pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting the Pruning Power of Strong Local Consistencies Through Parallelization
Authors: Minas Dasygenis, Kostas Stergiou
Date: 2017-05-15 00:00:00
Tasks: ARC

Local consistencies stronger than arc consistency have received a lot of
attention since the early days of CSP research. %because of the strong pruning
they can achieve. However, they have not been widely adopted by CSP solvers.
This is because applying such consistencies can sometimes result in
considerably smaller search tree sizes and therefore in important speed-ups,
but in other cases the search space reduction may be small, causing severe run
time penalties. Taking advantage of recent advances in parallelization, we
propose a novel approach for the application of strong local consistencies
(SLCs) that can improve their performance by largely preserving the speed-ups
they offer in cases where they are successful, and eliminating the run time
penalties in cases where they are unsuccessful. This approach is presented in
the form of two search algorithms. Both algorithms consist of a master search
process, which is a typical CSP solver, and a number of slave processes, with
each one implementing a SLC method. The first algorithm runs the different SLCs
synchronously at each node of the search tree explored in the master process,
while the second one can run them asynchronously at different nodes of the
search tree. Experimental results demonstrate the benefits of the proposed
method.
Back to RGB: 3D tracking of hands and hand-object interactions based on short-baseline stereo
Authors: Paschalis Panteleris, Antonis Argyros
Date: 2017-05-15 00:00:00
Tasks: 3D Reconstruction

We present a novel solution to the problem of 3D tracking of the articulated
motion of human hand(s), possibly in interaction with other objects. The vast
majority of contemporary relevant work capitalizes on depth information
provided by RGBD cameras. In this work, we show that accurate and efficient 3D
hand tracking is possible, even for the case of RGB stereo. A straightforward
approach for solving the problem based on such input would be to first recover
depth and then apply a state of the art depth-based 3D hand tracking method.
Unfortunately, this does not work well in practice because the stereo-based,
dense 3D reconstruction of hands is far less accurate than the one obtained by
RGBD cameras. Our approach bypasses 3D reconstruction and follows a completely
different route: 3D hand tracking is formulated as an optimization problem
whose solution is the hand configuration that maximizes the color consistency
between the two views of the hand. We demonstrate the applicability of our
method for real time tracking of a single hand, of a hand manipulating an
object and of two interacting hands. The method has been evaluated
quantitatively on standard datasets and in comparison to relevant, state of the
art RGBD-based approaches. The obtained results demonstrate that the proposed
stereo-based method performs equally well to its RGBD-based competitors, and in
some cases, it even outperforms them.
Machine Comprehension by Text-to-Text Neural Question Generation
Authors: Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Sandeep Subramanian, Saizheng Zhang, Adam Trischler
Date: 2017-05-04 00:00:00
Proceeding: WS 2017 8
Tasks: Question Answering, Question Generation, Question-Generation, Reading Comprehension, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a recurrent neural model that generates natural-language questions
from documents, conditioned on answers. We show how to train the model using a
combination of supervised and reinforcement learning. After teacher forcing for
standard maximum likelihood training, we fine-tune the model using policy
gradient techniques to maximize several rewards that measure question quality.
Most notably, one of these rewards is the performance of a question-answering
system. We motivate question generation as a means to improve the performance
of question answering systems. Our model is trained and evaluated on the recent
question-answering dataset SQuAD.
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis
Authors: Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar
Date: 2017-05-15 00:00:00
Proceeding: ICML 2017 8
Tasks: Prognosis

