pwc-archive/papers-with-abstracts huggingface dataset:
QuasarNET: Human-level spectral classification and redshifting with Deep Neural Networks
Authors: Nicolas Busca, Christophe Balland
Date: 2018-08-29 00:00:00
Tasks: General Classification

We introduce QuasarNET, a deep convolutional neural network that performs
classification and redshift estimation of astrophysical spectra with
human-expert accuracy. We pose these two tasks as a \emph{feature detection}
problem: presence or absence of spectral features determines the class, and
their wavelength determines the redshift, very much like human-experts proceed.
When ran on BOSS data to identify quasars through their emission lines,
QuasarNET defines a sample $99.51\pm0.03$\% pure and $99.52\pm0.03$\% complete,
well above the requirements of many analyses using these data. QuasarNET
significantly reduces the problem of line-confusion that induces catastrophic
redshift failures to below 0.2\%. We also extend QuasarNET to classify spectra
with broad absorption line (BAL) features, achieving an accuracy of
$98.0\pm0.4$\% for recognizing BAL and $97.0\pm0.2$\% for rejecting non-BAL
quasars. QuasarNET is trained on data of low signal-to-noise and medium
resolution, typical of current and future astrophysical surveys, and could be
easily applied to classify spectra from current and upcoming surveys such as
eBOSS, DESI and 4MOST.
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
Authors: Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh, Lazaros Polymenakos
Date: 2018-08-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Goal-Oriented Dialog, Reinforcement Learning, valid

In a dialog, there can be multiple valid next utterances at any point. The
present end-to-end neural methods for dialog do not take this into account.
They learn with the assumption that at any time there is only one correct next
utterance. In this work, we focus on this problem in the goal-oriented dialog
setting where there are different paths to reach a goal. We propose a new
method, that uses a combination of supervised learning and reinforcement
learning approaches to address this issue. We also propose a new and more
effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid
next utterances to the original-bAbI dialog tasks, which allows evaluation of
goal-oriented dialog systems in a more realistic setting. We show that there is
a significant drop in performance of existing end-to-end neural methods from
81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on
permuted-bAbI dialog tasks. We also show that our proposed method improves the
performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog
tasks.
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
Authors: Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
Date: 2012-08-18 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, feature selection, General Classification, Hyperparameter Optimization

Many different machine learning algorithms exist; taking into account each
algorithm's hyperparameters, there is a staggeringly large number of possible
alternatives overall. We consider the problem of simultaneously selecting a
learning algorithm and setting its hyperparameters, going beyond previous work
that addresses these issues in isolation. We show that this problem can be
addressed by a fully automated approach, leveraging recent innovations in
Bayesian optimization. Specifically, we consider a wide range of feature
selection techniques (combining 3 search and 8 evaluator methods) and all
classification approaches implemented in WEKA, spanning 2 ensemble methods, 10
meta-methods, 27 base classifiers, and hyperparameter settings for each
classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup
09, variants of the MNIST dataset and CIFAR-10, we show classification
performance often much better than using standard selection/hyperparameter
optimization methods. We hope that our approach will help non-expert users to
more effectively identify machine learning algorithms and hyperparameter
settings appropriate to their applications, and hence to achieve improved
performance.
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Authors: Michael Hersche, Tino Rellstab, Pasquale Davide Schiavone, Lukas Cavigelli, Luca Benini, Abbas Rahimi
Date: 2018-06-18 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), Motor Imagery

Accurate, fast, and reliable multiclass classification of
electroencephalography (EEG) signals is a challenging task towards the
development of motor imagery brain-computer interface (MI-BCI) systems. We
propose enhancements to different feature extractors, along with a support
vector machine (SVM) classifier, to simultaneously improve classification
accuracy and execution time during training and testing. We focus on the
well-known common spatial pattern (CSP) and Riemannian covariance methods, and
significantly extend these two feature extractors to multiscale temporal and
spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$
standard deviation across 9 subjects) classification accuracy that surpasses
the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI
competition IV-2a dataset. The Riemannian covariance features outperform the
CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training
and 4x faster in testing. Using more temporal windows for Riemannian features
results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.
Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
Authors: Homa Hosseinmardi, Amir Ghasemian, Shrikanth Narayanan, Kristina Lerman, Emilio Ferrara
Date: 2018-08-31 00:00:00

