pwc-archive/papers-with-abstracts huggingface dataset:
Injecting Relational Structural Representation in Neural Networks for Question Similarity
Authors: Antonio Uva, Daniele Bonadiman, Alessandro Moschitti
Date: 2018-06-20 00:00:00
Proceeding: ACL 2018 7
Tasks: Question Similarity

Effectively using full syntactic parsing information in Neural Networks (NNs)
to solve relational tasks, e.g., question similarity, is still an open problem.
In this paper, we propose to inject structural representations in NNs by (i)
learning an SVM model using Tree Kernels (TKs) on relatively few pairs of
questions (few thousands) as gold standard (GS) training data is typically
scarce, (ii) predicting labels on a very large corpus of question pairs, and
(iii) pre-training NNs on such large corpus. The results on Quora and SemEval
question similarity datasets show that NNs trained with our approach can learn
more accurate models, especially after fine tuning on GS.
A Supervised Approach To The Interpretation Of Imperative To-Do Lists
Authors: Paul Landes, Barbara Di Eugenio
Date: 2018-06-20 00:00:00
Tasks: Management

To-do lists are a popular medium for personal information management. As
to-do tasks are increasingly tracked in electronic form with mobile and desktop
organizers, so does the potential for software support for the corresponding
tasks by means of intelligent agents. While there has been work in the area of
personal assistants for to-do tasks, no work has focused on classifying user
intention and information extraction as we do. We show that our methods perform
well across two corpora that span sub-domains, one of which we released.
Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism
Authors: Dominique Beaini, Sofiane Achiche, Yann-Seing Law-Kam Cio, Maxime Raison
Date: 2018-06-20 00:00:00

Computer vision is a growing field with a lot of new applications in
automation and robotics, since it allows the analysis of images and shapes for
the generation of numerical or analytical information. One of the most used
method of information extraction is image filtering through convolution
kernels, with each kernel specialized for specific applications. The objective
of this paper is to present a novel convolution kernels, based on principles of
electromagnetic potentials and fields, for a general use in computer vision and
to demonstrate its usage for shape and stroke analysis. Such filtering
possesses unique geometrical properties that can be interpreted using well
understood physics theorems. Therefore, this paper focuses on the development
of the electromagnetic kernels and on their application on images for shape and
stroke analysis. It also presents several interesting features of
electromagnetic kernels, such as resolution, size and orientation independence,
robustness to noise and deformation, long distance stroke interaction and
ability to work with 3D images
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
Authors: Alex D. Pon, Oles Andrienko, Ali Harakeh, Steven L. Waslander
Date: 2018-06-20 00:00:00
Tasks: GPU, Traffic Sign Detection, Traffic Sign Recognition

Traffic light and sign detectors on autonomous cars are integral for road
scene perception. The literature is abundant with deep learning networks that
detect either lights or signs, not both, which makes them unsuitable for
real-life deployment due to the limited graphics processing unit (GPU) memory
and power available on embedded systems. The root cause of this issue is that
no public dataset contains both traffic light and sign labels, which leads to
difficulties in developing a joint detection framework. We present a deep
hierarchical architecture in conjunction with a mini-batch proposal selection
mechanism that allows a network to detect both traffic lights and signs from
training on separate traffic light and sign datasets. Our method solves the
overlapping issue where instances from one dataset are not labelled in the
other dataset. We are the first to present a network that performs joint
detection on traffic lights and signs. We measure our network on the
Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small
Traffic Lights benchmark for traffic light detection and show it outperforms
the existing Bosch Small Traffic light state-of-the-art method. We focus on
autonomous car deployment and show our network is more suitable than others
because of its low memory footprint and real-time image processing time.
Qualitative results can be viewed at https://youtu.be/_YmogPzBXOw
The Corpus Replication Task
Authors: Tobias Eichinger
Date: 2018-06-20 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

