pwc-archive/papers-with-abstracts huggingface dataset:
Persistent-homology-based gait recognition
Authors: J. Lamar-Leon, Raul Alonso-Baryolo, Edel Garcia-Reyes, R. Gonzalez-Diaz
Date: 2017-07-21 00:00:00
Tasks: Gait Recognition, Gender Classification, object-detection, Object Detection

Gait recognition is an important biometric technique for video surveillance
tasks, due to the advantage of using it at distance. In this paper, we present
a persistent homology-based method to extract topological features (the
so-called {\it topological gait signature}) from the the body silhouettes of a
gait sequence. % It has been used before in several conference papers of the
same authors for human identification, gender classification, carried object
detection and monitoring human activities at distance. % The novelty of this
paper is the study of the stability of the topological gait signature under
small perturbations and the number of gait cycles contained in a gait sequence.
In other words, we show that the topological gait signature is robust to the
presence of noise in the body silhouettes and to the number of gait cycles
contained in a given gait sequence. % We also show that computing our
topological gait signature of only the lowest fourth part of the body
silhouette, we avoid the upper body movements that are unrelated to the natural
dynamic of the gait, caused for example by carrying a bag or wearing a coat.
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
Authors: William Lotter, Greg Sorensen, David Cox
Date: 2017-07-21 00:00:00
Tasks: General Classification

Screening mammography is an important front-line tool for the early detection
of breast cancer, and some 39 million exams are conducted each year in the
United States alone. Here, we describe a multi-scale convolutional neural
network (CNN) trained with a curriculum learning strategy that achieves high
levels of accuracy in classifying mammograms. Specifically, we first train
CNN-based patch classifiers on segmentation masks of lesions in mammograms, and
then use the learned features to initialize a scanning-based model that renders
a decision on the whole image, trained end-to-end on outcome data. We
demonstrate that our approach effectively handles the "needle in a haystack"
nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSM
dataset.
Split and Rephrase
Authors: Shashi Narayan, Claire Gardent, Shay B. Cohen, Anastasia Shimorina
Date: 2017-07-21 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Sentence, Split and Rephrase, Translation

We propose a new sentence simplification task (Split-and-Rephrase) where the
aim is to split a complex sentence into a meaning preserving sequence of
shorter sentences. Like sentence simplification, splitting-and-rephrasing has
the potential of benefiting both natural language processing and societal
applications. Because shorter sentences are generally better processed by NLP
systems, it could be used as a preprocessing step which facilitates and
improves the performance of parsers, semantic role labellers and machine
translation systems. It should also be of use for people with reading
disabilities because it allows the conversion of longer sentences into shorter
ones. This paper makes two contributions towards this new task. First, we
create and make available a benchmark consisting of 1,066,115 tuples mapping a
single complex sentence to a sequence of sentences expressing the same meaning.
Second, we propose five models (vanilla sequence-to-sequence to
semantically-motivated models) to understand the difficulty of the proposed
task.
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis
Authors: Seongah Jeong, Xiang Li, Jiarui Yang, Quanzheng Li, Vahid Tarokh
Date: 2017-07-21 00:00:00
Tasks: Denoising, Dictionary Learning, Functional Connectivity

We propose a novel denoising framework for task functional Magnetic Resonance
Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the
brain functional connectivity via dictionary learning and sparse coding (DLSC).
In order to address the limitations of the unsupervised DLSC-based fMRI
studies, we utilize the prior knowledge of task paradigm in the learning step
to train a data-driven dictionary and to model the sparse representation. We
apply the proposed DLSC-based method to Human Connectome Project (HCP) motor
tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar
circuits in somatomotor networks show that the DLSC-based denoising framework
can significantly improve the prominent connectivity patterns, in comparison to
the temporal non-local means (tNLM)-based denoising method as well as the case
without denoising, which is consistent and neuroscientifically meaningful
within motor area. The promising results show that the proposed method can
provide an important foundation for the high-resolution functional connectivity
analysis, and provide a better approach for fMRI preprocessing.
Mimicking Word Embeddings using Subword RNNs
Authors: Yuval Pinter, Robert Guthrie, Jacob Eisenstein
Date: 2017-07-21 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Word Embeddings

Word embeddings improve generalization over lexical features by placing each
word in a lower-dimensional space, using distributional information obtained
from unlabeled data. However, the effectiveness of word embeddings for
downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which
embeddings do not exist. In this paper, we present MIMICK, an approach to
generating OOV word embeddings compositionally, by learning a function from
spellings to distributional embeddings. Unlike prior work, MIMICK does not
require re-training on the original word embedding corpus; instead, learning is
performed at the type level. Intrinsic and extrinsic evaluations demonstrate
the power of this simple approach. On 23 languages, MIMICK improves performance
over a word-based baseline for tagging part-of-speech and morphosyntactic
attributes. It is competitive with (and complementary to) a supervised
character-based model in low-resource settings.
A Framework for Easing the Development of Applications Embedding Answer Set Programming
Authors: Francesco Calimeri, Davide Fuscà, Stefano Germano, Simona Perri, Jessica Zangari
Date: 2017-07-21 00:00:00

