pwc-archive/papers-with-abstracts huggingface dataset:
Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
Authors: Muhammad Naveed Tabassum, Esa Ollila
Date: 2018-04-11 00:00:00
Tasks: feature selection

We propose a modification of linear discriminant analysis, referred to as
compressive regularized discriminant analysis (CRDA), for analysis of
high-dimensional datasets. CRDA is specially designed for feature elimination
purpose and can be used as gene selection method in microarray studies. CRDA
lends ideas from $\ell_{q,1}$ norm minimization algorithms in the multiple
measurement vectors (MMV) model and utilizes joint-sparsity promoting hard
thresholding for feature elimination. A regularization of the sample covariance
matrix is also needed as we consider the challenging scenario where the number
of features (variables) is comparable or exceeding the sample size of the
training dataset. A simulation study and four examples of real-life microarray
datasets evaluate the performances of CRDA based classifiers. Overall, the
proposed method gives fewer misclassification errors than its competitors,
while at the same time achieving accurate feature selection.
Emergent Communication through Negotiation
Authors: Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z. Leibo, Karl Tuyls, Stephen Clark
Date: 2018-04-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning

Multi-agent reinforcement learning offers a way to study how communication
could emerge in communities of agents needing to solve specific problems. In
this paper, we study the emergence of communication in the negotiation
environment, a semi-cooperative model of agent interaction. We introduce two
communication protocols -- one grounded in the semantics of the game, and one
which is \textit{a priori} ungrounded and is a form of cheap talk. We show that
self-interested agents can use the pre-grounded communication channel to
negotiate fairly, but are unable to effectively use the ungrounded channel.
However, prosocial agents do learn to use cheap talk to find an optimal
negotiating strategy, suggesting that cooperation is necessary for language to
emerge. We also study communication behaviour in a setting where one agent
interacts with agents in a community with different levels of prosociality and
show how agent identifiability can aid negotiation.
Edge-based LBP description of surfaces with colorimetric patterns
Authors: Elia Moscoso Thompson, Silvia Biasotti
Date: 2018-04-11 00:00:00
Tasks: Retrieval

In this paper we target the problem of the retrieval of colour patterns over
surfaces. We generalize to surface tessellations the well known Local Binary
Pattern (LBP) descriptor for images. The key concept of the LBP is to code the
variability of the colour values around each pixel. In the case of a surface
tessellation we adopt rings around vertices that are obtained with a
sphere-mesh intersection driven by the edges of the mesh; for this reason, we
name our method edgeLBP. Experimental results are provided to show how this
description performs well for pattern retrieval, also when patterns come from
degraded and corrupted archaeological fragments.
Data Augmentation by Pairing Samples for Images Classification
Authors: Hiroshi Inoue
Date: 2018-01-09 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification

Data augmentation is a widely used technique in many machine learning tasks,
such as image classification, to virtually enlarge the training dataset size
and avoid overfitting. Traditional data augmentation techniques for image
classification tasks create new samples from the original training data by, for
example, flipping, distorting, adding a small amount of noise to, or cropping a
patch from an original image. In this paper, we introduce a simple but
surprisingly effective data augmentation technique for image classification
tasks. With our technique, named SamplePairing, we synthesize a new sample from
one image by overlaying another image randomly chosen from the training data
(i.e., taking an average of two images for each pixel). By using two images
randomly selected from the training set, we can generate $N^2$ new samples from
$N$ training samples. This simple data augmentation technique significantly
improved classification accuracy for all the tested datasets; for example, the
top-1 error rate was reduced from 33.5% to 29.0% for the ILSVRC 2012 dataset
with GoogLeNet and from 8.22% to 6.93% in the CIFAR-10 dataset. We also show
that our SamplePairing technique largely improved accuracy when the number of
samples in the training set was very small. Therefore, our technique is more
valuable for tasks with a limited amount of training data, such as medical
imaging tasks.
Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systems
Authors: Cumhur Erkan Tuncali, James Kapinski, Hisahiro Ito, Jyotirmoy V. Deshmukh
Date: 2018-04-11 00:00:00

