pwc-archive/papers-with-abstracts huggingface dataset:
Token-based Function Computation with Memory
Authors: Saber Salehkaleybar, S. Jamaloddin Golestani
Date: 2017-03-26 00:00:00

In distributed function computation, each node has an initial value and the
goal is to compute a function of these values in a distributed manner. In this
paper, we propose a novel token-based approach to compute a wide class of
target functions to which we refer as "Token-based function Computation with
Memory" (TCM) algorithm. In this approach, node values are attached to tokens
and travel across the network. Each pair of travelling tokens would coalesce
when they meet, forming a token with a new value as a function of the original
token values. In contrast to the Coalescing Random Walk (CRW) algorithm, where
token movement is governed by random walk, meeting of tokens in our scheme is
accelerated by adopting a novel chasing mechanism. We proved that, compared to
the CRW algorithm, the TCM algorithm results in a reduction of time complexity
by a factor of at least $\sqrt{n/\log(n)}$ in Erd\"os-Renyi and complete
graphs, and by a factor of $\log(n)/\log(\log(n))$ in torus networks.
Simulation results show that there is at least a constant factor improvement in
the message complexity of TCM algorithm in all considered topologies.
Robustness of the CRW and TCM algorithms in the presence of node failure is
analyzed. We show that their robustness can be improved by running multiple
instances of the algorithms in parallel.
Deleting and Testing Forbidden Patterns in Multi-Dimensional Arrays
Authors: Omri Ben-Eliezer, Simon Korman, Daniel Reichman
Date: 2016-07-13 00:00:00
Tasks: LEMMA, Open-Ended Question Answering

Understanding the local behaviour of structured multi-dimensional data is a
fundamental problem in various areas of computer science. As the amount of data
is often huge, it is desirable to obtain sublinear time algorithms, and
specifically property testers, to understand local properties of the data.
  We focus on the natural local problem of testing pattern freeness: given a
large $d$-dimensional array $A$ and a fixed $d$-dimensional pattern $P$ over a
finite alphabet, we say that $A$ is $P$-free if it does not contain a copy of
the forbidden pattern $P$ as a consecutive subarray. The distance of $A$ to
$P$-freeness is the fraction of entries of $A$ that need to be modified to make
it $P$-free. For any $\epsilon \in [0,1]$ and any large enough pattern $P$ over
any alphabet, other than a very small set of exceptional patterns, we design a
tolerant tester that distinguishes between the case that the distance is at
least $\epsilon$ and the case that it is at most $a_d \epsilon$, with query
complexity and running time $c_d \epsilon^{-1}$, where $a_d < 1$ and $c_d$
depend only on $d$.
  To analyze the testers we establish several combinatorial results, including
the following $d$-dimensional modification lemma, which might be of independent
interest: for any large enough pattern $P$ over any alphabet (excluding a small
set of exceptional patterns for the binary case), and any array $A$ containing
a copy of $P$, one can delete this copy by modifying one of its locations
without creating new $P$-copies in $A$.
  Our results address an open question of Fischer and Newman, who asked whether
there exist efficient testers for properties related to tight substructures in
multi-dimensional structured data. They serve as a first step towards a general
understanding of local properties of multi-dimensional arrays, as any such
property can be characterized by a fixed family of forbidden patterns.
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
Authors: Ayushman Dash, John Cristian Borges Gamboa, Sheraz Ahmed, Marcus Liwicki, Muhammad Zeshan Afzal
Date: 2017-03-19 00:00:00
Tasks: Diversity, Generative Adversarial Network, MS-SSIM, SSIM

In this work, we present the Text Conditioned Auxiliary Classifier Generative
Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network
(GAN) for synthesizing images from their text descriptions. Former approaches
have tried to condition the generative process on the textual data; but allying
it to the usage of class information, known to diversify the generated samples
and improve their structural coherence, has not been explored. We trained the
presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the
discriminability of the generated images with Inception-Score, as well as their
diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our
approach outperforms the state-of-the-art models, i.e., its inception score is
3.45, corresponding to a relative increase of 7.8% compared to the recently
introduced StackGan. A comparison of the mean MS-SSIM scores of the training
and generated samples per class shows that our approach is able to generate
highly diverse images with an average MS-SSIM of 0.14 over all generated
classes.
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision
Authors: Bar Hilleli, Ran El-Yaniv
Date: 2016-12-04 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a scheme for training a computerized agent to perform complex
human tasks such as highway steering. The scheme is designed to follow a
natural learning process whereby a human instructor teaches a computerized
trainee. The learning process consists of five elements: (i) unsupervised
feature learning; (ii) supervised imitation learning; (iii) supervised reward
induction; (iv) supervised safety module construction; and (v) reinforcement
learning. We implemented the last four elements of the scheme using deep
convolutional networks and applied it to successfully create a computerized
agent capable of autonomous highway steering over the well-known racing game
Assetto Corsa. We demonstrate that the use of the last four elements is
essential to effectively carry out the steering task using vision alone,
without access to a driving simulator internals, and operating in wall-clock
time. This is made possible also through the introduction of a safety network,
a novel way for preventing the agent from performing catastrophic mistakes
during the reinforcement learning stage.
Structured Learning of Tree Potentials in CRF for Image Segmentation
Authors: Fayao Liu, Guosheng Lin, Ruizhi Qiao, Chunhua Shen
Date: 2017-03-26 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

