pwc-archive/papers-with-abstracts huggingface dataset:
Fashioning with Networks: Neural Style Transfer to Design Clothes
Authors: Prutha Date, Ashwinkumar Ganesan, Tim Oates
Date: 2017-07-31 00:00:00
Tasks: Image Segmentation, object-detection, Object Detection, Object Recognition, Semantic Segmentation, Style Transfer, Texture Synthesis

Convolutional Neural Networks have been highly successful in performing a
host of computer vision tasks such as object recognition, object detection,
image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how
the style of a painter can be extracted from an image of the painting and
applied to another normal photograph, thus recreating the photo in the style of
the painter. The method has been successfully applied to a wide range of images
and has since spawned multiple applications and mobile apps. In this paper, the
neural style transfer algorithm is applied to fashion so as to synthesize new
custom clothes. We construct an approach to personalize and generate new custom
clothes based on a users preference and by learning the users fashion choices
from a limited set of clothes from their closet. The approach is evaluated by
analyzing the generated images of clothes and how well they align with the
users fashion style.
Automatic segmentation of the intracranialvolume in fetal MR images
Authors: N. Khalili, P. Moeskops, N. H. P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus, M. J. N. L. Benders, M. A. Viergever, J. P. W. Pluim, I. Išgum
Date: 2017-07-31 00:00:00
Tasks: Segmentation

MR images of the fetus allow non-invasive analysis of the fetal brain.
Quantitative analysis of fetal brain development requires automatic brain
tissue segmentation that is typically preceded by segmentation of the
intracranial volume (ICV). This is challenging because fetal MR images
visualize the whole moving fetus and in addition partially visualize the
maternal body. This paper presents an automatic method for segmentation of the
ICV in fetal MR images. The method employs a multi-scale convolutional neural
network in 2D slices to enable learning spatial information from larger context
as well as detailed local information. The method is developed and evaluated
with 30 fetal T2-weighted MRI scans (average age $33.2\pm1.2$ weeks
postmenstrual age). The set contains $10$ scans acquired in axial, $10$ in
coronal and $10$ in sagittal imaging planes. A reference standard was defined
in all images by manual annotation of the intracranial volume in $10$
equidistantly distributed slices. The automatic analysis was performed by
training and testing the network using scans acquired in the representative
imaging plane as well as combining the training data from all imaging planes.
On average, the automatic method achieved Dice coefficients of 0.90 for the
axial images, 0.90 for the coronal images and 0.92 for the sagittal images.
Combining the training sets resulted in average Dice coefficients of 0.91 for
the axial images, 0.95 for the coronal images, and 0.92 for the sagittal
images. The results demonstrate that the evaluated method achieved good
performance in extracting ICV in fetal MR scans regardless of the imaging
plane.
Quantum Privacy-Preserving Perceptron
Authors: Shenggang Ying, Mingsheng Ying, Yuan Feng
Date: 2017-07-31 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

With the extensive applications of machine learning, the issue of private or
sensitive data in the training examples becomes more and more serious: during
the training process, personal information or habits may be disclosed to
unexpected persons or organisations, which can cause serious privacy problems
or even financial loss. In this paper, we present a quantum privacy-preserving
algorithm for machine learning with perceptron. There are mainly two steps to
protect original training examples. Firstly when checking the current
classifier, quantum tests are employed to detect data user's possible
dishonesty. Secondly when updating the current classifier, private random noise
is used to protect the original data. The advantages of our algorithm are: (1)
it protects training examples better than the known classical methods; (2) it
requires no quantum database and thus is easy to implement.
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English
Authors: Duygu Ataman, Matteo Negri, Marco Turchi, Marcello Federico
Date: 2017-07-31 00:00:00
Tasks: Machine Translation, Morphological Analysis, NMT, Translation

