pwc-archive/papers-with-abstracts huggingface dataset:
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses
Authors: Dirk Sudholt
Date: 2018-01-30 00:00:00
Tasks: Diversity, Evolutionary Algorithms, Multiobjective Optimization

Population diversity is crucial in evolutionary algorithms to enable global
exploration and to avoid poor performance due to premature convergence. This
book chapter reviews runtime analyses that have shown benefits of population
diversity, either through explicit diversity mechanisms or through naturally
emerging diversity. These works show that the benefits of diversity are
manifold: diversity is important for global exploration and the ability to find
several global optima. Diversity enhances crossover and enables crossover to be
more effective than mutation. Diversity can be crucial in dynamic optimization,
when the problem landscape changes over time. And, finally, it facilitates
search for the whole Pareto front in evolutionary multiobjective optimization.
The presented analyses rigorously quantify the performance of evolutionary
algorithms in the light of population diversity, laying the foundation for a
rigorous understanding of how search dynamics are affected by the presence or
absence of population diversity and the introduction of diversity mechanisms.
Leveraging Adiabatic Quantum Computation for Election Forecasting
Authors: Maxwell Henderson, John Novak, Tristan Cook
Date: 2018-01-30 00:00:00

Accurate, reliable sampling from fully-connected graphs with arbitrary
correlations is a difficult problem. Such sampling requires knowledge of the
probabilities of observing every possible state of a graph. As graph size
grows, the number of model states becomes intractably large and efficient
computation requires full sampling be replaced with heuristics and algorithms
that are only approximations of full sampling. This work investigates the
potential impact of adiabatic quantum computation for sampling purposes,
building on recent successes training Boltzmann machines using a quantum
device. We investigate the use case of quantum computation to train Boltzmann
machines for predicting the 2016 Presidential election.
A Machine Learning Approach to Quantitative Prosopography
Authors: Aayushee Gupta, Haimonti Dutta, Srikanta Bedathur, Lipika Dey
Date: 2018-01-30 00:00:00
Tasks: Articles, BIG-bench Machine Learning, NER

Prosopography is an investigation of the common characteristics of a group of
people in history, by a collective study of their lives. It involves a study of
biographies to solve historical problems. If such biographies are unavailable,
surviving documents and secondary biographical data are used. Quantitative
prosopography involves analysis of information from a wide variety of sources
about "ordinary people". In this paper, we present a machine learning framework
for automatically designing a people gazetteer which forms the basis of
quantitative prosopographical research. The gazetteer is learnt from the noisy
text of newspapers using a Named Entity Recognizer (NER). It is capable of
identifying influential people from it by making use of a custom designed
Influential Person Index (IPI). Our corpus comprises of 14020 articles from a
local newspaper, "The Sun", published from New York in 1896. Some influential
people identified by our algorithm include Captain Donald Hankey (an English
soldier), Dame Nellie Melba (an Australian operatic soprano), Hugh Allan (a
Canadian shipping magnate) and Sir Hugh John McDonald (the first Prime Minister
of Canada).
Deep Quaternion Networks
Authors: Chase Gaudet, Anthony Maida
Date: 2017-12-13 00:00:00
Tasks: Road Segmentation, Segmentation

The field of deep learning has seen significant advancement in recent years.
However, much of the existing work has been focused on real-valued numbers.
Recent work has shown that a deep learning system using the complex numbers can
be deeper for a fixed parameter budget compared to its real-valued counterpart.
In this work, we explore the benefits of generalizing one step further into the
hyper-complex numbers, quaternions specifically, and provide the architecture
components needed to build deep quaternion networks. We develop the theoretical
basis by reviewing quaternion convolutions, developing a novel quaternion
weight initialization scheme, and developing novel algorithms for quaternion
batch-normalization. These pieces are tested in a classification model by
end-to-end training on the CIFAR-10 and CIFAR-100 data sets and a segmentation
model by end-to-end training on the KITTI Road Segmentation data set. These
quaternion networks show improved convergence compared to real-valued and
complex-valued networks, especially on the segmentation task, while having
fewer parameters
Deep Hyperspherical Learning
Authors: Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song
Date: 2017-11-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Representation Learning