We propose a new approach to image segmentation, which exploits the
advantages of both conditional random fields (CRFs) and decision trees. In the
literature, the potential functions of CRFs are mostly defined as a linear
combination of some pre-defined parametric models, and then methods like
structured support vector machines (SSVMs) are applied to learn those linear
coefficients. We instead formulate the unary and pairwise potentials as
nonparametric forests---ensembles of decision trees, and learn the ensemble
parameters and the trees in a unified optimization problem within the
large-margin framework. In this fashion, we easily achieve nonlinear learning
of potential functions on both unary and pairwise terms in CRFs. Moreover, we
learn class-wise decision trees for each object that appears in the image. Due
to the rich structure and flexibility of decision trees, our approach is
powerful in modelling complex data likelihoods and label relationships. The
resulting optimization problem is very challenging because it can have
exponentially many variables and constraints. We show that this challenging
optimization can be efficiently solved by combining a modified column
generation and cutting-planes techniques. Experimental results on both binary
(Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC
2012) segmentation datasets demonstrate the power of the learned nonlinear
nonparametric potentials.
Team Formation for Scheduling Educational Material in Massive Online Classes
Authors: Sanaz Bahargam, Dóra Erdos, Azer Bestavros, Evimaria Terzi
Date: 2017-03-26 00:00:00
Tasks: Scheduling

Whether teaching in a classroom or a Massive Online Open Course it is crucial
to present the material in a way that benefits the audience as a whole. We
identify two important tasks to solve towards this objective, 1 group students
so that they can maximally benefit from peer interaction and 2 find an optimal
schedule of the educational material for each group. Thus, in this paper, we
solve the problem of team formation and content scheduling for education. Given
a time frame d, a set of students S with their required need to learn different
activities T and given k as the number of desired groups, we study the problem
of finding k group of students. The goal is to teach students within time frame
d such that their potential for learning is maximized and find the best
schedule for each group. We show this problem to be NP-hard and develop a
polynomial algorithm for it. We show our algorithm to be effective both on
synthetic as well as a real data set. For our experiments, we use real data on
students' grades in a Computer Science department. As part of our contribution,
we release a semi-synthetic dataset that mimics the properties of the real
data.
Saliency-guided video classification via adaptively weighted learning
Authors: Yunzhen Zhao, Yuxin Peng
Date: 2017-03-23 00:00:00
Tasks: Classification, General Classification, Optical Flow Estimation, Video Classification

Video classification is productive in many practical applications, and the
recent deep learning has greatly improved its accuracy. However, existing works
often model video frames indiscriminately, but from the view of motion, video
frames can be decomposed into salient and non-salient areas naturally. Salient
and non-salient areas should be modeled with different networks, for the former
present both appearance and motion information, and the latter present static
background information. To address this problem, in this paper, video saliency
is predicted by optical flow without supervision firstly. Then two streams of
3D CNN are trained individually for raw frames and optical flow on salient
areas, and another 2D CNN is trained for raw frames on non-salient areas. For
the reason that these three streams play different roles for each class, the
weights of each stream are adaptively learned for each class. Experimental
results show that saliency-guided modeling and adaptively weighted learning can
reinforce each other, and we achieve the state-of-the-art results.
LEPOR: An Augmented Machine Translation Evaluation Metric
Authors: Lifeng Han
Date: 2017-03-26 00:00:00
Tasks: Machine Translation, POS, Translation

Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicate and apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks. In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances on different languages. Firstly, we design augmented factors to yield highly accurate evaluation. Secondly, we design a tunable evaluation model where weighting of factors can be optimized according to the characteristics of languages. Thirdly, in the enhanced version of our methods, we design concise linguistic feature using part-of-speech (POS) to show that our methods can yield even higher performance when using some external linguistic resources. Finally, we introduce the practical performance of our metrics in the ACL-WMT workshop shared tasks, which show that the proposed methods are robust across different languages. In addition, we also present some novel work on quality estimation of MT without using reference translations including the usage of probability models of Na\"ive Bayes (NB), support vector machine (SVM) classification algorithms, and CRFs.
Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients
Authors: Jinying Chen, Hong Yu
Date: 2017-03-01 00:00:00