Answer Set Programming (ASP) is a well-established declarative problem
solving paradigm which became widely used in AI and recognized as a powerful
tool for knowledge representation and reasoning (KRR), especially for its high
expressiveness and the ability to deal also with incomplete knowledge.
  Recently, thanks to the availability of a number of robust and efficient
implementations, ASP has been increasingly employed in a number of different
domains, and used for the development of industrial-level and enterprise
applications. This made clear the need for proper development tools and
interoperability mechanisms for easing interaction and integration with
external systems in the widest range of real-world scenarios, including mobile
applications and educational contexts.
  In this work we present a framework for integrating the KRR capabilities of
ASP into generic applications. We show the use of the framework by illustrating
proper specializations for some relevant ASP systems over different platforms,
including the mobile setting; furthermore, the potential of the framework for
educational purposes is illustrated by means of the development of several
ASP-based applications.
Reconstruction of Word Embeddings from Sub-Word Parameters
Authors: Karl Stratos
Date: 2017-07-21 00:00:00
Proceeding: WS 2017 9
Tasks: Part-Of-Speech Tagging, Word Embeddings, Word Similarity

Pre-trained word embeddings improve the performance of a neural model at the
cost of increasing the model size. We propose to benefit from this resource
without paying the cost by operating strictly at the sub-lexical level. Our
approach is quite simple: before task-specific training, we first optimize
sub-word parameters to reconstruct pre-trained word embeddings using various
distance measures. We report interesting results on a variety of tasks: word
similarity, word analogy, and part-of-speech tagging.
Entity Identification as Multitasking
Authors: Karl Stratos
Date: 2016-12-08 00:00:00
Proceeding: WS 2017 9
Tasks: Boundary Detection, Type prediction, Vocal Bursts Type Prediction

Standard approaches in entity identification hard-code boundary detection and
type prediction into labels (e.g., John/B-PER Smith/I-PER) and then perform
Viterbi. This has two disadvantages: 1. the runtime complexity grows
quadratically in the number of types, and 2. there is no natural segment-level
representation. In this paper, we propose a novel neural architecture that
addresses these disadvantages. We frame the problem as multitasking, separating
boundary detection and type prediction but optimizing them jointly. Despite its
simplicity, this architecture performs competitively with fully structured
models such as BiLSTM-CRFs while scaling linearly in the number of types.
Furthermore, by construction, the model induces type-disambiguating embeddings
of predicted mentions.
A Sub-Character Architecture for Korean Language Processing
Authors: Karl Stratos
Date: 2017-07-20 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Parsing

We introduce a novel sub-character architecture that exploits a unique
compositional structure of the Korean language. Our method decomposes each
character into a small set of primitive phonetic units called jamo letters from
which character- and word-level representations are induced. The jamo letters
divulge syntactic and semantic information that is difficult to access with
conventional character-level units. They greatly alleviate the data sparsity
problem, reducing the observation space to 1.6% of the original while
increasing accuracy in our experiments. We apply our architecture to dependency
parsing and achieve dramatic improvement over strong lexical baselines.
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Authors: Ivan Vulić, Nikola Mrkšić, Anna Korhonen
Date: 2017-07-21 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Clustering, Cross-Lingual Transfer, Feature Engineering, General Classification, Learning Word Embeddings, Translation, Word Embeddings

Existing approaches to automatic VerbNet-style verb classification are
heavily dependent on feature engineering and therefore limited to languages
with mature NLP pipelines. In this work, we propose a novel cross-lingual
transfer method for inducing VerbNets for multiple languages. To the best of
our knowledge, this is the first study which demonstrates how the architectures
for learning word embeddings can be applied to this challenging
syntactic-semantic task. Our method uses cross-lingual translation pairs to tie
each of the six target languages into a bilingual vector space with English,
jointly specialising the representations to encode the relational information
from English VerbNet. A standard clustering algorithm is then run on top of the
VerbNet-specialised representations, using vector dimensions as features for
learning verb classes. Our results show that the proposed cross-lingual
transfer approach sets new state-of-the-art verb classification performance
across all six target languages explored in this work.
Autocompletion interfaces make crowd workers slower, but their use promotes response diversity
Authors: Xipei Liu, James P. Bagrow
Date: 2017-07-21 00:00:00
Tasks: Diversity