In the field of Natural Language Processing (NLP), we revisit the well-known
word embedding algorithm word2vec. Word embeddings identify words by vectors
such that the words' distributional similarity is captured. Unexpectedly,
besides semantic similarity even relational similarity has been shown to be
captured in word embeddings generated by word2vec, whence two questions arise.
Firstly, which kind of relations are representable in continuous space and
secondly, how are relations built. In order to tackle these questions we
propose a bottom-up point of view. We call generating input text for which
word2vec outputs target relations solving the Corpus Replication Task. Deeming
generalizations of this approach to any set of relations possible, we expect
solving of the Corpus Replication Task to provide partial answers to the
questions.
TxPI-u: A Resource for Personality Identification of Undergraduates
Authors: Gabriela Ramírez-de-la-Rosa, Esaú Villatoro-Tello, Héctor Jiménez-Salazar
Date: 2018-06-20 00:00:00

Resources such as labeled corpora are necessary to train automatic models
within the natural language processing (NLP) field. Historically, a large
number of resources regarding a broad number of problems are available mostly
in English. One of such problems is known as Personality Identification where
based on a psychological model (e.g. The Big Five Model), the goal is to find
the traits of a subject's personality given, for instance, a text written by
the same subject. In this paper we introduce a new corpus in Spanish called
Texts for Personality Identification (TxPI). This corpus will help to develop
models to automatically assign a personality trait to an author of a text
document. Our corpus, TxPI-u, contains information of 416 Mexican undergraduate
students with some demographics information such as, age, gender, and the
academic program they are enrolled. Finally, as an additional contribution, we
present a set of baselines to provide a comparison scheme for further research.
Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context
Authors: Lucy Lu Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm, Waleed Ammar
Date: 2018-06-20 00:00:00
Proceeding: WS 2018 7

Ontology alignment is the task of identifying semantically equivalent
entities from two given ontologies. Different ontologies have different
representations of the same entity, resulting in a need to de-duplicate
entities when merging ontologies. We propose a method for enriching entities in
an ontology with external definition and context information, and use this
additional information for ontology alignment. We develop a neural architecture
capable of encoding the additional information when available, and show that
the addition of external data results in an F1-score of 0.69 on the Ontology
Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable
with the entity-level matchers in a SOTA system.
Conditions for Major Transitions in Biological and Cultural Evolution
Authors: Peter D. Turney
Date: 2018-06-20 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Evolution by natural selection can be seen an algorithm for generating
creative solutions to difficult problems. More precisely, evolution by natural
selection is a class of algorithms that share a set of properties. The question
we address here is, what are the conditions that define this class of
algorithms? There is a standard answer to this question: Briefly, the
conditions are variation, heredity, and selection. We agree that these three
conditions are sufficient for a limited type of evolution, but they are not
sufficient for open-ended evolution. By open-ended evolution, we mean evolution
that generates a continuous stream of creative solutions, without stagnating.
We propose a set of conditions for open-ended evolution. The new conditions
build on the standard conditions by adding fission, fusion, and cooperation. We
test the proposed conditions by applying them to major transitions in the
evolution of life and culture. We find that the proposed conditions are able to
account for the major transitions.
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
Authors: Amy Zhang, Nicolas Ballas, Joelle Pineau
Date: 2018-06-20 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, Diagnostic, Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The risks and perils of overfitting in machine learning are well known.
However most of the treatment of this, including diagnostic tools and remedies,
was developed for the supervised learning case. In this work, we aim to offer
new perspectives on the characterization and prevention of overfitting in deep
Reinforcement Learning (RL) methods, with a particular focus on continuous
domains. We examine several aspects, such as how to define and diagnose
overfitting in MDPs, and how to reduce risks by injecting sufficient training
diversity. This work complements recent findings on the brittleness of deep RL
methods and offers practical observations for RL researchers and practitioners.
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Authors: Sean MacAvaney, Bart Desmet, Arman Cohan, Luca Soldaini, Andrew Yates, Ayah Zirikly, Nazli Goharian
Date: 2018-06-20 00:00:00
Proceeding: WS 2018 6
Tasks: General Classification