Today's densely instrumented world offers tremendous opportunities for
continuous acquisition and analysis of multimodal sensor data providing
temporal characterization of an individual's behaviors. Is it possible to
efficiently couple such rich sensor data with predictive modeling techniques to
provide contextual, and insightful assessments of individual performance and
wellbeing? Prediction of different aspects of human behavior from these noisy,
incomplete, and heterogeneous bio-behavioral temporal data is a challenging
problem, beyond unsupervised discovery of latent structures. We propose a
Supervised Tensor Embedding (STE) algorithm for high dimension multimodal data
with join decomposition of input and target variable. Furthermore, we show that
features selection will help to reduce the contamination in the prediction and
increase the performance. The efficiently of the methods was tested via two
different real world datasets.
Algoritmos Genéticos Aplicado ao Problema de Roteamento de Veículos
Authors: Felipe F. Müller, Luis A. A. Meira
Date: 2018-08-31 00:00:00
Tasks: Combinatorial Optimization

Routing problems are often faced by companies who serve costumers through
vehicles. Such problems have a challenging structure to optimize, despite the
recent advances in combinatorial optimization. The goal of this project is to
study and propose optimization algorithms to the vehicle routing problems
(VRP). Focus will be on the problem variant in which the length of the route is
restricted by a constant. A real problem will be tackled: optimization of
postmen routes. Such problem was modeled as {multi-objective} in a roadmap with
25 vehicles and {30,000 deliveries} per day.
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti
Authors: Nikita Gordienko, Peng Gang, Yuri Gordienko, Wei Zeng, Oleg Alienin, Oleksandr Rokovyi, Sergii Stirenko
Date: 2018-08-31 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Dimensionality Reduction

Machine learning techniques are presented for automatic recognition of the
historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia
cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and
Cyrillic letters (CGCL) was assembled and pre-processed for recognition and
prediction by machine learning methods. The dataset consists of more than 4000
images for 34 types of letters. The explanatory data analysis of CGCL and
notMNIST datasets shown that the carved letters can hardly be differentiated by
dimensionality reduction methods, for example, by t-distributed stochastic
neighbor embedding (tSNE) due to the worse letter representation by stone
carving in comparison to hand writing. The multinomial logistic regression
(MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR
model demonstrated the area under curve (AUC) values for receiver operating
characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL,
respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and
CGCL (despite the much smaller size and quality of CGCL in comparison to
notMNIST) under condition of the high lossy data augmentation. CGCL dataset was
published to be available for the data science community as an open source
resource.
Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach
Authors: Worawate Ausawalaithong, Sanparith Marukatat, Arjaree Thirach, Theerawit Wilaiprasitporn
Date: 2018-08-31 00:00:00
Tasks: Computed Tomography (CT), Lung Cancer Diagnosis, Specificity, Transfer Learning

Since, cancer is curable when diagnosed at an early stage, lung cancer
screening plays an important role in preventive care. Although both low dose
computed tomography (LDCT) and computed tomography (CT) scans provide more
medical information than normal chest x-rays, there is very limited access to
these technologies in rural areas. Recently, there is a trend in using
computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer
from biomedical images. In this study, the 121-layer convolutional neural
network also known as DenseNet-121 by G. Huang et. al., along with the transfer
learning scheme was explored as a means to classify lung cancer using chest
X-ray images. The model was trained on a lung nodules dataset before training
on the lung cancer dataset to alleviate the problem of a small dataset. The
proposed model yields 74.43$\pm$6.01\% of mean accuracy, 74.96$\pm$9.85\% of
mean specificity, and 74.68$\pm$15.33\% of mean sensitivity. The proposed model
also provides a heatmap for identifying the location of the lung nodule. These
findings are promising for further development of chest x-ray-based lung cancer
diagnosis using the deep learning approach. Moreover, these findings solve the
problem of small dataset.
Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
Authors: Steven Guan, Amir Khan, Siddhartha Sikdar, Parag V. Chitnis
Date: 2018-08-31 00:00:00

Photoacoustic imaging is an emerging imaging modality that is based upon the
photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic
pressure waves are measured by an array of detectors and used to reconstruct an
image of the initial pressure distribution. A common challenge faced in PAT is
that the measured acoustic waves can only be sparsely sampled. Reconstructing
sparsely sampled data using standard methods results in severe artifacts that
obscure information within the image. We propose a modified convolutional
neural network (CNN) architecture termed Fully Dense UNet (FD-UNet) for
removing artifacts from 2D PAT images reconstructed from sparse data and
compare the proposed CNN with the standard UNet in terms of reconstructed image
quality.
Finite LTL Synthesis with Environment Assumptions and Quality Measures
Authors: Alberto Camacho, Meghyn Bienvenu, Sheila A. McIlraith
Date: 2018-08-31 00:00:00