Creative tasks such as ideation or question proposal are powerful
applications of crowdsourcing, yet the quantity of workers available for
addressing practical problems is often insufficient. To enable scalable
crowdsourcing thus requires gaining all possible efficiency and information
from available workers. One option for text-focused tasks is to allow assistive
technology, such as an autocompletion user interface (AUI), to help workers
input text responses. But support for the efficacy of AUIs is mixed. Here we
designed and conducted a randomized experiment where workers were asked to
provide short text responses to given questions. Our experimental goal was to
determine if an AUI helps workers respond more quickly and with improved
consistency by mitigating typos and misspellings. Surprisingly, we found that
neither occurred: workers assigned to the AUI treatment were slower than those
assigned to the non-AUI control and their responses were more diverse, not
less, than those of the control. Both the lexical and semantic diversities of
responses were higher, with the latter measured using word2vec. A crowdsourcer
interested in worker speed may want to avoid using an AUI, but using an AUI to
boost response diversity may be valuable to crowdsourcers interested in
receiving as much novel information from workers as possible.
A study on text-score disagreement in online reviews
Authors: Michela Fazzolari, Vittoria Cozza, Marinella Petrocchi, Angelo Spognardi
Date: 2017-07-21 00:00:00
Tasks: Sentiment Analysis

In this paper, we focus on online reviews and employ artificial intelligence
tools, taken from the cognitive computing field, to help understanding the
relationships between the textual part of the review and the assigned numerical
score. We move from the intuitions that 1) a set of textual reviews expressing
different sentiments may feature the same score (and vice-versa); and 2)
detecting and analyzing the mismatches between the review content and the
actual score may benefit both service providers and consumers, by highlighting
specific factors of satisfaction (and dissatisfaction) in texts.
  To prove the intuitions, we adopt sentiment analysis techniques and we
concentrate on hotel reviews, to find polarity mismatches therein. In
particular, we first train a text classifier with a set of annotated hotel
reviews, taken from the Booking website. Then, we analyze a large dataset, with
around 160k hotel reviews collected from Tripadvisor, with the aim of detecting
a polarity mismatch, indicating if the textual content of the review is in
line, or not, with the associated score.
  Using well established artificial intelligence techniques and analyzing in
depth the reviews featuring a mismatch between the text polarity and the score,
we find that -on a scale of five stars- those reviews ranked with middle scores
include a mixture of positive and negative aspects.
  The approach proposed here, beside acting as a polarity detector, provides an
effective selection of reviews -on an initial very large dataset- that may
allow both consumers and providers to focus directly on the review subset
featuring a text/score disagreement, which conveniently convey to the user a
summary of positive and negative features of the review target.
Machine Learning for Structured Clinical Data
Authors: Brett K. Beaulieu-Jones
Date: 2017-07-21 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Research is a tertiary priority in the EHR, where the priorities are patient
care and billing. Because of this, the data is not standardized or formatted in
a manner easily adapted to machine learning approaches. Data may be missing for
a large variety of reasons ranging from individual input styles to differences
in clinical decision making, for example, which lab tests to issue. Few
patients are annotated at a research quality, limiting sample size and
presenting a moving gold standard. Patient progression over time is key to
understanding many diseases but many machine learning algorithms require a
snapshot, at a single time point, to create a usable vector form. Furthermore,
algorithms that produce black box results do not provide the interpretability
required for clinical adoption. This chapter discusses these challenges and
others in applying machine learning techniques to the structured EHR (i.e.
Patient Demographics, Family History, Medication Information, Vital Signs,
Laboratory Tests, Genetic Testing). It does not cover feature extraction from
additional sources such as imaging data or free text patient notes but the
approaches discussed can include features extracted from these sources.
Maximum Likelihood Estimation based on Random Subspace EDA: Application to Extrasolar Planet Detection
Authors: Bin Liu, Ke-Jia Chen
Date: 2017-04-18 00:00:00

This paper addresses maximum likelihood (ML) estimation based model fitting
in the context of extrasolar planet detection. This problem is featured by the
following properties: 1) the candidate models under consideration are highly
nonlinear; 2) the likelihood surface has a huge number of peaks; 3) the
parameter space ranges in size from a few to dozens of dimensions. These
properties make the ML search a very challenging problem, as it lacks any
analytical or gradient based searching solution to explore the parameter space.
A population based searching method, called estimation of distribution
algorithm (EDA), is adopted to explore the model parameter space starting from
a batch of random locations. EDA is featured by its ability to reveal and
utilize problem structures. This property is desirable for characterizing the
detections. However, it is well recognized that EDAs can not scale well to
large scale problems, as it consists of iterative random sampling and model
fitting procedures, which results in the well-known dilemma curse of
dimensionality. A novel mechanism to perform EDAs in interactive random
subspaces spanned by correlated variables is proposed and the hope is to
alleviate the curse of dimensionality for EDAs by performing the operations of
sampling and model fitting in lower dimensional subspaces. The effectiveness of
the proposed algorithm is verified via both benchmark numerical studies and
real data analysis.
Image Companding and Inverse Halftoning using Deep Convolutional Neural Networks
Authors: Xianxu Hou, Guoping Qiu
Date: 2017-07-01 00:00:00
Tasks: Deep Learning