The necessity of using a fixed-size word vocabulary in order to control the
model complexity in state-of-the-art neural machine translation (NMT) systems
is an important bottleneck on performance, especially for morphologically rich
languages. Conventional methods that aim to overcome this problem by using
sub-word or character-level representations solely rely on statistics and
disregard the linguistic properties of words, which leads to interruptions in
the word structure and causes semantic and syntactic losses. In this paper, we
propose a new vocabulary reduction method for NMT, which can reduce the
vocabulary of a given input corpus at any rate while also considering the
morphological properties of the language. Our method is based on unsupervised
morphology learning and can be, in principle, used for pre-processing any
language pair. We also present an alternative word segmentation method based on
supervised morphological analysis, which aids us in measuring the accuracy of
our model. We evaluate our method in Turkish-to-English NMT task where the
input language is morphologically rich and agglutinative. We analyze different
representation methods in terms of translation accuracy as well as the semantic
and syntactic properties of the generated output. Our method obtains a
significant improvement of 2.3 BLEU points over the conventional vocabulary
reduction technique, showing that it can provide better accuracy in open
vocabulary translation of morphologically rich languages.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
Authors: Nils Reimers, Iryna Gurevych
Date: 2017-07-31 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Named Entity Recognition (NER)

In this paper we show that reporting a single performance score is
insufficient to compare non-deterministic approaches. We demonstrate for common
sequence tagging tasks that the seed value for the random number generator can
result in statistically significant (p < 10^-4) differences for
state-of-the-art systems. For two recent systems for NER, we observe an
absolute difference of one percentage point F1-score depending on the selected
seed value, making these systems perceived either as state-of-the-art or
mediocre. Instead of publishing and reporting single performance scores, we
propose to compare score distributions based on multiple executions. Based on
the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we
present network architectures that produce both superior performance as well as
are more stable with respect to the remaining hyperparameters.
Real-Time Visual Localisation in a Tagged Environment
Authors: Jérémy Taquet, Gaël Écorchard, Libor Přeučil
Date: 2017-07-31 00:00:00

In a robotised warehouse a major issue is the safety of human operators in
case of intervention in the work area of the robots. The current solution is to
shut down every robot but it causes a loss of productivity, especially for
large robotised warehouses. In order to avoid this loss we need to ensure the
operator's security during his/her intervention in the warehouse without
powering off the robots. The human operator needs to be localised in the
warehouse and the trajectories of the robots have to be modified so that they
do not interfere with the human. The purpose of this paper is to demonstrate a
visual localisation method with visual elements that are already available in
the current warehouse setup.
Familia: An Open-Source Toolkit for Industrial Topic Modeling
Authors: Di Jiang, Zeyu Chen, Rongzhong Lian, Siqi Bao, Chen Li
Date: 2017-07-31 00:00:00
Tasks: Model Selection, Topic Models

Familia is an open-source toolkit for pragmatic topic modeling in industry.
Familia abstracts the utilities of topic modeling in industry as two paradigms:
semantic representation and semantic matching. Efficient implementations of the
two paradigms are made publicly available for the first time. Furthermore, we
provide off-the-shelf topic models trained on large-scale industrial corpora,
including Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word
Embedding (TWE). We further describe typical applications which are
successfully powered by topic modeling, in order to ease the confusions and
difficulties of software engineers during topic model selection and
utilization.
Combining Thesaurus Knowledge and Probabilistic Topic Models
Authors: Natalia Loukachevitch, Michael Nokel, Kirill Ivanov
Date: 2017-07-31 00:00:00
Tasks: Topic Models

In this paper we present the approach of introducing thesaurus knowledge into
probabilistic topic models. The main idea of the approach is based on the
assumption that the frequencies of semantically related words and phrases,
which are met in the same texts, should be enhanced: this action leads to their
larger contribution into topics found in these texts. We have conducted
experiments with several thesauri and found that for improving topic models, it
is useful to utilize domain-specific knowledge. If a general thesaurus, such as
WordNet, is used, the thesaurus-based improvement of topic models can be
achieved with excluding hyponymy relations in combined topic models.
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
Authors: Jay Patravali, Shubham Jain, Sasank Chilamkurthy
Date: 2017-07-31 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we develop a 2D and 3D segmentation pipelines for fully
automated cardiac MR image segmentation using Deep Convolutional Neural
Networks (CNN). Our models are trained end-to-end from scratch using the ACD
Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR
images in End Diastole and End Systole phase. We show that both our
segmentation models achieve near state-of-the-art performance scores in terms
of distance metrics and have convincing accuracy in terms of clinical
parameters. A comparative analysis is provided by introducing a novel dice loss
function and its combination with cross entropy loss. By exploring different
network structures and comprehensive experiments, we discuss several key
insights to obtain optimal model performance, which also is central to the
theme of this challenge.
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
Authors: Taeksoo Kim, Byoungjip Kim, Moonsu Cha, Jiwon Kim
Date: 2017-07-31 00:00:00
Tasks: Attribute