We present a simulation-based approach for generating barrier certificate
functions for safety verification of cyber-physical systems (CPS) that contain
neural network-based controllers. A linear programming solver is utilized to
find a candidate generator function from a set of simulation traces obtained by
randomly selecting initial states for the CPS model. A level set of the
generator function is then selected to act as a barrier certificate for the
system, meaning it demonstrates that no unsafe system states are reachable from
a given set of initial states. The barrier certificate properties are verified
with an SMT solver. This approach is demonstrated on a case study in which a
Dubins car model of an autonomous vehicle is controlled by a neural network to
follow a given path.
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
Authors: Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Williams Rizzi, Cosimo Damiano Persia
Date: 2018-04-11 00:00:00
Tasks: Incremental Learning, Predictive Process Monitoring

A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make predictive process monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviors over time. As a solution to this problem, we propose the use of algorithms that allow the incremental construction of the predictive model. These incremental learning algorithms update the model whenever new cases become available so that the predictive model evolves over time to fit the current circumstances. The algorithms have been implemented using different case encoding strategies and evaluated on a number of real and synthetic datasets. The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting.
Projection image-to-image translation in hybrid X-ray/MR imaging
Authors: Bernhard Stimpel, Christopher Syben, Tobias Würfl, Katharina Breininger, Katrin Mentl, Jonathan M. Lommen, Arnd Dörfler, Andreas Maier
Date: 2018-04-11 00:00:00
Tasks: Image Enhancement, Image Generation, Image-to-Image Translation, Translation

The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding X-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating X-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
A Variable Neighborhood Search for Flying Sidekick Traveling Salesman Problem
Authors: Julia C. Freitas, Puca Huachi V. Penna
Date: 2018-04-11 00:00:00
Tasks: Traveling Salesman Problem

The efficiency and dynamism of Unmanned Aerial Vehicles (UAVs), or drones,
present substantial application opportunities in several industries in the last
years. Notably, the logistic companies gave close attention to these vehicles
envisioning reduce delivery time and operational cost. A variant of the
Traveling Salesman Problem (TSP) called Flying Sidekick Traveling Salesman
Problem (FSTSP) was introduced involving drone-assisted parcel delivery. The
drone is launched from the truck, proceeds to deliver parcels to a customer and
then is recovered by the truck in a third location. While the drone travels
through a trip, the truck delivers parcels to other customers as long as the
drone has enough battery to hover waiting for the truck. This work proposes a
hybrid heuristic that the initial solution is created from the optimal TSP
solution reached by a TSP solver. Next, an implementation of the General
Variable Neighborhood Search is used to obtain the delivery routes of truck and
drone. Computational experiments show the potential of the algorithm to improve
the delivery time significantly. Furthermore, we provide a new set of instances
based on well-known TSPLIB instances.
VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning
Authors: Heoun-taek Lim, Hak Gu Kim, Yong Man Ro
Date: 2018-04-11 00:00:00
Tasks: Image Quality Assessment, Position

In this paper, we propose a novel virtual reality image quality assessment
(VR IQA) with adversarial learning for omnidirectional images. To take into
account the characteristics of the omnidirectional image, we devise deep
networks including novel quality score predictor and human perception guider.
The proposed quality score predictor automatically predicts the quality score
of distorted image using the latent spatial and position feature. The proposed
human perception guider criticizes the predicted quality score of the predictor
with the human perceptual score using adversarial learning. For evaluation, we
conducted extensive subjective experiments with omnidirectional image dataset.
Experimental results show that the proposed VR IQA metric outperforms the 2-D
IQA and the state-of-the-arts VR IQA.
Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder
Authors: Hak Gu Kim, Wissam J. Baddar, Heoun-taek Lim, Hyunwook Jeong, Yong Man Ro
Date: 2018-04-11 00:00:00

This paper proposes a new objective metric of exceptional motion in VR video
contents for VR sickness assessment. In VR environment, VR sickness can be
caused by several factors which are mismatched motion, field of view, motion
parallax, viewing angle, etc. Similar to motion sickness, VR sickness can
induce a lot of physical symptoms such as general discomfort, headache, stomach
awareness, nausea, vomiting, fatigue, and disorientation. To address the
viewing safety issues in virtual environment, it is of great importance to
develop an objective VR sickness assessment method that predicts and analyses
the degree of VR sickness induced by the VR content. The proposed method takes
into account motion information that is one of the most important factors in
determining the overall degree of VR sickness. In this paper, we detect the
exceptional motion that is likely to induce VR sickness. Spatio-temporal
features of the exceptional motion in the VR video content are encoded using a
convolutional autoencoder. For objectively assessing the VR sickness, the level
of exceptional motion in VR video content is measured by using the
convolutional autoencoder as well. The effectiveness of the proposed method has
been successfully evaluated by subjective assessment experiment using simulator
sickness questionnaires (SSQ) in VR environment.
An Iterative Closest Points Approach to Neural Generative Models
Authors: Joose Rajamäki, Perttu Hämäläinen
Date: 2017-11-16 00:00:00