Critically ill patients in regular wards are vulnerable to unanticipated
adverse events which require prompt transfer to the intensive care unit (ICU).
To allow for accurate prognosis of deteriorating patients, we develop a novel
continuous-time probabilistic model for a monitored patient's temporal sequence
of physiological data. Our model captures "informatively sampled" patient
episodes: the clinicians' decisions on when to observe a hospitalized patient's
vital signs and lab tests over time are represented by a marked Hawkes process,
with intensity parameters that are modulated by the patient's latent clinical
states, and with observable physiological data (mark process) modeled as a
switching multi-task Gaussian process. In addition, our model captures
"informatively censored" patient episodes by representing the patient's latent
clinical states as an absorbing semi-Markov jump process. The model parameters
are learned from offline patient episodes in the electronic health records via
an EM-based algorithm. Experiments conducted on a cohort of patients admitted
to a major medical center over a 3-year period show that risk prognosis based
on our model significantly outperforms the currently deployed medical risk
scores and other baseline machine learning algorithms.
Elite Bases Regression: A Real-time Algorithm for Symbolic Regression
Authors: Chen Chen, Changtong Luo, Zonglin Jiang
Date: 2017-04-24 00:00:00
Tasks: regression, Symbolic Regression

Symbolic regression is an important but challenging research topic in data
mining. It can detect the underlying mathematical models. Genetic programming
(GP) is one of the most popular methods for symbolic regression. However, its
convergence speed might be too slow for large scale problems with a large
number of variables. This drawback has become a bottleneck in practical
applications. In this paper, a new non-evolutionary real-time algorithm for
symbolic regression, Elite Bases Regression (EBR), is proposed. EBR generates a
set of candidate basis functions coded with parse-matrix in specific mapping
rules. Meanwhile, a certain number of elite bases are preserved and updated
iteratively according to the correlation coefficients with respect to the
target model. The regression model is then spanned by the elite bases. A
comparative study between EBR and a recent proposed machine learning method for
symbolic regression, Fast Function eXtraction (FFX), are conducted. Numerical
results indicate that EBR can solve symbolic regression problems more
effectively.
Extending Defensive Distillation
Authors: Nicolas Papernot, Patrick McDaniel
Date: 2017-05-15 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning is vulnerable to adversarial examples: inputs carefully
modified to force misclassification. Designing defenses against such inputs
remains largely an open problem. In this work, we revisit defensive
distillation---which is one of the mechanisms proposed to mitigate adversarial
examples---to address its limitations. We view our results not only as an
effective way of addressing some of the recently discovered attacks but also as
reinforcing the importance of improved training techniques.
Comparison of Maximum Likelihood and GAN-based training of Real NVPs
Authors: Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan
Date: 2017-05-15 00:00:00
Tasks: One-Shot Learning

We train a generator by maximum likelihood and we also train the same
generator architecture by Wasserstein GAN. We then compare the generated
samples, exact log-probability densities and approximate Wasserstein distances.
We show that an independent critic trained to approximate Wasserstein distance
between the validation set and the generator distribution helps detect
overfitting. Finally, we use ideas from the one-shot learning literature to
develop a novel fast learning critic.
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks
Authors: Emil Eriksson, György Dán, Viktoria Fodor
Date: 2017-05-15 00:00:00
Tasks: Distributed Optimization

Real-time visual analysis tasks, like tracking and recognition, require swift
execution of computationally intensive algorithms. Visual sensor networks can
be enabled to perform such tasks by augmenting the sensor network with
processing nodes and distributing the computational burden in a way that the
cameras contend for the processing nodes while trying to minimize their task
completion times. In this paper, we formulate the problem of minimizing the
completion time of all camera sensors as an optimization problem. We propose
algorithms for fully distributed optimization, analyze the existence of
equilibrium allocations, evaluate the effect of the network topology and of the
video characteristics, and the benefits of central coordination. Our results
demonstrate that with sufficient information available, distributed
optimization can provide low completion times, moreover predictable and stable
performance can be achieved with additional, sparse central coordination.
Instance Flow Based Online Multiple Object Tracking
Authors: Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
Date: 2017-03-03 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking, Optical Flow Estimation, Semantic Segmentation