Background: Electronic health record (EHR) notes contain abundant medical
jargon that can be difficult for patients to comprehend. One way to help
patients is to reduce information overload and help them focus on medical terms
that matter most to them.
  Objective: The aim of this work was to develop FIT (Finding Important Terms
for patients), an unsupervised natural language processing (NLP) system that
ranks medical terms in EHR notes based on their importance to patients.
  Methods: We built FIT on a new unsupervised ensemble ranking model derived
from the biased random walk algorithm to combine heterogeneous information
resources for ranking candidate terms from each EHR note. Specifically, FIT
integrates four single views for term importance: patient use of medical
concepts, document-level term salience, word-occurrence based term relatedness,
and topic coherence. It also incorporates partial information of term
importance as conveyed by terms' unfamiliarity levels and semantic types. We
evaluated FIT on 90 expert-annotated EHR notes and compared it with three
benchmark unsupervised ensemble ranking methods.
  Results: FIT achieved 0.885 AUC-ROC for ranking candidate terms from EHR
notes to identify important terms. When including term identification, the
performance of FIT for identifying important terms from EHR notes was 0.813
AUC-ROC. It outperformed the three ensemble rankers for most metrics. Its
performance is relatively insensitive to its parameter.
  Conclusions: FIT can automatically identify EHR terms important to patients
and may help develop personalized interventions to improve quality of care. By
using unsupervised learning as well as a robust and flexible framework for
information fusion, FIT can be readily applied to other domains and
applications.
Learning to Predict: A Fast Re-constructive Method to Generate Multimodal Embeddings
Authors: Guillem Collell, Teddy Zhang, Marie-Francine Moens
Date: 2017-03-25 00:00:00

Integrating visual and linguistic information into a single multimodal
representation is an unsolved problem with wide-reaching applications to both
natural language processing and computer vision. In this paper, we present a
simple method to build multimodal representations by learning a
language-to-vision mapping and using its output to build multimodal embeddings.
In this sense, our method provides a cognitively plausible way of building
representations, consistent with the inherently re-constructive and associative
nature of human memory. Using seven benchmark concept similarity tests we show
that the mapped vectors not only implicitly encode multimodal information, but
also outperform strong unimodal baselines and state-of-the-art multimodal
methods, thus exhibiting more "human-like" judgments---particularly in
zero-shot settings.
Visual Discovery at Pinterest
Authors: Andrew Zhai, Dmitry Kislyuk, Yushi Jing, Michael Feng, Eric Tzeng, Jeff Donahue, Yue Li Du, Trevor Darrell
Date: 2017-02-15 00:00:00
Tasks: object-detection, Object Detection

Over the past three years Pinterest has experimented with several visual
search and recommendation services, including Related Pins (2014), Similar
Looks (2015), Flashlight (2016) and Lens (2017). This paper presents an
overview of our visual discovery engine powering these services, and shares the
rationales behind our technical and product decisions such as the use of object
detection and interactive user interfaces. We conclude that this visual
discovery engine significantly improves engagement in both search and
recommendation tasks.
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
Authors: Luka Čehovin Zajc, Alan Lukežič, Aleš Leonardis, Matej Kristan
Date: 2016-12-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Object Tracking, Visual Object Tracking

Object-to-camera motion produces a variety of apparent motion patterns that
significantly affect performance of short-term visual trackers. Despite being
crucial for designing robust trackers, their influence is poorly explored in
standard benchmarks due to weakly defined, biased and overlapping attribute
annotations. In this paper we propose to go beyond pre-recorded benchmarks with
post-hoc annotations by presenting an approach that utilizes omnidirectional
videos to generate realistic, consistently annotated, short-term tracking
scenarios with exactly parameterized motion patterns. We have created an
evaluation system, constructed a fully annotated dataset of omnidirectional
videos and the generators for typical motion patterns. We provide an in-depth
analysis of major tracking paradigms which is complementary to the standard
benchmarks and confirms the expressiveness of our evaluation approach.
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
Authors: Junbei Zhang, Xiaodan Zhu, Qian Chen, Li-Rong Dai, Si Wei, Hui Jiang
Date: 2017-03-14 00:00:00
Tasks: Question Answering, Reading Comprehension

