Novielli, Filippo Lanubile, Maëlick Claes, Miikka Kuutila
Date: 2017-03-27 00:00:00

Emotional arousal increases activation and performance but may also lead to
burnout in software development. We present the first version of a Software
Engineering Arousal lexicon (SEA) that is specifically designed to address the
problem of emotional arousal in the software developer ecosystem. SEA is built
using a bootstrapping approach that combines word embedding model trained on
issue-tracking data and manual scoring of items in the lexicon. We show that
our lexicon is able to differentiate between issue priorities, which are a
source of emotional activation and then act as a proxy for arousal. The best
performance is obtained by combining SEA (428 words) with a previously created
general purpose lexicon by Warriner et al. (13,915 words) and it achieves
Cohen's d effect sizes up to 0.5.
A Sentence Simplification System for Improving Relation Extraction
Authors: Christina Niklaus, Bernhard Bermeitinger, Siegfried Handschuh, André Freitas
Date: 2017-03-27 00:00:00
Proceeding: COLING 2016 12
Tasks: Relation, Relation Extraction, Sentence, Text Simplification

In this demo paper, we present a text simplification approach that is
directed at improving the performance of state-of-the-art Open Relation
Extraction (RE) systems. As syntactically complex sentences often pose a
challenge for current Open RE approaches, we have developed a simplification
framework that performs a pre-processing step by taking a single sentence as
input and using a set of syntactic-based transformation rules to create a
textual input that is easier to process for subsequently applied Open RE
systems.
Argumentation Mining in User-Generated Web Discourse
Authors: Ivan Habernal, Iryna Gurevych
Date: 2016-01-11 00:00:00
Proceeding: CL 2017 4

The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.
Development of JavaScript-based deep learning platform and application to distributed training
Authors: Masatoshi Hidaka, Ken Miura, Tatsuya Harada
Date: 2017-02-07 00:00:00
Tasks: Deep Learning

Deep learning is increasingly attracting attention for processing big data.
Existing frameworks for deep learning must be set up to specialized computer
systems. Gaining sufficient computing resources therefore entails high costs of
deployment and maintenance. In this work, we implement a matrix library and
deep learning framework that uses JavaScript. It can run on web browsers
operating on ordinary personal computers and smartphones. Using JavaScript,
deep learning can be accomplished in widely diverse environments without the
necessity for software installation. Using GPGPU from WebCL framework, our
framework can train large scale convolutional neural networks such as VGGNet
and ResNet. In the experiments, we demonstrate their practicality by training
VGGNet in a distributed manner using web browsers as the client.
Structural Damage Identification Using Artificial Neural Network and Synthetic data
Authors: Divya Shyam Singha, G. B. L. Chowdarya, D Roy Mahapatraa
Date: 2017-03-27 00:00:00

This paper presents real-time vibration based identification technique using
measured frequency response functions(FRFs) under random vibration loading.
Artificial Neural Networks (ANNs) are trained to map damage fingerprints to
damage characteristic parameters. Principal component statistical analysis(PCA)
technique was used to tackle the problem of high dimensionality and high noise
of data, which is common for industrial structures. The present study considers
Crack, Rivet hole expansion and redundant uniform mass as damages on the
structure. Frequency response function data after being reduced in size using
PCA is fed to individual neural networks to localize and predict the severity
of damage on the structure. The system of ANNs trained with both numerical and
experimental model data to make the system reliable and robust. The methodology
is applied to a numerical model of stiffened panel structure, where damages are
confined close to the stiffener. The results showed that, in all the cases
considered, it is possible to localize and predict severity of the damage
occurrence with very good accuracy and reliability.
Thompson Sampling for Linear-Quadratic Control Problems
Authors: Marc Abeille, Alessandro Lazaric
Date: 2017-03-27 00:00:00
Tasks: Reinforcement Learning, Thompson Sampling

We consider the exploration-exploitation tradeoff in linear quadratic (LQ)
control problems, where the state dynamics is linear and the cost function is
quadratic in states and controls. We analyze the regret of Thompson sampling
(TS) (a.k.a. posterior-sampling for reinforcement learning) in the frequentist
setting, i.e., when the parameters characterizing the LQ dynamics are fixed.
Despite the empirical and theoretical success in a wide range of problems from
multi-armed bandit to linear bandit, we show that when studying the frequentist
regret TS in control problems, we need to trade-off the frequency of sampling
optimistic parameters and the frequency of switches in the control policy. This
results in an overall regret of $O(T^{2/3})$, which is significantly worse than
the regret $O(\sqrt{T})$ achieved by the optimism-in-face-of-uncertainty
algorithm in LQ control problems.
Multimodal deep learning approach for joint EEG-EMG data compression and classification
Authors: Ahmed Ben Said, Amr Mohamed, Tarek Elfouly, Khaled Harras, Z. Jane Wang
Date: 2017-03-27 00:00:00
Tasks: Data Compression, Decoder, EEG, Electroencephalogram (EEG), General Classification, Multimodal Deep Learning