Self-reported diagnosis statements have been widely employed in studying
language related to mental health in social media. However, existing research
has largely ignored the temporality of mental health diagnoses. In this work,
we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported
depression diagnosis posts from Reddit that include temporal information about
the diagnosis. Annotations include whether a mental health condition is present
and how recently the diagnosis happened. Furthermore, we include exact temporal
spans that relate to the date of diagnosis. This information is valuable for
various computational methods to examine mental health through social media
because one's mental health state is not static. We also test several baseline
classification and extraction approaches, which suggest that extracting
temporal information from self-reported diagnosis statements is challenging.
Multi-Layer Ensembling Techniques for Multilingual Intent Classification
Authors: Charles Costello, Ruixi Lin, Vishwas Mruthyunjaya, Bettina Bolla, Charles Jankowski
Date: 2018-06-20 00:00:00
Tasks: Classification, General Classification, intent-classification, Intent Classification

In this paper we determine how multi-layer ensembling improves performance on
multilingual intent classification. We develop a novel multi-layer ensembling
approach that ensembles both different model initializations and different
model architectures. We also introduce a new banking domain dataset and compare
results against the standard ATIS dataset and the Chinese SMP2017 dataset to
determine ensembling performance in multilingual and multi-domain contexts. We
run ensemble experiments across all three datasets, and conclude that
ensembling provides significant performance increases, and that multi-layer
ensembling is a no-risk way to improve performance on intent classification. We
also find that a diverse ensemble of simple models can reach perform comparable
to much more sophisticated state-of-the-art models. Our best F 1 scores on
ATIS, Banking, and SMP are 97.54%, 91.79%, and 93.55% respectively, which
compare well with the state-of-the-art on ATIS and best submission to the
SMP2017 competition. The total ensembling performance increases we achieve are
0.23%, 1.96%, and 4.04% F 1 respectively.
Como funciona o Deep Learning
Authors: Moacir Antonelli Ponti, Gabriel B. Paranhos da Costa
Date: 2018-06-20 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, General Classification

Deep Learning methods are currently the state-of-the-art in many problems
which can be tackled via machine learning, in particular classification
problems. However there is still lack of understanding on how those methods
work, why they work and what are the limitations involved in using them. In
this chapter we will describe in detail the transition from shallow to deep
networks, include examples of code on how to implement them, as well as the
main issues one faces when training a deep network. Afterwards, we introduce
some theoretical background behind the use of deep models, and discuss their
limitations.
iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos
Authors: Aron Monszpart, Paul Guerrero, Duygu Ceylan, Ersin Yumer, Niloy J. Mitra
Date: 2018-06-20 00:00:00
Tasks: Human-Object Interaction Detection, Object

A long-standing challenge in scene analysis is the recovery of scene
arrangements under moderate to heavy occlusion, directly from monocular video.
While the problem remains a subject of active research, concurrent advances
have been made in the context of human pose reconstruction from monocular
video, including image-space feature point detection and 3D pose recovery.
These methods, however, start to fail under moderate to heavy occlusion as the
problem becomes severely under-constrained. We approach the problems
differently. We observe that people interact similarly in similar scenes.
Hence, we exploit the correlation between scene object arrangement and motions
performed in that scene in both directions: first, typical motions performed
when interacting with objects inform us about possible object arrangements; and
second, object arrangements, in turn, constrain the possible motions.
  We present iMapper, a data-driven method that focuses on identifying
human-object interactions, and jointly reasons about objects and human movement
over space-time to recover both a plausible scene arrangement and consistent
human interactions. We first introduce the notion of characteristic
interactions as regions in space-time when an informative human-object
interaction happens. This is followed by a novel occlusion-aware matching
procedure that searches and aligns such characteristic snapshots from an
interaction database to best explain the input monocular video. Through
extensive evaluations, both quantitative and qualitative, we demonstrate that
iMapper significantly improves performance over both dedicated state-of-the-art
scene analysis and 3D human pose recovery approaches, especially under medium
to heavy occlusion.
Combining Support Vector Machine and Elephant Herding Optimization for Cardiac Arrhythmias
Authors: Aboul Ella Hassanien, Moataz Kilany, Essam H. Houssein
Date: 2018-06-20 00:00:00
Tasks: Classification, General Classification, Specificity