Convolution as inner product has been the founding basis of convolutional
neural networks (CNNs) and the key to end-to-end visual representation
learning. Benefiting from deeper architectures, recent CNNs have demonstrated
increasingly strong representation abilities. Despite such improvement, the
increased depth and larger parameter space have also led to challenges in
properly training a network. In light of such challenges, we propose
hyperspherical convolution (SphereConv), a novel learning framework that gives
angular representations on hyperspheres. We introduce SphereNet, deep
hyperspherical convolution networks that are distinct from conventional inner
product based convolutional networks. In particular, SphereNet adopts
SphereConv as its basic convolution operator and is supervised by generalized
angular softmax loss - a natural loss formulation under SphereConv. We show
that SphereNet can effectively encode discriminative representation and
alleviate training difficulty, leading to easier optimization, faster
convergence and comparable (even better) classification accuracy over
convolutional counterparts. We also provide some theoretical insights for the
advantages of learning on hyperspheres. In addition, we introduce the learnable
SphereConv, i.e., a natural improvement over prefixed SphereConv, and
SphereNorm, i.e., hyperspherical learning as a normalization method.
Experiments have verified our conclusions.
Rigorous Restricted Isometry Property of Low-Dimensional Subspaces
Authors: Gen Li, Qinghua Liu, Yuantao Gu
Date: 2018-01-30 00:00:00
Tasks: compressed sensing, Dimensionality Reduction, LEMMA

Dimensionality reduction is in demand to reduce the complexity of solving
large-scale problems with data lying in latent low-dimensional structures in
machine learning and computer version. Motivated by such need, in this work we
study the Restricted Isometry Property (RIP) of Gaussian random projections for
low-dimensional subspaces in $\mathbb{R}^N$, and rigorously prove that the
projection Frobenius norm distance between any two subspaces spanned by the
projected data in $\mathbb{R}^n$ ($n<N$) remain almost the same as the distance
between the original subspaces with probability no less than $1 - {\rm
e}^{-\mathcal{O}(n)}$. Previously the well-known Johnson-Lindenstrauss (JL)
Lemma and RIP for sparse vectors have been the foundation of sparse signal
processing including Compressed Sensing. As an analogy to JL Lemma and RIP for
sparse vectors, this work allows the use of random projections to reduce the
ambient dimension with the theoretical guarantee that the distance between
subspaces after compression is well preserved.
Bayesian Neural Networks
Authors: Vikram Mullachery, Aniruddh Khera, Amir Husain
Date: 2018-01-23 00:00:00
Tasks: Probabilistic Programming

This paper describes and discusses Bayesian Neural Network (BNN). The paper
showcases a few different applications of them for classification and
regression problems. BNNs are comprised of a Probabilistic Model and a Neural
Network. The intent of such a design is to combine the strengths of Neural
Networks and Stochastic modeling. Neural Networks exhibit continuous function
approximator capabilities. Stochastic models allow direct specification of a
model with known interaction between parameters to generate data. During the
prediction phase, stochastic models generate a complete posterior distribution
and produce probabilistic guarantees on the predictions. Thus BNNs are a unique
combination of neural network and stochastic models with the stochastic model
forming the core of this integration. BNNs can then produce probabilistic
guarantees on it's predictions and also generate the distribution of parameters
that it has learnt from the observations. That means, in the parameter space,
one can deduce the nature and shape of the neural network's learnt parameters.
These two characteristics makes them highly attractive to theoreticians as well
as practitioners. Recently there has been a lot of activity in this area, with
the advent of numerous probabilistic programming libraries such as: PyMC3,
Edward, Stan etc. Further this area is rapidly gaining ground as a standard
machine learning approach for numerous problems
An Iterative Spanning Forest Framework for Superpixel Segmentation
Authors: John E. Vargas-Muñoz, Ananda S. Chowdhury, Eduardo B. Alexandre, Felipe L. Galvão, Paulo A. Vechiatto Miranda, Alexandre X. Falcão
Date: 2018-01-30 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Superpixel segmentation has become an important research problem in image
processing. In this paper, we propose an Iterative Spanning Forest (ISF)
framework, based on sequences of Image Foresting Transforms, where one can
choose i) a seed sampling strategy, ii) a connectivity function, iii) an
adjacency relation, and iv) a seed pixel recomputation procedure to generate
improved sets of connected superpixels (supervoxels in 3D) per iteration. The
superpixels in ISF structurally correspond to spanning trees rooted at those
seeds. We present five ISF methods to illustrate different choices of its
components. These methods are compared with approaches from the
state-of-the-art in effectiveness and efficiency. The experiments involve 2D
and 3D datasets with distinct characteristics, and a high level application,
named sky image segmentation. The theoretical properties of ISF are
demonstrated in the supplementary material and the results show that some of
its methods are competitive with or superior to the best baselines in
effectiveness and efficiency.
Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks
Authors: Philip Warrick, Masun Nabhan Homsi
Date: 2018-01-30 00:00:00
Tasks: Arrhythmia Detection, feature selection, General Classification, Rhythm

Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder
associated with deadly and debilitating consequences including heart failure,
stroke, poor mental health, reduced quality of life and death. Having an
automatic system that diagnoses various types of cardiac arrhythmias would
assist cardiologists to initiate appropriate preventive measures and to improve
the analysis of cardiac disease. To this end, this paper introduces a new
approach to detect and classify automatically cardiac arrhythmias in
electrocardiograms (ECG) recordings.
  Methods: The proposed approach used a combination of Convolution Neural
Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with
pooling, dropout and normalization techniques to improve their accuracy. The
network predicted a classification at every 18th input sample and we selected
the final prediction for classification. Results were cross-validated on the
Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG
recordings lasting from 9s to just over 60s.
  Results: Using the proposed structure and no explicit feature selection,
10-fold stratified cross-validation gave an overall F-measure of 0.83.10-0.015
on the held-out test data (mean-standard deviation over all folds) and 0.80 on
the hidden dataset of the Challenge entry server.
Word Translation Without Parallel Data
Authors: Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou
Date: 2017-10-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: Cross-Lingual Word Embeddings, Machine Translation, Translation, Unsupervised Machine Translation, Word Alignment, Word Embeddings, Word Translation

State-of-the-art methods for learning cross-lingual word embeddings have
relied on bilingual dictionaries or parallel corpora. Recent studies showed
that the need for parallel data supervision can be alleviated with
character-level information. While these methods showed encouraging results,
they are not on par with their supervised counterparts and are limited to pairs
of languages sharing a common alphabet. In this work, we show that we can build
a bilingual dictionary between two languages without using any parallel
corpora, by aligning monolingual word embedding spaces in an unsupervised way.
Without using any character information, our model even outperforms existing
supervised methods on cross-lingual tasks for some language pairs. Our
experiments demonstrate that our method works very well also for distant
language pairs, like English-Russian or English-Chinese. We finally describe
experiments on the English-Esperanto low-resource language pair, on which there
only exists a limited amount of parallel data, to show the potential impact of
our method in fully unsupervised machine translation. Our code, embeddings and
dictionaries are publicly available.
Sliding Line Point Regression for Shape Robust Scene Text Detection
Authors: Yixing Zhu, Jun Du
Date: 2018-01-30 00:00:00
Tasks: Curved Text Detection, object-detection, Object Detection, Region Proposal, regression, Scene Text Detection, Text Detection

Traditional text detection methods mostly focus on quadrangle text. In this
study we propose a novel method named sliding line point regression (SLPR) in
order to detect arbitrary-shape text in natural scene. SLPR regresses multiple
points on the edge of text line and then utilizes these points to sketch the
outlines of the text. The proposed SLPR can be adapted to many object detection
architectures such as Faster R-CNN and R-FCN. Specifically, we first generate
the smallest rectangular box including the text with region proposal network
(RPN), then isometrically regress the points on the edge of text by using the
vertically and horizontally sliding lines. To make full use of information and
reduce redundancy, we calculate x-coordinate or y-coordinate of target point by
the rectangular box position, and just regress the remaining y-coordinate or
x-coordinate. Accordingly we can not only reduce the parameters of system, but
also restrain the points which will generate more regular polygon. Our approach
achieved competitive results on traditional ICDAR2015 Incidental Scene Text
benchmark and curve text detection dataset CTW1500.
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Authors: Toon Van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel
Date: 2018-01-30 00:00:00
Tasks: Clustering, valid

Clustering is inherently ill-posed: there often exist multiple valid
clusterings of a single dataset, and without any additional information a
clustering system has no way of knowing which clustering it should produce.
This motivates the use of constraints in clustering, as they allow users to
communicate their interests to the clustering system. Active constraint-based
clustering algorithms select the most useful constraints to query, aiming to
produce a good clustering using as few constraints as possible. We propose
COBRA, an active method that first over-clusters the data by running K-means
with a $K$ that is intended to be too large, and subsequently merges the
resulting small clusters into larger ones based on pairwise constraints. In its
merging step, COBRA is able to keep the number of pairwise queries low by
maximally exploiting constraint transitivity and entailment. We experimentally
show that COBRA outperforms the state of the art in terms of clustering quality
and runtime, without requiring the number of clusters in advance.
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Authors: Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter
Date: 2017-08-29 00:00:00
Proceeding: ICLR 2018 1

Generative adversarial networks (GANs) evolved into one of the most
successful unsupervised techniques for generating realistic images. Even though
it has recently been shown that GAN training converges, GAN models often end up
in local Nash equilibria that are associated with mode collapse or otherwise
fail to model the target distribution. We introduce Coulomb GANs, which pose
the GAN learning problem as a potential field of charged particles, where
generated samples are attracted to training set samples but repel each other.
The discriminator learns a potential field while the generator decreases the
energy by moving its samples along the vector (force) field determined by the
gradient of the potential field. Through decreasing the energy, the GAN model
learns to generate samples according to the whole target distribution and does
not only cover some of its modes. We prove that Coulomb GANs possess only one
Nash equilibrium which is optimal in the sense that the model distribution
equals the target distribution. We show the efficacy of Coulomb GANs on a
variety of image datasets. On LSUN and celebA, Coulomb GANs set a new state of
the art and produce a previously unseen variety of different samples.
Binary Compressive Sensing via Smoothed $\ell_0$ Gradient Descent
Authors: Tianlin Liu, Dae Gwan Lee
Date: 2018-01-30 00:00:00
Tasks: Compressive Sensing