Learning to transfer visual attributes requires supervision dataset.
Corresponding images with varying attribute values with the same identity are
required for learning the transfer function. This largely limits their
applications, because capturing them is often a difficult task. To address the
issue, we propose an unsupervised method to learn to transfer visual attribute.
The proposed method can learn the transfer function without any corresponding
images. Inspecting visualization results from various unsupervised attribute
transfer tasks, we verify the effectiveness of the proposed method.
Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach
Authors: Vishnu Raj, Irene Dias, Thulasi Tholeti, Sheetal Kalyani
Date: 2017-07-31 00:00:00
Tasks: channel selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With the advent of the 5th generation of wireless standards and an increasing
demand for higher throughput, methods to improve the spectral efficiency of
wireless systems have become very important. In the context of cognitive radio,
a substantial increase in throughput is possible if the secondary user can make
smart decisions regarding which channel to sense and when or how often to
sense. Here, we propose an algorithm to not only select a channel for data
transmission but also to predict how long the channel will remain unoccupied so
that the time spent on channel sensing can be minimized. Our algorithm learns
in two stages - a reinforcement learning approach for channel selection and a
Bayesian approach to determine the optimal duration for which sensing can be
skipped. Comparisons with other learning methods are provided through extensive
simulations. We show that the number of sensing is minimized with negligible
increase in primary interference; this implies that lesser energy is spent by
the secondary user in sensing and also higher throughput is achieved by saving
on sensing.
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
Authors: Ronald Clark, Sen Wang, Andrew Markham, Niki Trigoni, Hongkai Wen
Date: 2017-02-21 00:00:00
Proceeding: CVPR 2017 7
Tasks: Autonomous Driving, Indoor Localization

Machine learning techniques, namely convolutional neural networks (CNN) and
regression forests, have recently shown great promise in performing 6-DoF
localization of monocular images. However, in most cases image-sequences,
rather only single images, are readily available. To this extent, none of the
proposed learning-based approaches exploit the valuable constraint of temporal
smoothness, often leading to situations where the per-frame error is larger
than the camera motion. In this paper we propose a recurrent model for
performing 6-DoF localization of video-clips. We find that, even by considering
only short sequences (20 frames), the pose estimates are smoothed and the
localization error can be drastically reduced. Finally, we consider means of
obtaining probabilistic pose estimates from our model. We evaluate our method
on openly-available real-world autonomous driving and indoor localization
datasets.
Evaluating Music Recommender Systems for Groups
Authors: Zsolt Mezei, Carsten Eickhoff
Date: 2017-07-31 00:00:00
Tasks: Benchmarking, Recommendation Systems

Recommendation to groups of users is a challenging and currently only
passingly studied task. Especially the evaluation aspect often appears ad-hoc
and instead of truly evaluating on groups of users, synthesizes groups by
merging individual preferences.
  In this paper, we present a user study, recording the individual and shared
preferences of actual groups of participants, resulting in a robust,
standardized evaluation benchmark. Using this benchmarking dataset, that we
share with the research community, we compare the respective performance of a
wide range of music group recommendation techniques proposed in the
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
Authors: Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Date: 2017-02-13 00:00:00
Tasks: Efficient Exploration

