pwc-archive/papers-with-abstracts huggingface dataset:
A Combined CNN and LSTM Model for Arabic Sentiment Analysis
Authors: Abdulaziz M. Alayba, Vasile Palade, Matthew England, Rahat Iqbal
Date: 2018-07-09 00:00:00
Tasks: Arabic Sentiment Analysis, Classification, Diversity, General Classification, Sentiment Analysis, Sentiment Classification, Translation

Deep neural networks have shown good data modelling capabilities when dealing
with challenging and large datasets from a wide range of application areas.
Convolutional Neural Networks (CNNs) offer advantages in selecting good
features and Long Short-Term Memory (LSTM) networks have proven good abilities
of learning sequential data. Both approaches have been reported to provide
improved results in areas such image processing, voice recognition, language
translation and other Natural Language Processing (NLP) tasks. Sentiment
classification for short text messages from Twitter is a challenging task, and
the complexity increases for Arabic language sentiment classification tasks
because Arabic is a rich language in morphology. In addition, the availability
of accurate pre-processing tools for Arabic is another current limitation,
along with limited research available in this area. In this paper, we
investigate the benefits of integrating CNNs and LSTMs and report obtained
improved accuracy for Arabic sentiment analysis on different datasets.
Additionally, we seek to consider the morphological diversity of particular
Arabic words by using different sentiment classification levels.
Model Agnostic Supervised Local Explanations
Authors: Gregory Plumb, Denali Molitor, Ameet Talwalkar
Date: 2018-07-09 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: feature selection, model

Model interpretability is an increasingly important component of practical
machine learning. Some of the most common forms of interpretability systems are
example-based, local, and global explanations. One of the main challenges in
interpretability is designing explanation systems that can capture aspects of
each of these explanation types, in order to develop a more thorough
understanding of the model. We address this challenge in a novel model called
MAPLE that uses local linear modeling techniques along with a dual
interpretation of random forests (both as a supervised neighborhood approach
and as a feature selection method). MAPLE has two fundamental advantages over
existing interpretability systems. First, while it is effective as a black-box
explanation system, MAPLE itself is a highly accurate predictive model that
provides faithful self explanations, and thus sidesteps the typical
accuracy-interpretability trade-off. Specifically, we demonstrate, on several
UCI datasets, that MAPLE is at least as accurate as random forests and that it
produces more faithful local explanations than LIME, a popular interpretability
system. Second, MAPLE provides both example-based and local explanations and
can detect global patterns, which allows it to diagnose limitations in its
local explanations.
Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images
Authors: Walid Abdullah Al, Il Dong Yun
Date: 2018-07-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deploying the idea of long-term cumulative return, reinforcement learning has
shown remarkable performance in various fields. We propose a formulation of the
landmark localization in 3D medical images as a reinforcement learning problem.
Whereas value-based methods have been widely used to solve similar problems, we
adopt an actor-critic based direct policy search method framed in a temporal
difference learning approach. Successful behavior learning is challenging in
large state and/or action spaces, requiring many trials. We introduce a partial
policy-based reinforcement learning to enable solving the large problem of
localization by learning the optimal policy on smaller partial domains.
Independent actors efficiently learn the corresponding partial policies, each
utilizing their own independent critic. The proposed policy reconstruction from
the partial policies ensures a robust and efficient localization utilizing the
sub-agents solving simple binary decision problems in their corresponding
partial action spaces. The proposed reinforcement learning requires a small
number of trials to learn the optimal behavior compared with the original
behavior learning scheme.
Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks
Authors: Saeid Asgari Taghanaki, Arkadeep Das, Ghassan Hamarneh
Date: 2018-07-09 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, X-ray Classification