We present a Compressive Sensing algorithm for reconstructing binary signals
from its linear measurements. The proposed algorithm minimizes a non-convex
cost function expressed as a weighted sum of smoothed $\ell_0$ norms which
takes into account the binariness of signals. We show that for binary signals
the proposed algorithm outperforms other existing algorithms in recovery rate
while requiring a short run time.
PEYMA: A Tagged Corpus for Persian Named Entities
Authors: Mahsa Sadat Shahshahani, Mahdi Mohseni, Azadeh Shakery, Heshaam Faili
Date: 2018-01-30 00:00:00
Tasks: NER, Question Answering, TAG

The goal in the NER task is to classify proper nouns of a text into classes
such as person, location, and organization. This is an important preprocessing
step in many NLP tasks such as question-answering and summarization. Although
many research studies have been conducted in this area in English and the
state-of-the-art NER systems have reached performances of higher than 90
percent in terms of F1 measure, there are very few research studies for this
task in Persian. One of the main important causes of this may be the lack of a
standard Persian NER dataset to train and test NER systems. In this research we
create a standard, big-enough tagged Persian NER dataset which will be
distributed for free for research purposes. In order to construct such a
standard dataset, we studied standard NER datasets which are constructed for
English researches and found out that almost all of these datasets are
constructed using news texts. So we collected documents from ten news websites.
Later, in order to provide annotators with some guidelines to tag these
documents, after studying guidelines used for constructing CoNLL and MUC
standard English datasets, we set our own guidelines considering the Persian
linguistic rules.
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
Authors: Qingji Guan, Yaping Huang, Zhun Zhong, Zhedong Zheng, Liang Zheng, Yi Yang
Date: 2018-01-30 00:00:00
Tasks: General Classification

This paper considers the task of thorax disease classification on chest X-ray
images. Existing methods generally use the global image as input for network
learning. Such a strategy is limited in two aspects. 1) A thorax disease
usually happens in (small) localized areas which are disease specific. Training
CNNs using global image may be affected by the (excessive) irrelevant noisy
areas. 2) Due to the poor alignment of some CXR images, the existence of
irregular borders hinders the network performance. In this paper, we address
the above problems by proposing a three-branch attention guided convolution
neural network (AG-CNN). AG-CNN 1) learns from disease-specific regions to
avoid noise and improve alignment, 2) also integrates a global branch to
compensate the lost discriminative cues by local branch. Specifically, we first
learn a global CNN branch using global images. Then, guided by the attention
heat map generated from the global branch, we inference a mask to crop a
discriminative region from the global image. The local region is used for
training a local CNN branch. Lastly, we concatenate the last pooling layers of
both the global and local branches for fine-tuning the fusion branch. The
Comprehensive experiment is conducted on the ChestX-ray14 dataset. We first
report a strong global baseline producing an average AUC of 0.841 with
ResNet-50 as backbone. After combining the local cues with the global
information, AG-CNN improves the average AUC to 0.868. While DenseNet-121 is
used, the average AUC achieves 0.871, which is a new state of the art in the
community.
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Authors: B Ravi Kiran, Dilip Mathew Thomas, Ranjith Parakkal
Date: 2018-01-09 00:00:00
Tasks: Anomaly Detection, Semi-supervised Anomaly Detection, Supervised Anomaly Detection, Video Anomaly Detection

Videos represent the primary source of information for surveillance
applications and are available in large amounts but in most cases contain
little or no annotation for supervised learning. This article reviews the
state-of-the-art deep learning based methods for video anomaly detection and
categorizes them based on the type of model and criteria of detection. We also
perform simple studies to understand the different approaches and provide the
criteria of evaluation for spatio-temporal anomaly detection.
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
Authors: Michal Bušta, Yash Patel, Jiri Matas
Date: 2018-01-30 00:00:00
Tasks: Optical Character Recognition (OCR)

An end-to-end trainable (fully differentiable) method for multi-language
scene text localization and recognition is proposed. The approach is based on a
single fully convolutional network (FCN) with shared layers for both tasks.
  E2E-MLT is the first published multi-language OCR for scene text. While
trained in multi-language setup, E2E-MLT demonstrates competitive performance
when compared to other methods trained for English scene text alone. The
experiments show that obtaining accurate multi-language multi-script
annotations is a challenging problem.