The last several years have seen intensive interest in exploring
neural-network-based models for machine comprehension (MC) and question
answering (QA). In this paper, we approach the problems by closely modelling
questions in a neural network framework. We first introduce syntactic
information to help encode questions. We then view and model different types of
questions and the information shared among them as an adaptation task and
proposed adaptation models for them. On the Stanford Question Answering Dataset
(SQuAD), we show that these approaches can help attain better results over a
competitive baseline.
Learning optimal wavelet bases using a neural network approach
Authors: Andreas Søgaard
Date: 2017-03-25 00:00:00

A novel method for learning optimal, orthonormal wavelet bases for
representing 1- and 2D signals, based on parallels between the wavelet
transform and fully connected artificial neural networks, is described. The
structural similarities between these two concepts are reviewed and combined to
a "wavenet", allowing for the direct learning of optimal wavelet filter
coefficient through stochastic gradient descent with back-propagation over
ensembles of training inputs, where conditions on the filter coefficients for
constituting orthonormal wavelet bases are cast as quadratic regularisations
terms. We describe the practical implementation of this method, and study its
performance for high-energy physics collision events for QCD $2 \to 2$
processes. It is shown that an optimal solution is found, even in a
high-dimensional search space, and the implications of the result are
discussed.
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
Authors: Sebastian Gehrmann, Franck Dernoncourt, Yeran Li, Eric T. Carlson, Joy T. Wu, Jonathan Welt, John Foote Jr., Edward T. Moseley, David W. Grant, Patrick D. Tyler, Leo Anthony Celi
Date: 2017-03-25 00:00:00
Tasks: Deep Learning, Patient Phenotyping

Objective: We investigate whether deep learning techniques for natural
language processing (NLP) can be used efficiently for patient phenotyping.
Patient phenotyping is a classification task for determining whether a patient
has a medical condition, and is a crucial part of secondary analysis of
healthcare data. We assess the performance of deep learning algorithms and
compare them with classical NLP approaches.
  Materials and Methods: We compare convolutional neural networks (CNNs),
n-gram models, and approaches based on cTAKES that extract pre-defined medical
concepts from clinical notes and use them to predict patient phenotypes. The
performance is tested on 10 different phenotyping tasks using 1,610 discharge
summaries extracted from the MIMIC-III database.
  Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The
average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our
model having an F1-score up to 37 points higher than alternative approaches. We
additionally assess the interpretability of our model by presenting a method
that extracts the most salient phrases for a particular prediction.
  Conclusion: We show that NLP methods based on deep learning improve the
performance of patient phenotyping. Our CNN-based algorithm automatically
learns the phrases associated with each patient phenotype. As such, it reduces
the annotation complexity for clinical domain experts, who are normally
required to develop task-specific annotation rules and identify relevant
phrases. Our method performs well in terms of both performance and
interpretability, which indicates that deep learning is an effective approach
to patient phenotyping based on clinicians' notes.
Morphological Analysis for the Maltese Language: The Challenges of a Hybrid System
Authors: Claudia Borg, Albert Gatt
Date: 2017-03-25 00:00:00
Proceeding: WS 2017 4
Tasks: Clustering, Morphological Analysis

Maltese is a morphologically rich language with a hybrid morphological system
which features both concatenative and non-concatenative processes. This paper
analyses the impact of this hybridity on the performance of machine learning
techniques for morphological labelling and clustering. In particular, we
analyse a dataset of morphologically related word clusters to evaluate the
difference in results for concatenative and nonconcatenative clusters. We also
describe research carried out in morphological labelling, with a particular
focus on the verb category. Two evaluations were carried out, one using an
unseen dataset, and another one using a gold standard dataset which was
manually labelled. The gold standard dataset was split into concatenative and
non-concatenative to analyse the difference in results between the two
morphological systems.
Improving the Accuracy of the CogniLearn System for Cognitive Behavior Assessment
Authors: Amir Ghaderi, Srujana Gattupalli, Dylan Ebert, Ali Sharifara, Vassilis Athitsos, Fillia Makedon
Date: 2017-03-25 00:00:00

HTKS is a game-like cognitive assessment method, designed for children
between four and eight years of age. During the HTKS assessment, a child
responds to a sequence of requests, such as "touch your head" or "touch your
toes". The cognitive challenge stems from the fact that the children are
instructed to interpret these requests not literally, but by touching a
different body part than the one stated. In prior work, we have developed the
CogniLearn system, that captures data from subjects performing the HTKS game,
and analyzes the motion of the subjects. In this paper we propose some specific
improvements that make the motion analysis module more accurate. As a result of
these improvements, the accuracy in recognizing cases where subjects touch
their toes has gone from 76.46% in our previous work to 97.19% in this paper.