Many people are currently suffering from heart diseases that can lead to
untimely death. The most common heart abnormality is arrhythmia, which is
simply irregular beating of the heart. A prediction system for the early
intervention and prevention of heart diseases, including cardiovascular
diseases (CDVs) and arrhythmia, is important. This paper introduces the
classification of electrocardiogram (ECG) heartbeats into normal or abnormal.
The approach is based on the combination of swarm optimization algorithms with
a modified PannTompkins algorithm (MPTA) and support vector machines (SVMs).
The MPTA was implemented to remove ECG noise, followed by the application of
the extended features extraction algorithm (EFEA) for ECG feature extraction.
Then, elephant herding optimization (EHO) was used to find a subset of ECG
features from a larger feature pool that provided better classification
performance than that achieved using the whole set. Finally, SVMs were used for
classification. The results show that the EHOSVM approach achieved good
classification results in terms of five statistical indices: accuracy, 93.31%;
sensitivity, 45.49%; precision, 46.45%; F-measure, 45.48%; and specificity,
45.48%. Furthermore, the results demonstrate a clear improvement in accuracy
compared to that of other methods when applied to the MITBIH arrhythmia
database.
InfoCatVAE: Representation Learning with Categorical Variational Autoencoders
Authors: Edouard Pineau, Marc Lelarge
Date: 2018-06-20 00:00:00
Tasks: Clustering, Representation Learning

This paper describes InfoCatVAE, an extension of the variational autoencoder
that enables unsupervised disentangled representation learning. InfoCatVAE uses
multimodal distributions for the prior and the inference network and then
maximizes the evidence lower bound objective (ELBO). We connect the new ELBO
derived for our model with a natural soft clustering objective which explains
the robustness of our approach. We then adapt the InfoGANs method to our
setting in order to maximize the mutual information between the categorical
code and the generated inputs and obtain an improved model.
A Review of Network Inference Techniques for Neural Activation Time Series
Authors: George Panagopoulos
Date: 2018-06-20 00:00:00
Tasks: Point Processes, Time Series, Time Series Analysis

Studying neural connectivity is considered one of the most promising and
challenging areas of modern neuroscience. The underpinnings of cognition are
hidden in the way neurons interact with each other. However, our experimental
methods of studying real neural connections at a microscopic level are still
arduous and costly. An efficient alternative is to infer connectivity based on
the neuronal activations using computational methods. A reliable method for
network inference, would not only facilitate research of neural circuits
without the need of laborious experiments but also reveal insights on the
underlying mechanisms of the brain. In this work, we perform a review of
methods for neural circuit inference given the activation time series of the
neural population. Approaching it from machine learning perspective, we divide
the methodologies into unsupervised and supervised learning. The methods are
based on correlation metrics, probabilistic point processes, and neural
networks. Furthermore, we add a data mining methodology inspired by influence
estimation in social networks as a new supervised learning approach. For
comparison, we use the small version of the Chalearn Connectomics competition,
that is accompanied with ground truth connections between neurons. The
experiments indicate that unsupervised learning methods perform better,
however, supervised methods could surpass them given enough data and resources.
Semi-supervised Seizure Prediction with Generative Adversarial Networks
Authors: Nhan Duy Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Omid Kavehei
Date: 2018-06-20 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Feature Engineering, Generative Adversarial Network, Prediction, Seizure prediction

