pwc-archive/papers-with-abstracts huggingface dataset:
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification
Authors: Iaroslav Omelianenko
Date: 2017-08-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), SSVEP, User Identification

In this article we present the results of our research related to the study
of correlations between specific visual stimulation and the elicited brain's
electro-physiological response collected by EEG sensors from a group of
participants. We will look at how the various characteristics of visual
stimulation affect the measured electro-physiological response of the brain and
describe the optimal parameters found that elicit a steady-state visually
evoked potential (SSVEP) in certain parts of the cerebral cortex where it can
be reliably perceived by the electrode of the EEG device. After that, we
continue with a description of the advanced machine learning pipeline model
that can perform confident classification of the collected EEG data in order to
(a) reliably distinguish signal from noise (about 85% validation score) and (b)
reliably distinguish between EEG records collected from different human
participants (about 80% validation score). Finally, we demonstrate that the
proposed method works reliably even with an inexpensive (less than $100)
consumer-grade EEG sensing device and with participants who do not have
previous experience with EEG technology (EEG illiterate). All this in
combination opens up broad prospects for the development of new types of
consumer devices, [e.g.] based on virtual reality helmets or augmented reality
glasses where EEG sensor can be easily integrated. The proposed method can be
used to improve an online user experience by providing [e.g.] password-less
user identification for VR / AR applications. It can also find a more advanced
application in intensive care units where collected EEG data can be used to
classify the level of conscious awareness of patients during anesthesia or to
automatically detect hardware failures by classifying the input signal as
noise.
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Authors: Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo
Date: 2017-07-26 00:00:00
Tasks: Deep Learning, Management

Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.
Submodular Mini-Batch Training in Generative Moment Matching Networks
Authors: Jun Qi
Date: 2017-07-18 00:00:00

This article was withdrawn because (1) it was uploaded without the
co-authors' knowledge or consent, and (2) there are allegations of plagiarism.
Deep MR to CT Synthesis using Unpaired Data
Authors: Jelmer M. Wolterink, Anna M. Dinkla, Mark H. F. Savenije, Peter R. Seevinck, Cornelis A. T. van den Berg, Ivana Isgum
Date: 2017-08-03 00:00:00
Tasks: Generative Adversarial Network

MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis.
Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned
MR and CT training images of the same patient. However, misalignment between
paired images could lead to errors in synthesized CT images. To overcome this,
we propose to train a generative adversarial network (GAN) with unpaired MR and
CT images. A GAN consisting of two synthesis convolutional neural networks
(CNNs) and two discriminator CNNs was trained with cycle consistency to
transform 2D brain MR image slices into 2D brain CT image slices and vice
versa. Brain MR and CT images of 24 patients were analyzed. A quantitative
evaluation showed that the model was able to synthesize CT images that closely
approximate reference CT images, and was able to outperform a GAN model trained
with paired MR and CT images.
Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization
Authors: Jinyuan Zhang, Aimin Zhou, Ke Tang, Guixu Zhang
Date: 2017-08-03 00:00:00
Tasks: Classification, Evolutionary Algorithms, General Classification, Multiobjective Optimization

In evolutionary algorithms, a preselection operator aims to select the
promising offspring solutions from a candidate offspring set. It is usually
based on the estimated or real objective values of the candidate offspring
solutions. In a sense, the preselection can be treated as a classification
procedure, which classifies the candidate offspring solutions into promising
ones and unpromising ones. Following this idea, we propose a classification
based preselection (CPS) strategy for evolutionary multiobjective optimization.
When applying classification based preselection, an evolutionary algorithm
maintains two external populations (training data set) that consist of some
selected good and bad solutions found so far; then it trains a classifier based
on the training data set in each generation. Finally it uses the classifier to
filter the unpromising candidate offspring solutions and choose a promising one
from the generated candidate offspring set for each parent solution. In such
cases, it is not necessary to estimate or evaluate the objective values of the
candidate offspring solutions. The classification based preselection is applied
to three state-of-the-art multiobjective evolutionary algorithms (MOEAs) and is
empirically studied on two sets of test instances. The experimental results
suggest that classification based preselection can successfully improve the
performance of these MOEAs.
Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC
Authors: Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Jose Garcia-Rodriguez
Date: 2017-08-03 00:00:00
Tasks: Clustering, Indoor Localization, Model extraction, Object, Object Reconstruction