We present a method to perform online Multiple Object Tracking (MOT) of known
object categories in monocular video data. Current Tracking-by-Detection MOT
approaches build on top of 2D bounding box detections. In contrast, we exploit
state-of-the-art instance aware semantic segmentation techniques to compute 2D
shape representations of target objects in each frame. We predict position and
shape of segmented instances in subsequent frames by exploiting optical flow
cues. We define an affinity matrix between instances of subsequent frames which
reflects locality and visual similarity. The instance association is solved by
applying the Hungarian method. We evaluate different configurations of our
algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our
tracking approach is able to track objects with high relative motions. In
addition, we provide results of our approach on the MOT 2D 2015 test set for
comparison with previous works. We achieve a MOTA score of 32.1.
Strategically knowing how
Authors: Raul Fervari, Andreas Herzig, Yanjun Li, Yanjing Wang
Date: 2017-05-15 00:00:00

In this paper, we propose a single-agent logic of goal-directed knowing how
extending the standard epistemic logic of knowing that with a new knowing how
operator. The semantics of the new operator is based on the idea that knowing
how to achieve $\phi$ means that there exists a (uniform) strategy such that
the agent knows that it can make sure $\phi$. We give an intuitive
axiomatization of our logic and prove the soundness, completeness, and
decidability of the logic. The crucial axioms relating knowing that and knowing
how illustrate our understanding of knowing how in this setting. This logic can
be used in representing both knowledge-that and knowledge-how.
What do Neural Machine Translation Models Learn about Morphology?
Authors: Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, James Glass
Date: 2017-04-11 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Machine Translation, Morphological Tagging, Translation

Neural machine translation (MT) models obtain state-of-the-art performance
while maintaining a simple, end-to-end architecture. However, little is known
about what these models learn about source and target languages during the
training process. In this work, we analyze the representations learned by
neural MT models at various levels of granularity and empirically evaluate the
quality of the representations for learning morphology through extrinsic
part-of-speech and morphological tagging tasks. We conduct a thorough
investigation along several parameters: word-based vs. character-based
representations, depth of the encoding layer, the identity of the target
language, and encoder vs. decoder representations. Our data-driven,
quantitative evaluation sheds light on important aspects in the neural MT
system and its ability to capture word structure.
Preparing for the Unknown: Learning a Universal Policy with Online System Identification
Authors: Wenhao Yu, Jie Tan, C. Karen Liu, Greg Turk
Date: 2017-02-08 00:00:00
Tasks: Friction

We present a new method of learning control policies that successfully
operate under unknown dynamic models. We create such policies by leveraging a
large number of training examples that are generated using a physical
simulator. Our system is made of two components: a Universal Policy (UP) and a
function for Online System Identification (OSI). We describe our control policy
as universal because it is trained over a wide array of dynamic models. These
variations in the dynamic model may include differences in mass and inertia of
the robots' components, variable friction coefficients, or unknown mass of an
object to be manipulated. By training the Universal Policy with this variation,
the control policy is prepared for a wider array of possible conditions when
executed in an unknown environment. The second part of our system uses the
recent state and action history of the system to predict the dynamics model
parameters mu. The value of mu from the Online System Identification is then
provided as input to the control policy (along with the system state).
Together, UP-OSI is a robust control policy that can be used across a wide
range of dynamic models, and that is also responsive to sudden changes in the
environment. We have evaluated the performance of this system on a variety of
tasks, including the problem of cart-pole swing-up, the double inverted
pendulum, locomotion of a hopper, and block-throwing of a manipulator. UP-OSI
is effective at these tasks across a wide range of dynamic models. Moreover,
when tested with dynamic models outside of the training range, UP-OSI
outperforms the Universal Policy alone, even when UP is given the actual value
of the model dynamics. In addition to the benefits of creating more robust
controllers, UP-OSI also holds out promise of narrowing the Reality Gap between
simulated and real physical systems.
Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
Authors: Xuefeng Xiao, Yafeng Yang, Tasweer Ahmad, Lianwen Jin, Tianhai Chang
Date: 2017-05-15 00:00:00