In this paper, we present a joint compression and classification approach of
EEG and EMG signals using a deep learning approach. Specifically, we build our
system based on the deep autoencoder architecture which is designed not only to
extract discriminant features in the multimodal data representation but also to
reconstruct the data from the latent representation using encoder-decoder
layers. Since autoencoder can be seen as a compression approach, we extend it
to handle multimodal data at the encoder layer, reconstructed and retrieved at
the decoder layer. We show through experimental results, that exploiting both
multimodal data intercorellation and intracorellation 1) Significantly reduces
signal distortion particularly for high compression levels 2) Achieves better
accuracy in classifying EEG and EMG signals recorded and labeled according to
the sentiments of the volunteer.
Constructing a Natural Language Inference Dataset using Generative Neural Networks
Authors: Janez Starc, Dunja Mladenić
Date: 2016-07-20 00:00:00
Tasks: Natural Language Inference, Natural Language Understanding

Natural Language Inference is an important task for Natural Language
Understanding. It is concerned with classifying the logical relation between
two sentences. In this paper, we propose several text generative neural
networks for generating text hypothesis, which allows construction of new
Natural Language Inference datasets. To evaluate the models, we propose a new
metric -- the accuracy of the classifier trained on the generated dataset. The
accuracy obtained by our best generative model is only 2.7% lower than the
accuracy of the classifier trained on the original, human crafted dataset.
Furthermore, the best generated dataset combined with the original dataset
achieves the highest accuracy. The best model learns a mapping embedding for
each training example. By comparing various metrics we show that datasets that
obtain higher ROUGE or METEOR scores do not necessarily yield higher
classification accuracies. We also provide analysis of what are the
characteristics of a good dataset including the distinguishability of the
generated datasets from the original one.
Mastering Sketching: Adversarial Augmentation for Structured Prediction
Authors: Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa
Date: 2017-03-27 00:00:00
Tasks: Prediction, Structured Prediction

We present an integral framework for training sketch simplification networks
that convert challenging rough sketches into clean line drawings. Our approach
augments a simplification network with a discriminator network, training both
networks jointly so that the discriminator network discerns whether a line
drawing is a real training data or the output of the simplification network,
which in turn tries to fool it. This approach has two major advantages. First,
because the discriminator network learns the structure in line drawings, it
encourages the output sketches of the simplification network to be more similar
in appearance to the training sketches. Second, we can also train the
simplification network with additional unsupervised data, using the
discriminator network as a substitute teacher. Thus, by adding only rough
sketches without simplified line drawings, or only line drawings without the
original rough sketches, we can improve the quality of the sketch
simplification. We show how our framework can be used to train models that
significantly outperform the state of the art in the sketch simplification
task, despite using the same architecture for inference. We additionally
present an approach to optimize for a single image, which improves accuracy at
the cost of additional computation time. Finally, we show that, using the same
framework, it is possible to train the network to perform the inverse problem,
i.e., convert simple line sketches into pencil drawings, which is not possible
using the standard mean squared error loss. We validate our framework with two
user tests, where our approach is preferred to the state of the art in sketch
simplification 92.3% of the time and obtains 1.2 more points on a scale of 1 to
5.
Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments
Authors: Ahmed Hussain Qureshi, Yasar Ayaz
Date: 2017-03-27 00:00:00
Tasks: Computational Efficiency, Motion Planning, Optimal Motion Planning

The sampling based motion planning algorithm known as Rapidly-exploring
Random Trees (RRT) has gained the attention of many researchers due to their
computational efficiency and effectiveness. Recently, a variant of RRT called
RRT* has been proposed that ensures asymptotic optimality. Subsequently its
bidirectional version has also been introduced in the literature known as
Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent
Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT*
and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed
for complex cluttered environments. IB-RRT* utilizes the bidirectional trees
approach and introduces intelligent sample insertion heuristic for fast
convergence to the optimal path solution using uniform sampling heuristics. The
proposed algorithm is evaluated theoretically and experimental results are
presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental
results demonstrate the superior efficiency of IB-RRT* in comparison with RRT*
and B-RRT in complex cluttered environments.