In this paper, we introduce deep learning technology to tackle two
traditional low-level image processing problems, companding and inverse
halftoning. We make two main contributions. First, to the best knowledge of the
authors, this is the first work that has successfully developed deep learning
based solutions to these two traditional low-level image processing problems.
This not only introduces new methods to tackle well-known image processing
problems but also demonstrates the power of deep learning in solving
traditional signal processing problems. Second, we have developed an effective
deep learning algorithm based on insights into the properties of visual quality
of images and the internal representation properties of a deep convolutional
neural network (CNN). We train a deep CNN as a nonlinear transformation
function to map a low bit depth image to higher bit depth or from a halftone
image to a continuous tone image. We also employ another pretrained deep CNN as
a feature extractor to derive visually important features to construct the
objective function for the training of the mapping CNN. We present experimental
results to demonstrate the effectiveness of the new deep learning based
solutions.
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps
Authors: Tobias Falke, Iryna Gurevych
Date: 2017-04-14 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Document Summarization, Multi-Document Summarization

Concept maps can be used to concisely represent important information and
bring structure into large document collections. Therefore, we study a variant
of multi-document summarization that produces summaries in the form of concept
maps. However, suitable evaluation datasets for this task are currently
missing. To close this gap, we present a newly created corpus of concept maps
that summarize heterogeneous collections of web documents on educational
topics. It was created using a novel crowdsourcing approach that allows us to
efficiently determine important elements in large document collections. We
release the corpus along with a baseline system and proposed evaluation
protocol to enable further research on this variant of summarization.
Towards learning domain-independent planning heuristics
Authors: Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo, Antonio Bucchiarone
Date: 2017-07-21 00:00:00

Automated planning remains one of the most general paradigms in Artificial
Intelligence, providing means of solving problems coming from a wide variety of
domains. One of the key factors restricting the applicability of planning is
its computational complexity resulting from exponentially large search spaces.
Heuristic approaches are necessary to solve all but the simplest problems. In
this work, we explore the possibility of obtaining domain-independent heuristic
functions using machine learning. This is a part of a wider research program
whose objective is to improve practical applicability of planning in systems
for which the planning domains evolve at run time. The challenge is therefore
the learning of (corrections of) domain-independent heuristics that can be
reused across different planning domains.
Neural Lattice-to-Sequence Models for Uncertain Inputs
Authors: Matthias Sperber, Graham Neubig, Jan Niehues, Alex Waibel
Date: 2017-04-03 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Translation

The input to a neural sequence-to-sequence model is often determined by an
up-stream system, e.g. a word segmenter, part of speech tagger, or speech
recognizer. These up-stream models are potentially error-prone. Representing
inputs through word lattices allows making this uncertainty explicit by
capturing alternative sequences and their posterior probabilities in a compact
form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a
LatticeLSTM that is able to consume word lattices, and can be used as encoder
in an attentional encoder-decoder model. We integrate lattice posterior scores
into this architecture by extending the TreeLSTM's child-sum and forget gates
and introducing a bias term into the attention mechanism. We experiment with
speech translation lattices and report consistent improvements over baselines
that translate either the 1-best hypothesis or the lattice without posterior
scores.
A Distributional Perspective on Reinforcement Learning
Authors: Marc G. Bellemare, Will Dabney, Rémi Munos
Date: 2017-07-21 00:00:00
Proceeding: ICML 2017 8
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we argue for the fundamental importance of the value
distribution: the distribution of the random return received by a reinforcement
learning agent. This is in contrast to the common approach to reinforcement
learning which models the expectation of this return, or value. Although there
is an established body of literature studying the value distribution, thus far
it has always been used for a specific purpose such as implementing risk-aware
behaviour. We begin with theoretical results in both the policy evaluation and
control settings, exposing a significant distributional instability in the
latter. We then use the distributional perspective to design a new algorithm
which applies Bellman's equation to the learning of approximate value
distributions. We evaluate our algorithm using the suite of games from the
Arcade Learning Environment. We obtain both state-of-the-art results and
anecdotal evidence demonstrating the importance of the value distribution in
approximate reinforcement learning. Finally, we combine theoretical and
empirical evidence to highlight the ways in which the value distribution
impacts learning in the approximate setting.