Currently, owing to the ubiquity of mobile devices, online handwritten
Chinese character recognition (HCCR) has become one of the suitable choice for
feeding input to cell phones and tablet devices. Over the past few years,
larger and deeper convolutional neural networks (CNNs) have extensively been
employed for improving character recognition performance. However, its
substantial storage requirement is a significant obstacle in deploying such
networks into portable electronic devices. To circumvent this problem, we
propose a novel technique called DropWeight for pruning redundant connections
in the CNN architecture. It is revealed that the proposed method not only
treats streamlined architectures such as AlexNet and VGGNet well but also
exhibits remarkable performance for deep residual network and inception
network. We also demonstrate that global pooling is a better choice for
building very compact online HCCR systems. Experiments were performed on the
ICDAR-2013 online HCCR competition dataset using our proposed network, and it
is found that the proposed approach requires only 0.57 MB for storage, whereas
state-of-the-art CNN-based methods require up to 135 MB; meanwhile the
performance is decreased only by 0.91%.
ResumeVis: A Visual Analytics System to Discover Semantic Information in Semi-structured Resume Data
Authors: Chen Zhang, Hao Wang, Yingcai Wu
Date: 2017-05-15 00:00:00

Massive public resume data emerging on the WWW indicates individual-related
characteristics in terms of profile and career experiences. Resume Analysis
(RA) provides opportunities for many applications, such as talent seeking and
evaluation. Existing RA studies based on statistical analyzing have primarily
focused on talent recruitment by identifying explicit attributes. However, they
failed to discover the implicit semantic information, i.e., individual career
progress patterns and social-relations, which are vital to comprehensive
understanding of career development. Besides, how to visualize them for better
human cognition is also challenging. To tackle these issues, we propose a
visual analytics system ResumeVis to mine and visualize resume data. Firstly, a
text-mining based approach is presented to extract semantic information. Then,
a set of visualizations are devised to represent the semantic information in
multiple perspectives. By interactive exploration on ResumeVis performed by
domain experts, the following tasks can be accomplished: to trace individual
career evolving trajectory; to mine latent social-relations among individuals;
and to hold the full picture of massive resumes' collective mobility. Case
studies with over 2500 online officer resumes demonstrate the effectiveness of
our system. We provide a demonstration video.
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
Authors: Ivan Kiskin, Bernardo Pérez Orozco, Theo Windebank, Davide Zilli, Marianne Sinka, Kathy Willis, Stephen Roberts
Date: 2017-05-15 00:00:00
Tasks: Deep Learning, Event Detection, Informativeness, Time Series, Time Series Analysis

Many real-world time-series analysis problems are characterised by scarce
data. Solutions typically rely on hand-crafted features extracted from the time
or frequency domain allied with classification or regression engines which
condition on this (often low-dimensional) feature vector. The huge advances
enjoyed by many application domains in recent years have been fuelled by the
use of deep learning architectures trained on large data sets. This paper
presents an application of deep learning for acoustic event detection in a
challenging, data-scarce, real-world problem. Our candidate challenge is to
accurately detect the presence of a mosquito from its acoustic signature. We
develop convolutional neural networks (CNNs) operating on wavelet
transformations of audio recordings. Furthermore, we interrogate the network's
predictive power by visualising statistics of network-excitatory samples. These
visualisations offer a deep insight into the relative informativeness of
components in the detection problem. We include comparisons with conventional
classifiers, conditioned on both hand-tuned and generic features, to stress the
strength of automatic deep feature learning. Detection is achieved with
performance metrics significantly surpassing those of existing algorithmic
methods, as well as marginally exceeding those attained by individual human
experts.
Emotion in Reinforcement Learning Agents and Robots: A Survey
Authors: Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
Date: 2017-05-15 00:00:00
Tasks: AI Agent, Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