Plane model extraction from three-dimensional point clouds is a necessary
step in many different applications such as planar object reconstruction,
indoor mapping and indoor localization. Different RANdom SAmple Consensus
(RANSAC)-based methods have been proposed for this purpose in recent years. In
this study, we propose a novel method-based on RANSAC called Multiplane Model
Estimation, which can estimate multiple plane models simultaneously from a
noisy point cloud using the knowledge extracted from a scene (or an object) in
order to reconstruct it accurately. This method comprises two steps: first, it
clusters the data into planar faces that preserve some constraints defined by
knowledge related to the object (e.g., the angles between faces); and second,
the models of the planes are estimated based on these data using a novel
multi-constraint RANSAC. We performed experiments in the clustering and RANSAC
stages, which showed that the proposed method performed better than
state-of-the-art methods.
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
Authors: Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Isgum
Date: 2017-08-03 00:00:00
Tasks: General Classification, Segmentation

Segmentation of the heart in cardiac cine MR is clinically used to quantify
cardiac function. We propose a fully automatic method for segmentation and
disease classification using cardiac cine MR images. A convolutional neural
network (CNN) was designed to simultaneously segment the left ventricle (LV),
right ventricle (RV) and myocardium in end-diastole (ED) and end-systole (ES)
images. Features derived from the obtained segmentations were used in a Random
Forest classifier to label patients as suffering from dilated cardiomyopathy,
hypertrophic cardiomyopathy, heart failure following myocardial infarction,
right ventricular abnormality, or no cardiac disease. The method was developed
and evaluated using a balanced dataset containing images of 100 patients, which
was provided in the MICCAI 2017 automated cardiac diagnosis challenge (ACDC).
The segmentation and classification pipeline were evaluated in a four-fold
stratified cross-validation. Average Dice scores between reference and
automatically obtained segmentations were 0.94, 0.88 and 0.87 for the LV, RV
and myocardium. The classifier assigned 91% of patients to the correct disease
category. Segmentation and disease classification took 5 s per patient. The
results of our study suggest that image-based diagnosis using cine MR cardiac
scans can be performed automatically with high accuracy.
On Calibration of Modern Neural Networks
Authors: Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
Date: 2017-06-14 00:00:00
Proceeding: ICML 2017 8
Tasks: Document Classification, General Classification

Confidence calibration -- the problem of predicting probability estimates
representative of the true correctness likelihood -- is important for
classification models in many applications. We discover that modern neural
networks, unlike those from a decade ago, are poorly calibrated. Through
extensive experiments, we observe that depth, width, weight decay, and Batch
Normalization are important factors influencing calibration. We evaluate the
performance of various post-processing calibration methods on state-of-the-art
architectures with image and document classification datasets. Our analysis and
experiments not only offer insights into neural network learning, but also
provide a simple and straightforward recipe for practical settings: on most
datasets, temperature scaling -- a single-parameter variant of Platt Scaling --
is surprisingly effective at calibrating predictions.
Optimal Belief Approximation
Authors: Reimar H. Leike, Torsten A. Enßlin
Date: 2016-10-27 00:00:00

In Bayesian statistics probability distributions express beliefs. However,
for many problems the beliefs cannot be computed analytically and
approximations of beliefs are needed. We seek a loss function that quantifies
how "embarrassing" it is to communicate a given approximation. We reproduce and
discuss an old proof showing that there is only one ranking under the
requirements that (1) the best ranked approximation is the non-approximated
belief and (2) that the ranking judges approximations only by their predictions
for actual outcomes. The loss function that is obtained in the derivation is
equal to the Kullback-Leibler divergence when normalized. This loss function is
frequently used in the literature. However, there seems to be confusion about
the correct order in which its functional arguments, the approximated and
non-approximated beliefs, should be used. The correct order ensures that the
recipient of a communication is only deprived of the minimal amount of
information. We hope that the elementary derivation settles the apparent
confusion. For example when approximating beliefs with Gaussian distributions
the optimal approximation is given by moment matching. This is in contrast to
many suggested computational schemes.
Algorithmic stability and hypothesis complexity
Authors: Tongliang Liu, Gábor Lugosi, Gergely Neu, DaCheng Tao
Date: 2017-02-28 00:00:00
Proceeding: ICML 2017 8