In this paper, we investigate the problem of synthesizing strategies for
linear temporal logic (LTL) specifications that are interpreted over finite
traces -- a problem that is central to the automated construction of
controllers, robot programs, and business processes. We study a natural variant
of the finite LTL synthesis problem in which strategy guarantees are predicated
on specified environment behavior. We further explore a quantitative extension
of LTL that supports specification of quality measures, utilizing it to
synthesize high-quality strategies. We propose new notions of optimality and
associated algorithms that yield strategies that best satisfy specified quality
measures. Our algorithms utilize an automata-game approach, positioning them
well for future implementation via existing state-of-the-art techniques.
Seeing Colors: Learning Semantic Text Encoding for Classification
Authors: Shah Nawaz, Alessandro Calefati, Muhammad Kamran Janjua, Ignazio Gallo
Date: 2018-08-31 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, text-classification, Text Classification

The question we answer with this work is: can we convert a text document into
an image to exploit best image classification models to classify documents? To
answer this question we present a novel text classification method which
converts a text document into an encoded image, using word embedding and
capabilities of Convolutional Neural Networks (CNNs), successfully employed in
image classification. We evaluate our approach by obtaining promising results
on some well-known benchmark datasets for text classification. This work allows
the application of many of the advanced CNN architectures developed for
Computer Vision to Natural Language Processing. We test the proposed approach
on a multi-modal dataset, proving that it is possible to use a single deep
model to represent text and image in the same feature space.
Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling
Authors: Oleg V. Shylo, Hesam Shams
Date: 2018-08-31 00:00:00
Tasks: Binary Classification, Evolutionary Algorithms, General Classification, Job Shop Scheduling, regression, Scheduling

Many optimization techniques evaluate solutions consecutively, where the next
candidate for evaluation is determined by the results of previous evaluations.
For example, these include iterative methods, "black box" optimization
algorithms, simulated annealing, evolutionary algorithms and tabu search, to
name a few. When solving an optimization problem, these algorithms evaluate a
large number of solutions, which raises the following question: Is it possible
to learn something about the optimum using these solutions? In this paper, we
define this "learning" question in terms of a logistic regression model and
explore its predictive accuracy computationally. The proposed model uses a
collection of solutions to predict the components of the optimal solutions. To
illustrate the utility of such predictions, we embed the logistic regression
model into the tabu search algorithm for job shop scheduling problem. The
resulting framework is simple to implement, yet provides a significant boost to
the performance of the standard tabu search.
Spherical Latent Spaces for Stable Variational Autoencoders
Authors: Jiacheng Xu, Greg Durrett
Date: 2018-08-31 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Language Modeling, Language Modelling, Topic Models

A hallmark of variational autoencoders (VAEs) for text processing is their
combination of powerful encoder-decoder models, such as LSTMs, with simple
latent distributions, typically multivariate Gaussians. These models pose a
difficult optimization problem: there is an especially bad local optimum where
the variational posterior always equals the prior and the model does not use
the latent variable at all, a kind of "collapse" which is encouraged by the KL
divergence term of the objective. In this work, we experiment with another
choice of latent distribution, namely the von Mises-Fisher (vMF) distribution,
which places mass on the surface of the unit hypersphere. With this choice of
prior and posterior, the KL divergence term now only depends on the variance of
the vMF distribution, giving us the ability to treat it as a fixed
hyperparameter. We show that doing so not only averts the KL collapse, but
consistently gives better likelihoods than Gaussians across a range of modeling
conditions, including recurrent language modeling and bag-of-words document
modeling. An analysis of the properties of our vMF representations shows that
they learn richer and more nuanced structures in their latent representations
than their Gaussian counterparts.
The MeMAD Submission to the WMT18 Multimodal Translation Task
Authors: Stig-Arne Grönroos, Benoit Huet, Mikko Kurimo, Jorma Laaksonen, Bernard Merialdo, Phu Pham, Mats Sjöberg, Umut Sulubacak, Jörg Tiedemann, Raphael Troncy, Raúl Vázquez
Date: 2018-08-31 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Multimodal Machine Translation, NMT, Translation

This paper describes the MeMAD project entry to the WMT Multimodal Machine
Translation Shared Task.
  We propose adapting the Transformer neural machine translation (NMT)
architecture to a multi-modal setting. In this paper, we also describe the
preliminary experiments with text-only translation systems leading us up to
this choice.
  We have the top scoring system for both English-to-German and
English-to-French, according to the automatic metrics for flickr18.
  Our experiments show that the effect of the visual features in our system is
small. Our largest gains come from the quality of the underlying text-only NMT
system. We find that appropriate use of additional data is effective.
Bottom-Up Abstractive Summarization
Authors: Sebastian Gehrmann, Yuntian Deng, Alexander M. Rush
Date: 2018-08-31 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Document Summarization, Multi-Document Summarization