We present a simple way to learn a transformation that maps samples of one
distribution to the samples of another distribution. Our algorithm comprises an
iteration of 1) drawing samples from some simple distribution and transforming
them using a neural network, 2) determining pairwise correspondences between
the transformed samples and training data (or a minibatch), and 3) optimizing
the weights of the neural network being trained to minimize the distances
between the corresponding vectors. This can be considered as a variant of the
Iterative Closest Points (ICP) algorithm, common in geometric computer vision,
although ICP typically operates on sensor point clouds and linear transforms
instead of random sample sets and neural nonlinear transforms. We demonstrate
the algorithm on simple synthetic data and MNIST data. We furthermore
demonstrate that the algorithm is capable of handling distributions with both
continuous and discrete variables.
Offline Object Extraction from Dynamic Occupancy Grid Map Sequences
Authors: Daniel Stumper, Fabian Gies, Stefan Hoermann, Klaus Dietmayer
Date: 2018-04-11 00:00:00
Tasks: Object

A dynamic occupancy grid map (DOGMa) allows a fast, robust, and complete
environment representation for automated vehicles. Dynamic objects in a DOGMa,
however, are commonly represented as independent cells while modeled objects
with shape and pose are favorable. The evaluation of algorithms for object
extraction or the training and validation of learning algorithms rely on
labeled ground truth data. Manually annotating objects in a DOGMa to obtain
ground truth data is a time consuming and expensive process. Additionally the
quality of labeled data depend strongly on the variation of filtered input
data. The presented work introduces an automatic labeling process, where a full
sequence is used to extract the best possible object pose and shape in terms of
temporal consistency. A two direction temporal search is executed to trace
single objects over a sequence, where the best estimate of its extent and pose
is refined in every time step. Furthermore, the presented algorithm only uses
statistical constraints of the cell clusters for the object extraction instead
of fixed heuristic parameters. Experimental results show a well-performing
automatic labeling algorithm with real sensor data even at challenging
scenarios.
Deep Learning For Computer Vision Tasks: A review
Authors: Rajat Kumar Sinha, Ruchi Pandey, Rohan Pattnaik
Date: 2018-04-11 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, Semantic Segmentation

Deep learning has recently become one of the most popular sub-fields of
machine learning owing to its distributed data representation with multiple
levels of abstraction. A diverse range of deep learning algorithms are being
employed to solve conventional artificial intelligence problems. This paper
gives an overview of some of the most widely used deep learning algorithms
applied in the field of computer vision. It first inspects the various
approaches of deep learning algorithms, followed by a description of their
applications in image classification, object identification, image extraction
and semantic segmentation in the presence of noise. The paper concludes with
the discussion of the future scope and challenges for construction and training
of deep neural networks.
Generating Multilingual Parallel Corpus Using Subtitles
Authors: Farshad Jafari
Date: 2018-04-11 00:00:00
Tasks: Machine Translation, Sentence

Neural Machine Translation with its significant results, still has a great
problem: lack or absence of parallel corpus for many languages. This article
suggests a method for generating considerable amount of parallel corpus for any
language pairs, extracted from open source materials existing on the Internet.
Parallel corpus contents will be derived from video subtitles. It needs a set
of video titles, with some attributes like release date, rating, duration and
etc. Process of finding and downloading subtitle pairs for desired language
pairs is automated by using a crawler. Finally sentence pairs will be extracted
from synchronous dialogues in subtitles. The main problem of this method is
unsynchronized subtitle pairs. Therefore subtitles will be verified before
downloading. If two subtitle were not synchronized, then another subtitle of
that video will be processed till it finds the matching subtitle. Using this
approach gives ability to make context based parallel corpus through filtering
videos by genre. Context based corpus can be used in complex translators which
decode sentences by different networks after determining contents subject.
Languages have many differences in their formal and informal styles, including
words and syntax. Other advantage of this method is to make corpus of informal
style of languages. Because most of movies dialogues are parts of a
conversation. So they had informal style. This feature of generated corpus can
be used in real-time translators to have more accurate conversation
translations.
Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models
Authors: Damian Kozbur
Date: 2017-02-03 00:00:00
Tasks: Model Selection, regression, Vocal Bursts Intensity Prediction