This article provides the first survey of computational models of emotion in
reinforcement learning (RL) agents. The survey focuses on agent/robot emotions,
and mostly ignores human user emotions. Emotions are recognized as functional
in decision-making by influencing motivation and action selection. Therefore,
computational emotion models are usually grounded in the agent's decision
making architecture, of which RL is an important subclass. Studying emotions in
RL-based agents is useful for three research fields. For machine learning (ML)
researchers, emotion models may improve learning efficiency. For the
interactive ML and human-robot interaction (HRI) community, emotions can
communicate state and enhance user investment. Lastly, it allows affective
modelling (AM) researchers to investigate their emotion theories in a
successful AI agent class. This survey provides background on emotion theory
and RL. It systematically addresses 1) from what underlying dimensions (e.g.,
homeostasis, appraisal) emotions can be derived and how these can be modelled
in RL-agents, 2) what types of emotions have been derived from these
dimensions, and 3) how these emotions may either influence the learning
efficiency of the agent or be useful as social signals. We also systematically
compare evaluation criteria, and draw connections to important RL sub-domains
like (intrinsic) motivation and model-based RL. In short, this survey provides
both a practical overview for engineers wanting to implement emotions in their
RL agents, and identifies challenges and directions for future emotion-RL
research.
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses
Authors: Jiaqian Yu, Matthew Blaschko
Date: 2015-12-24 00:00:00

Learning with non-modular losses is an important problem when sets of
predictions are made simultaneously. The main tools for constructing convex
surrogate loss functions for set prediction are margin rescaling and slack
rescaling. In this work, we show that these strategies lead to tight convex
surrogates iff the underlying loss function is increasing in the number of
incorrect predictions. However, gradient or cutting-plane computation for these
functions is NP-hard for non-supermodular loss functions. We propose instead a
novel surrogate loss function for submodular losses, the Lov\'asz hinge, which
leads to O(p log p) complexity with O(p) oracle accesses to the loss function
to compute a gradient or cutting-plane. We prove that the Lov\'asz hinge is
convex and yields an extension. As a result, we have developed the first
tractable convex surrogates in the literature for submodular losses. We
demonstrate the utility of this novel convex surrogate through several set
prediction tasks, including on the PASCAL VOC and Microsoft COCO datasets.
A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment
Authors: Qingbo Wu, Hongliang Li, Fanman Meng, King N. Ngan
Date: 2017-05-15 00:00:00
Tasks: Image Quality Assessment

In the field of objective image quality assessment (IQA), the Spearman's
$\rho$ and Kendall's $\tau$ are two most popular rank correlation indicators,
which straightforwardly assign uniform weight to all quality levels and assume
each pair of images are sortable. They are successful for measuring the average
accuracy of an IQA metric in ranking multiple processed images. However, two
important perceptual properties are ignored by them as well. Firstly, the
sorting accuracy (SA) of high quality images are usually more important than
the poor quality ones in many real world applications, where only the
top-ranked images would be pushed to the users. Secondly, due to the subjective
uncertainty in making judgement, two perceptually similar images are usually
hardly sortable, whose ranks do not contribute to the evaluation of an IQA
metric. To more accurately compare different IQA algorithms, we explore a
perceptually weighted rank correlation indicator in this paper, which rewards
the capability of correctly ranking high quality images, and suppresses the
attention towards insensitive rank mistakes. More specifically, we focus on
activating `valid' pairwise comparison towards image quality, whose difference
exceeds a given sensory threshold (ST). Meanwhile, each image pair is assigned
an unique weight, which is determined by both the quality level and rank
deviation. By modifying the perception threshold, we can illustrate the sorting
accuracy with a more sophisticated SA-ST curve, rather than a single rank
correlation coefficient. The proposed indicator offers a new insight for
interpreting visual perception behaviors. Furthermore, the applicability of our
indicator is validated in recommending robust IQA metrics for both the degraded
and enhanced image data.