We introduce a notion of algorithmic stability of learning algorithms---that
we term \emph{argument stability}---that captures stability of the hypothesis
output by the learning algorithm in the normed space of functions from which
hypotheses are selected. The main result of the paper bounds the generalization
error of any learning algorithm in terms of its argument stability. The bounds
are based on martingale inequalities in the Banach space to which the
hypotheses belong. We apply the general bounds to bound the performance of some
learning algorithms based on empirical risk minimization and stochastic
gradient descent.
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop
Authors: Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crisan, Camelia-M. Pintea, Vasile Palade
Date: 2017-08-03 00:00:00
Tasks: Protein Folding, Traveling Salesman Problem

The goal of Machine Learning to automatically learn from data, extract
knowledge and to make decisions without any human intervention. Such automatic
(aML) approaches show impressive success. Recent results even demonstrate
intriguingly that deep learning applied for automatic classification of skin
lesions is on par with the performance of dermatologists, yet outperforms the
average. As human perception is inherently limited, such approaches can
discover patterns, e.g. that two objects are similar, in arbitrarily
high-dimensional spaces what no human is able to do. Humans can deal only with
limited amounts of data, whilst big data is beneficial for aML; however, in
health informatics, we are often confronted with a small number of data sets,
where aML suffer of insufficient training samples and many problems are
computationally hard. Here, interactive machine learning (iML) may be of help,
where a human-in-the-loop contributes to reduce the complexity of NP-hard
problems. A further motivation for iML is that standard black-box approaches
lack transparency, hence do not foster trust and acceptance of ML among
end-users. Rising legal and privacy aspects, e.g. with the new European General
Data Protection Regulations, make black-box approaches difficult to use,
because they often are not able to explain why a decision has been made. In
this paper, we present some experiments to demonstrate the effectiveness of the
human-in-the-loop approach, particularly in opening the black-box to a
glass-box and thus enabling a human directly to interact with an learning
algorithm. We selected the Ant Colony Optimization framework, and applied it on
the Traveling Salesman Problem, which is a good example, due to its relevance
for health informatics, e.g. for the study of protein folding. From studies of
how humans extract so much from so little data, fundamental ML-research also
may benefit.
Learning Feature Pyramids for Human Pose Estimation
Authors: Wei Yang, Shuang Li, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
Date: 2017-08-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: Pose Estimation

Articulated human pose estimation is a fundamental yet challenging task in
computer vision. The difficulty is particularly pronounced in scale variations
of human body parts when camera view changes or severe foreshortening happens.
Although pyramid methods are widely used to handle scale changes at inference
time, learning feature pyramids in deep convolutional neural networks (DCNNs)
is still not well explored. In this work, we design a Pyramid Residual Module
(PRMs) to enhance the invariance in scales of DCNNs. Given input features, the
PRMs learn convolutional filters on various scales of input features, which are
obtained with different subsampling ratios in a multi-branch network. Moreover,
we observe that it is inappropriate to adopt existing methods to initialize the
weights of multi-branch networks, which achieve superior performance than plain
networks in many tasks recently. Therefore, we provide theoretic derivation to
extend the current weight initialization scheme to multi-branch network
structures. We investigate our method on two standard benchmarks for human pose
estimation. Our approach obtains state-of-the-art results on both benchmarks.
Code is available at https://github.com/bearpaw/PyraNet.
Switching Convolutional Neural Network for Crowd Counting
Authors: Deepak Babu Sam, Shiv Surya, R. Venkatesh Babu
Date: 2017-08-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Crowd Counting

We propose a novel crowd counting model that maps a given crowd scene to its
density. Crowd analysis is compounded by myriad of factors like inter-occlusion
between people due to extreme crowding, high similarity of appearance between
people and background elements, and large variability of camera view-points.
Current state-of-the art approaches tackle these factors by using multi-scale
CNN architectures, recurrent networks and late fusion of features from
multi-column CNN with different receptive fields. We propose switching
convolutional neural network that leverages variation of crowd density within
an image to improve the accuracy and localization of the predicted crowd count.
Patches from a grid within a crowd scene are relayed to independent CNN
regressors based on crowd count prediction quality of the CNN established
during training. The independent CNN regressors are designed to have different
receptive fields and a switch classifier is trained to relay the crowd scene
patch to the best CNN regressor. We perform extensive experiments on all major
crowd counting datasets and evidence better performance compared to current
state-of-the-art methods. We provide interpretable representations of the
multichotomy of space of crowd scene patches inferred from the switch. It is
observed that the switch relays an image patch to a particular CNN column based
on density of crowd.
Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran
Authors: Shaghayegh Kargozar Nahavandya, Lalit Kumar, Pedram Ghamisi
Date: 2017-08-03 00:00:00