Forward regression is a statistical model selection and estimation procedure
which inductively selects covariates that add predictive power into a working
statistical regression model. Once a model is selected, unknown regression
parameters are estimated by least squares. This paper analyzes forward
regression in high-dimensional sparse linear models. Probabilistic bounds for
prediction error norm and number of selected covariates are proved. The
analysis in this paper gives sharp rates and does not require beta-min or
irrepresentability conditions.
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Authors: Vassilis Vassiliades, Jean-Baptiste Mouret
Date: 2018-04-11 00:00:00
Tasks: Diversity

Evolution has produced an astonishing diversity of species, each filling a
different niche. Algorithms like MAP-Elites mimic this divergent evolutionary
process to find a set of behaviorally diverse but high-performing solutions,
called the elites. Our key insight is that species in nature often share a
surprisingly large part of their genome, in spite of occupying very different
niches; similarly, the elites are likely to be concentrated in a specific
"elite hypervolume" whose shape is defined by their common features. In this
paper, we first introduce the elite hypervolume concept and propose two metrics
to characterize it: the genotypic spread and the genotypic similarity. We then
introduce a new variation operator, called "directional variation", that
exploits interspecies (or inter-elites) correlations to accelerate the
MAP-Elites algorithm. We demonstrate the effectiveness of this operator in
three problems (a toy function, a redundant robotic arm, and a hexapod robot).
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
Authors: Aymeric Dieuleveut, Alain Durmus, Francis Bach
Date: 2017-07-20 00:00:00

We consider the minimization of an objective function given access to
unbiased estimates of its gradient through stochastic gradient descent (SGD)
with constant step-size. While the detailed analysis was only performed for
quadratic functions, we provide an explicit asymptotic expansion of the moments
of the averaged SGD iterates that outlines the dependence on initial
conditions, the effect of noise and the step-size, as well as the lack of
convergence in the general (non-quadratic) case. For this analysis, we bring
tools from Markov chain theory into the analysis of stochastic gradient. We
then show that Richardson-Romberg extrapolation may be used to get closer to
the global optimum and we show empirical improvements of the new extrapolation
scheme.
Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization
Authors: Hakan Karaoguz, Patric Jensfelt
Date: 2018-04-11 00:00:00
Tasks: Decoder, Object Localization, Region Proposal, Unsupervised Object Localization

In this paper we address the problem of unsupervised localization of objects
in single images. Compared to previous state-of-the-art method our method is
fully unsupervised in the sense that there is no prior instance level or
category level information about the image. Furthermore, we treat each image
individually and do not rely on any neighboring image similarity. We employ
deep-learning based generation of saliency maps and region proposals to tackle
this problem. First salient regions in the image are determined using an
encoder/decoder architecture. The resulting saliency map is matched with region
proposals from a class agnostic region proposal network to roughly localize the
candidate object regions. These regions are further refined based on the
overlap and similarity ratios. Our experimental evaluations on a benchmark
dataset show that the method gets close to current state-of-the-art methods in
terms of localization accuracy even though these make use of multiple frames.
Furthermore, we created a more challenging and realistic dataset with multiple
object categories and varying viewpoint and illumination conditions for
evaluating the method's performance in real world scenarios.
Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning
Authors: Nils Gessert, Markus Heyder, Sarah Latus, Matthias Lutz, Alexander Schlaefer
Date: 2018-04-11 00:00:00
Tasks: General Classification, Transfer Learning

Advanced atherosclerosis in the coronary arteries is one of the leading
causes of deaths worldwide while being preventable and treatable. In order to
image atherosclerotic lesions (plaque), intravascular optical coherence
tomography (IVOCT) can be used. The technique provides high-resolution images
of arterial walls which allows for early plaque detection by experts. Due to
the vast amount of IVOCT images acquired in clinical routines, automatic plaque
detection has been addressed. For example, attenuation profiles in single
A-Scans of IVOCT images are examined to detect plaque. We address automatic
plaque classification from entire IVOCT images, the cross-sectional view of the
artery, using deep feature learning. In this way, we take context between
A-Scans into account and we directly learn relevant features from the image
source without the need for handcrafting features.