Recently, there have been several successful deep learning approaches for
automatically classifying chest X-ray images into different disease categories.
However, there is not yet a comprehensive vulnerability analysis of these
models against the so-called adversarial perturbations/attacks, which makes
deep models more trustful in clinical practices. In this paper, we extensively
analyzed the performance of two state-of-the-art classification deep networks
on chest X-ray images. These two networks were attacked by three different
categories (ten methods in total) of adversarial methods (both white- and
black-box), namely gradient-based, score-based, and decision-based attacks.
Furthermore, we modified the pooling operations in the two classification
networks to measure their sensitivities against different attacks, on the
specific task of chest X-ray classification.
Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings
Authors: Nikola Ljubešić, Darja Fišer, Anita Peti-Stantić
Date: 2018-07-09 00:00:00
Tasks: Cross-Lingual Transfer, Word Embeddings

The notions of concreteness and imageability, traditionally important in
psycholinguistics, are gaining significance in semantic-oriented natural
language processing tasks. In this paper we investigate the predictability of
these two concepts via supervised learning, using word embeddings as
explanatory variables. We perform predictions both within and across languages
by exploiting collections of cross-lingual embeddings aligned to a single
vector space. We show that the notions of concreteness and imageability are
highly predictable both within and across languages, with a moderate loss of up
to 20% in correlation when predicting across languages. We further show that
the cross-lingual transfer via word embeddings is more efficient than the
simple transfer via bilingual dictionaries.
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
Authors: Nicholas Geneva, Nicholas Zabaras
Date: 2018-07-08 00:00:00
Tasks: Form, Uncertainty Quantification

Data-driven methods for improving turbulence modeling in Reynolds-Averaged
Navier-Stokes (RANS) simulations have gained significant interest in the
computational fluid dynamics community. Modern machine learning algorithms have
opened up a new area of black-box turbulence models allowing for the tuning of
RANS simulations to increase their predictive accuracy. While several
data-driven turbulence models have been reported, the quantification of the
uncertainties introduced has mostly been neglected. Uncertainty quantification
for such data-driven models is essential since their predictive capability
rapidly declines as they are tested for flow physics that deviate from that in
the training data. In this work, we propose a novel data-driven framework that
not only improves RANS predictions but also provides probabilistic bounds for
fluid quantities such as velocity and pressure. The uncertainties capture both
model form uncertainty as well as epistemic uncertainty induced by the limited
training data. An invariant Bayesian deep neural network is used to predict the
anisotropic tensor component of the Reynolds stress. This model is trained
using Stein variational gradient decent algorithm. The computed uncertainty on
the Reynolds stress is propagated to the quantities of interest by vanilla
Monte Carlo simulation. Results are presented for two test cases that differ
geometrically from the training flows at several different Reynolds numbers.
The prediction enhancement of the data-driven model is discussed as well as the
associated probabilistic bounds for flow properties of interest. Ultimately
this framework allows for a quantitative measurement of model confidence and
uncertainty quantification for flows in which no high-fidelity observations or
prior knowledge is available.
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images
Authors: Sergiu Deitsch, Vincent Christlein, Stephan Berger, Claudia Buerhop-Lutz, Andreas Maier, Florian Gallwitz, Christian Riess
Date: 2018-07-08 00:00:00
Tasks: Anomaly Classification, GPU

Electroluminescence (EL) imaging is a useful modality for the inspection of
photovoltaic (PV) modules. EL images provide high spatial resolution, which
makes it possible to detect even finest defects on the surface of PV modules.
However, the analysis of EL images is typically a manual process that is
expensive, time-consuming, and requires expert knowledge of many different
types of defects. In this work, we investigate two approaches for automatic
detection of such defects in a single image of a PV cell. The approaches differ
in their hardware requirements, which are dictated by their respective
application scenarios. The more hardware-efficient approach is based on
hand-crafted features that are classified in a Support Vector Machine (SVM). To
obtain a strong performance, we investigate and compare various processing
variants. The more hardware-demanding approach uses an end-to-end deep
Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit
(GPU). Both approaches are trained on 1,968 cells extracted from high
resolution EL intensity images of mono- and polycrystalline PV modules. The CNN
is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a
slightly lower average accuracy of 82.44%, but can run on arbitrary hardware.
Both automated approaches make continuous, highly accurate monitoring of PV
cells feasible.
Automated labeling of bugs and tickets using attention-based mechanisms in recurrent neural networks
Authors: Volodymyr Lyubinets, Taras Boiko, Deon Nicholas
Date: 2018-07-08 00:00:00
Tasks: text-classification, Text Classification