In this article, we propose an approach that can make use of not only labeled
EEG signals but also the unlabeled ones which is more accessible. We also
suggest the use of data fusion to further improve the seizure prediction
accuracy. Data fusion in our vision includes EEG signals, cardiogram signals,
body temperature and time. We use the short-time Fourier transform on 28-s EEG
windows as a pre-processing step. A generative adversarial network (GAN) is
trained in an unsupervised manner where information of seizure onset is
disregarded. The trained Discriminator of the GAN is then used as feature
extractor. Features generated by the feature extractor are classified by two
fully-connected layers (can be replaced by any classifier) for the labeled EEG
signals. This semi-supervised seizure prediction method achieves area under the
operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp
EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively.
Unsupervised training without the need of labeling is important because not
only it can be performed in real-time during EEG signal recording, but also it
does not require feature engineering effort for each patient.
A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task
Authors: Josef Michalek, Jan Vanek
Date: 2018-06-19 00:00:00
Tasks: speech-recognition, Speech Recognition, Survey

In this survey paper, we have evaluated several recent deep neural network
(DNN) architectures on a TIMIT phone recognition task. We chose the TIMIT
corpus due to its popularity and broad availability in the community. It also
simulates a low-resource scenario that is helpful in minor languages. Also, we
prefer the phone recognition task because it is much more sensitive to an
acoustic model quality than a large vocabulary continuous speech recognition
(LVCSR) task. In recent years, many DNN published papers reported results on
TIMIT. However, the reported phone error rates (PERs) were often much higher
than a PER of a simple feed-forward (FF) DNN. That was the main motivation of
this paper: To provide a baseline DNNs with open-source scripts to easily
replicate the baseline results for future papers with lowest possible PERs.
According to our knowledge, the best-achieved PER of this survey is better than
the best-published PER to date.
Revisiting Singing Voice Detection: a Quantitative Review and the Future Outlook
Authors: Kyungyun Lee, Keunwoo Choi, Juhan Nam
Date: 2018-06-04 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Since the vocal component plays a crucial role in popular music, singing
voice detection has been an active research topic in music information
retrieval. Although several proposed algorithms have shown high performances,
we argue that there still is a room to improve to build a more robust singing
voice detection system. In order to identify the area of improvement, we first
perform an error analysis on three recent singing voice detection systems.
Based on the analysis, we design novel methods to test the systems on multiple
sets of internally curated and generated data to further examine the pitfalls,
which are not clearly revealed with the current datasets. From the experiment
results, we also propose several directions towards building a more robust
singing voice detector.
Finding Local Minima via Stochastic Nested Variance Reduction
Authors: Dongruo Zhou, Pan Xu, Quanquan Gu
Date: 2018-06-22 00:00:00
Tasks: Stochastic Optimization

We propose two algorithms that can find local minima faster than the
state-of-the-art algorithms in both finite-sum and general stochastic nonconvex
optimization. At the core of the proposed algorithms is
$\text{One-epoch-SNVRG}^+$ using stochastic nested variance reduction (Zhou et
al., 2018a), which outperforms the state-of-the-art variance reduction
algorithms such as SCSG (Lei et al., 2017). In particular, for finite-sum
optimization problems, the proposed
$\text{SNVRG}^{+}+\text{Neon2}^{\text{finite}}$ algorithm achieves
$\tilde{O}(n^{1/2}\epsilon^{-2}+n\epsilon_H^{-3}+n^{3/4}\epsilon_H^{-7/2})$
gradient complexity to converge to an $(\epsilon, \epsilon_H)$-second-order
stationary point, which outperforms $\text{SVRG}+\text{Neon2}^{\text{finite}}$
(Allen-Zhu and Li, 2017) , the best existing algorithm, in a wide regime. For
general stochastic optimization problems, the proposed
$\text{SNVRG}^{+}+\text{Neon2}^{\text{online}}$ achieves
$\tilde{O}(\epsilon^{-3}+\epsilon_H^{-5}+\epsilon^{-2}\epsilon_H^{-3})$
gradient complexity, which is better than both
$\text{SVRG}+\text{Neon2}^{\text{online}}$ (Allen-Zhu and Li, 2017) and
Natasha2 (Allen-Zhu, 2017) in certain regimes. Furthermore, we explore the
acceleration brought by third-order smoothness of the objective function.