The SLEUTH model, based on the Cellular Automata (CA), can be applied to city
development simulation in metropolitan areas. In this study the SLEUTH model
was used to model the urban expansion and predict the future possible behavior
of the urban growth in Tehran. The fundamental data were five Landsat TM and
ETM images of 1988, 1992, 1998, 2001 and 2010. Three scenarios were designed to
simulate the spatial pattern. The first scenario assumed historical
urbanization mode would persist and the only limitations for development were
height and slope. The second one was a compact scenario which makes the growth
mostly internal and limited the expansion of suburban areas. The last scenario
proposed a polycentric urban structure which let the little patches grow
without any limitation and would not consider the areas beyond the specific
buffer zone from the larger patches for development. Results showed that the
urban growth rate was greater in the first scenario in comparison with the
other two scenarios. Also it was shown that the third scenario was more
suitable for Tehran since it could avoid undesirable effects such as congestion
and pollution and was more in accordance with the conditions of Tehran city.
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
Authors: Piji Li, Lidong Bing, Wai Lam
Date: 2017-08-03 00:00:00
Proceeding: WS 2017 9
Tasks: Document Summarization, Multi-Document Summarization

We investigate the problem of reader-aware multi-document summarization
(RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we
extend a variational auto-encodes (VAEs) based MDS framework by jointly
considering news documents and reader comments. To conduct evaluation for
summarization performance, we prepare a new dataset. We describe the methods
for data collection, aspect annotation, and summary writing as well as
scrutinizing by experts. Experimental results show that reader comments can
improve the summarization performance, which also demonstrates the usefulness
of the proposed dataset. The annotated dataset for RA-MDS is available online.
Learning to Discover Sparse Graphical Models
Authors: Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew Blaschko
Date: 2016-05-20 00:00:00
Proceeding: ICML 2017 8

We consider structure discovery of undirected graphical models from
observational data. Inferring likely structures from few examples is a complex
task often requiring the formulation of priors and sophisticated inference
procedures. Popular methods rely on estimating a penalized maximum likelihood
of the precision matrix. However, in these approaches structure recovery is an
indirect consequence of the data-fit term, the penalty can be difficult to
adapt for domain-specific knowledge, and the inference is computationally
demanding. By contrast, it may be easier to generate training samples of data
that arise from graphs with the desired structure properties. We propose here
to leverage this latter source of information as training data to learn a
function, parametrized by a neural network that maps empirical covariance
matrices to estimated graph structures. Learning this function brings two
benefits: it implicitly models the desired structure or sparsity properties to
form suitable priors, and it can be tailored to the specific problem of edge
structure discovery, rather than maximizing data likelihood. Applying this
framework, we find our learnable graph-discovery method trained on synthetic
data generalizes well: identifying relevant edges in both synthetic and real
data, completely unknown at training time. We find that on genetics, brain
imaging, and simulation data we obtain performance generally superior to
analytical methods.
Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning
Authors: Mike Kestemont, Jeroen De Gussem
Date: 2016-03-04 00:00:00
Tasks: LEMMA, Lemmatization, Part-Of-Speech Tagging, POS, Representation Learning, TAG

In this paper we consider two sequence tagging tasks for medieval Latin:
part-of-speech tagging and lemmatization. These are both basic, yet
foundational preprocessing steps in applications such as text re-use detection.
Nevertheless, they are generally complicated by the considerable orthographic
variation which is typical of medieval Latin. In Digital Classics, these tasks
are traditionally solved in a (i) cascaded and (ii) lexicon-dependent fashion.
For example, a lexicon is used to generate all the potential lemma-tag pairs
for a token, and next, a context-aware PoS-tagger is used to select the most
appropriate tag-lemma pair. Apart from the problems with out-of-lexicon items,
error percolation is a major downside of such approaches. In this paper we
explore the possibility to elegantly solve these tasks using a single,
integrated approach. For this, we make use of a layered neural network
architecture from the field of deep representation learning.
Detection of Abnormal Input-Output Associations
Authors: Charmgil Hong, Si-Qi Liu, Milos Hauskrecht
Date: 2017-08-03 00:00:00
Tasks: Outlier Detection, Relation

We study a novel outlier detection problem that aims to identify abnormal
input-output associations in data, whose instances consist of multi-dimensional
input (context) and output (responses) pairs. We present our approach that
works by analyzing data in the conditional (input--output) relation space,
captured by a decomposable probabilistic model. Experimental results
demonstrate the ability of our approach in identifying multivariate conditional
outliers.