We explore solutions for automated labeling of content in bug trackers and
customer support systems. In order to do that, we classify content in terms of
several criteria, such as priority or product area. In the first part of the
paper, we provide an overview of existing methods used for text classification.
These methods fall into two categories - the ones that rely on neural networks
and the ones that don't. We evaluate results of several solutions of both
kinds. In the second part of the paper we present our own recurrent neural
network solution based on hierarchical attention paradigm. It consists of
several Hierarchical Attention network blocks with varying Gated Recurrent Unit
cell sizes and a complementary shallow network that goes alongside. Lastly, we
evaluate above-mentioned methods when predicting fields from two datasets -
Arch Linux bug tracker and Chromium bug tracker. Our contributions include a
comprehensive benchmark between a variety of methods on relevant datasets; a
novel solution that outperforms previous generation methods; and two new
datasets that are made public for further research.
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure
Authors: Hamed Hakkak
Date: 2018-07-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-based compression is an effective, facilitating, and expanded model of
neural network models with limited computing and low power. However,
conventional models of compression techniques utilize crafted features [2,3,12]
and explore specialized areas for exploration and design of large spaces in
terms of size, speed, and accuracy, which usually have returns Less and time is
up. This paper will effectively analyze deep auto compression (ADC) and
reinforcement learning strength in an effective sample and space design, and
improve the compression quality of the model. The results of compression of the
advanced model are obtained without any human effort and in a completely
automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher
than the manual compression model for VGG-16 in ImageNet.
Stochastic Block Model for Hypergraphs: Statistical limits and a semidefinite programming approach
Authors: Chiheon Kim, Afonso S. Bandeira, Michel X. Goemans
Date: 2018-07-08 00:00:00
Tasks: Community Detection, Stochastic Block Model

We study the problem of community detection in a random hypergraph model
which we call the stochastic block model for $k$-uniform hypergraphs ($k$-SBM).
We investigate the exact recovery problem in $k$-SBM and show that a sharp
phase transition occurs around a threshold: below the threshold it is
impossible to recover the communities with non-vanishing probability, yet above
the threshold there is an estimator which recovers the communities almost
asymptotically surely. We also consider a simple, efficient algorithm for the
exact recovery problem which is based on a semidefinite relaxation technique.
Reasoning about exceptions in ontologies: from the lexicographic closure to the skeptical closure
Authors: Laura Giordano, Valentina Gliozzi
Date: 2018-07-08 00:00:00

Reasoning about exceptions in ontologies is nowadays one of the challenges
the description logics community is facing. The paper describes a preferential
approach for dealing with exceptions in Description Logics, based on the
rational closure. The rational closure has the merit of providing a simple and
efficient approach for reasoning with exceptions, but it does not allow
independent handling of the inheritance of different defeasible properties of
concepts. In this work we outline a possible solution to this problem by
introducing a variant of the lexicographical closure, that we call skeptical
closure, which requires to construct a single base. We develop a bi-preference
semantics semantics for defining a characterization of the skeptical closure.
Machine Learning in High Energy Physics Community White Paper
Authors: Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata
Date: 2018-07-08 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.
Separability is not the best goal for machine learning
Authors: Wlodzislaw Duch
Date: 2018-07-08 00:00:00
Tasks: BIG-bench Machine Learning