Neural network-based methods for abstractive summarization produce outputs
that are more fluent than other techniques, but which can be poor at content
selection. This work proposes a simple technique for addressing this issue: use
a data-efficient content selector to over-determine phrases in a source
document that should be part of the summary. We use this selector as a
bottom-up attention step to constrain the model to likely phrases. We show that
this approach improves the ability to compress text, while still generating
fluent summaries. This two-step process is both simpler and higher performing
than other end-to-end content selection models, leading to significant
improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the
content selector can be trained with as little as 1,000 sentences, making it
easy to transfer a trained summarizer to a new domain.
Cognate-aware morphological segmentation for multilingual neural translation
Authors: Stig-Arne Grönroos, Sami Virpioja, Mikko Kurimo
Date: 2018-08-31 00:00:00
Proceeding: WS 2018 10
Tasks: Translation

This article describes the Aalto University entry to the WMT18 News
Translation Shared Task. We participate in the multilingual subtrack with a
system trained under the constrained condition to translate from English to
both Finnish and Estonian. The system is based on the Transformer model. We
focus on improving the consistency of morphological segmentation for words that
are similar orthographically, semantically, and distributionally; such words
include etymological cognates, loan words, and proper names. For this, we
introduce Cognate Morfessor, a multilingual variant of the Morfessor method. We
show that our approach improves the translation quality particularly for
Estonian, which has less resources for training the translation model.
Data-driven discovery of PDEs in complex datasets
Authors: Jens Berg, Kaj Nyström
Date: 2018-08-31 00:00:00
Tasks: Deep Learning, Model Selection

Many processes in science and engineering can be described by partial
differential equations (PDEs). Traditionally, PDEs are derived by considering
first principles of physics to derive the relations between the involved
physical quantities of interest. A different approach is to measure the
quantities of interest and use deep learning to reverse engineer the PDEs which
are describing the physical process.
  In this paper we use machine learning, and deep learning in particular, to
discover PDEs hidden in complex data sets from measurement data. We include
examples of data from a known model problem, and real data from weather station
measurements. We show how necessary transformations of the input data amounts
to coordinate transformations in the discovered PDE, and we elaborate on
feature and model selection. It is shown that the dynamics of a non-linear,
second order PDE can be accurately described by an ordinary differential
equation which is automatically discovered by our deep learning algorithm. Even
more interestingly, we show that similar results apply in the context of more
complex simulations of the Swedish temperature distribution.
Multimodal Continuous Turn-Taking Prediction Using Multiscale RNNs
Authors: Matthew Roddy, Gabriel Skantze, Naomi Harte
Date: 2018-08-31 00:00:00
Tasks: Prediction

In human conversational interactions, turn-taking exchanges can be
coordinated using cues from multiple modalities. To design spoken dialog
systems that can conduct fluid interactions it is desirable to incorporate cues
from separate modalities into turn-taking models. We propose that there is an
appropriate temporal granularity at which modalities should be modeled. We
design a multiscale RNN architecture to model modalities at separate timescales
in a continuous manner. Our results show that modeling linguistic and acoustic
features at separate temporal rates can be beneficial for turn-taking modeling.
We also show that our approach can be used to incorporate gaze features into
turn-taking models.
Using a Game Engine to Simulate Critical Incidents and Data Collection by Autonomous Drones
Authors: David L. Smyth, Frank G. Glavin, Michael G. Madden
Date: 2018-08-31 00:00:00

Using a game engine, we have developed a virtual environment which models
important aspects of critical incident scenarios. We focused on modelling
phenomena relating to the identification and gathering of key forensic
evidence, in order to develop and test a system which can handle chemical,
biological, radiological/nuclear or explosive (CBRNe) events autonomously. This
allows us to build and validate AI-based technologies, which can be trained and
tested in our custom virtual environment before being deployed in real-world
scenarios. We have used our virtual scenario to rapidly prototype a system
which can use simulated Remote Aerial Vehicles (RAVs) to gather images from the
environment for the purpose of mapping. Our environment provides us with an
effective medium through which we can develop and test various AI methodologies
for critical incident scene assessment, in a safe and controlled manner
Scalable Manifold Learning for Big Data with Apache Spark
Authors: Frank Schoeneman, Jaroslaw Zola
Date: 2018-08-31 00:00:00
Tasks: Dimensionality Reduction

Non-linear spectral dimensionality reduction methods, such as Isomap, remain
important technique for learning manifolds. However, due to computational
complexity, exact manifold learning using Isomap is currently impossible from
large-scale data. In this paper, we propose a distributed memory framework
implementing end-to-end exact Isomap under Apache Spark model. We show how each
critical step of the Isomap algorithm can be efficiently realized using basic
Spark model, without the need to provision data in the secondary storage. We
show how the entire method can be implemented using PySpark, offloading compute
intensive linear algebra routines to BLAS. Through experimental results, we
demonstrate excellent scalability of our method, and we show that it can
process datasets orders of magnitude larger than what is currently possible,
using a 25-node parallel~cluster.