Neural networks use their hidden layers to transform input data into linearly
separable data clusters, with a linear or a perceptron type output layer making
the final projection on the line perpendicular to the discriminating
hyperplane. For complex data with multimodal distributions this transformation
is difficult to learn. Projection on $k\geq 2$ line segments is the simplest
extension of linear separability, defining much easier goal for the learning
process. Simple problems are 2-separable, but problems with inherent complex
logic may be solved in a simple way by $k$-separable projections. The
difficulty of learning non-linear data distributions is shifted to separation
of line intervals, simplifying the transformation of data by hidden network
layers. For classification of difficult Boolean problems, such as the parity
problem, linear projection combined with \ksep is sufficient and provides a
powerful new target for learning. More complex targets may also be defined,
changing the goal of learning from linear discrimination to creation of data
distributions that can easily be handled by specialized models selected to
analyze output distributions. This approach can replace many layers of
transformation required by deep learning models.
Large Margin Few-Shot Learning
Authors: Yong Wang, Xiao-Ming Wu, Qimai Li, Jiatao Gu, Wangmeng Xiang, Lei Zhang, Victor O. K. Li
Date: 2018-07-08 00:00:00
Tasks: Few-Shot Learning, General Classification

The key issue of few-shot learning is learning to generalize. This paper
proposes a large margin principle to improve the generalization capacity of
metric based methods for few-shot learning. To realize it, we develop a unified
framework to learn a more discriminative metric space by augmenting the
classification loss function with a large margin distance loss function for
training. Extensive experiments on two state-of-the-art few-shot learning
methods, graph neural networks and prototypical networks, show that our method
can improve the performance of existing models substantially with very little
computational overhead, demonstrating the effectiveness of the large margin
principle and the potential of our method.
Learning The Sequential Temporal Information with Recurrent Neural Networks
Authors: Pushparaja Murugan
Date: 2018-07-08 00:00:00
Tasks: Image Captioning, Language Modeling, Language Modelling, Machine Translation, Multiple Object Tracking, Object Tracking, speech-recognition, Speech Recognition, Stock Market Prediction, Time Series, Time Series Analysis, Translation

Recurrent Networks are one of the most powerful and promising artificial
neural network algorithms to processing the sequential data such as natural
languages, sound, time series data. Unlike traditional feed-forward network,
Recurrent Network has a inherent feed back loop that allows to store the
temporal context information and pass the state of information to the entire
sequences of the events. This helps to achieve the state of art performance in
many important tasks such as language modeling, stock market prediction, image
captioning, speech recognition, machine translation and object tracking etc.,
However, training the fully connected RNN and managing the gradient flow are
the complicated process. Many studies are carried out to address the mentioned
limitation. This article is intent to provide the brief details about recurrent
neurons, its variances and trips & tricks to train the fully recurrent neural
network. This review work is carried out as a part of our IPO studio software
module 'Multiple Object Tracking'.
Semi-parametric Image Inpainting
Authors: Karim Iskakov
Date: 2018-07-08 00:00:00
Tasks: Image Inpainting

This paper introduces a semi-parametric approach to image inpainting for
irregular holes. The nonparametric part consists of an external image database.
During test time database is used to retrieve a supplementary image, similar to
the input masked picture, and utilize it as auxiliary information for the deep
neural network. Further, we propose a novel method of generating masks with
irregular holes and present public dataset with such masks. Experiments on
CelebA-HQ dataset show that our semi-parametric method yields more realistic
results than previous approaches, which is confirmed by the user study.
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts
Authors: Pankaj Gupta, Bernt Andrassy, Hinrich Schütze
Date: 2018-07-08 00:00:00
Proceeding: COLING 2018 8
Tasks: Retrieval, Sentence

The goal of our industrial ticketing system is to retrieve a relevant
solution for an input query, by matching with historical tickets stored in
knowledge base. A query is comprised of subject and description, while a
historical ticket consists of subject, description and solution. To retrieve a
relevant solution, we use textual similarity paradigm to learn similarity in
the query and historical tickets. The task is challenging due to significant
term mismatch in the query and ticket pairs of asymmetric lengths, where
subject is a short text but description and solution are multi-sentence texts.
We present a novel Replicated Siamese LSTM model to learn similarity in
asymmetric text pairs, that gives 22% and 7% gain (Accuracy@10) for retrieval
task, respectively over unsupervised and supervised baselines. We also show
that the topic and distributed semantic features for short and long texts
improved both similarity learning and retrieval.