The MAP-Elites algorithm produces a set of high-performing solutions that
vary according to features defined by the user. This technique has the
potential to be a powerful tool for design space exploration, but is limited by
the need for numerous evaluations. The Surrogate-Assisted Illumination
algorithm (SAIL), introduced here, integrates approximative models and
intelligent sampling of the objective function to minimize the number of
evaluations required by MAP-Elites.
  The ability of SAIL to efficiently produce both accurate models and diverse
high performing solutions is illustrated on a 2D airfoil design problem. The
search space is divided into bins, each holding a design with a different
combination of features. In each bin SAIL produces a better performing solution
than MAP-Elites, and requires several orders of magnitude fewer evaluations.
The CMA-ES algorithm was used to produce an optimal design in each bin: with
the same number of evaluations required by CMA-ES to find a near-optimal
solution in a single bin, SAIL finds solutions of similar quality in every bin.
Optimization models of natural communication
Authors: Ramon Ferrer-i-Cancho
Date: 2014-12-08 00:00:00

A family of information theoretic models of communication was introduced more
than a decade ago to explain the origins of Zipf's law for word frequencies.
The family is a based on a combination of two information theoretic principles:
maximization of mutual information between forms and meanings and minimization
of form entropy. The family also sheds light on the origins of three other
patterns: the principle of contrast, a related vocabulary learning bias and the
meaning-frequency law. Here two important components of the family, namely the
information theoretic principles and the energy function that combines them
linearly, are reviewed from the perspective of psycholinguistics, language
learning, information theory and synergetic linguistics. The minimization of
this linear function is linked to the problem of compression of standard
information theory and might be tuned by self-organization.
Capacity limitations of visual search in deep convolutional neural networks
Authors: Endel Poder
Date: 2017-07-31 00:00:00
Tasks: Object Recognition

Deep convolutional neural networks follow roughly the architecture of biological visual systems and have shown a performance comparable to human observers in object recognition tasks. In this study, I tested three pretrained deep neural networks in visual search for simple visual features, and for feature configurations. The results reveal a qualitative difference from human performance. It appears that there is no clear difference between searches for simple features that pop out in experiments with humans, and for feature configurations that exhibit strict capacity limitations in human vision. Both types of stimuli reveal comparable capacity limitations in the neural networks tested here.
GPS Multipath Detection in the Frequency Domain
Authors: Elie Amani, Karim Djouani, Anish Kurien, Jean-Rémi De Boer, Willy Vigneau, Lionel Ries
Date: 2017-07-31 00:00:00

Multipath is among the major sources of errors in precise positioning using
GPS and continues to be extensively studied. Two Fast Fourier Transform
(FFT)-based detectors are presented in this paper as GPS multipath detection
techniques. The detectors are formulated as binary hypothesis tests under the
assumption that the multipath exists for a sufficient time frame that allows
its detection based on the quadrature arm of the coherent Early-minus-Late
discriminator (Q EmL) for a scalar tracking loop (STL) or on the quadrature (Q
EmL) and/or in-phase arm (I EmL) for a vector tracking loop (VTL), using an
observation window of N samples. Performance analysis of the suggested
detectors is done on multipath signal data acquired from the multipath
environment simulator developed by the German Aerospace Centre (DLR) as well as
on multipath data from real GPS signals. Application of the detection tests to
correlator outputs of scalar and vector tracking loops shows that they may be
used to exclude multipath contaminated satellites from the navigation solution.
These detection techniques can be extended to other Global Navigation Satellite
Systems (GNSS) such as GLONASS, Galileo and Beidou.
Low-Resource Neural Headline Generation
Authors: Ottokar Tilk, Tanel Alumäe
Date: 2017-07-31 00:00:00
Proceeding: WS 2017 9
Tasks: All, Headline Generation

Recent neural headline generation models have shown great results, but are
generally trained on very large datasets. We focus our efforts on improving
headline quality on smaller datasets by the means of pretraining. We propose
new methods that enable pre-training all the parameters of the model and
utilize all available text, resulting in improvements by up to 32.4% relative
in perplexity and 2.84 points in ROUGE.
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization
Authors: Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
Date: 2017-06-27 00:00:00
Tasks: Matrix Completion

Trace norm regularization is a widely used approach for learning low rank
matrices. A standard optimization strategy is based on formulating the problem
as one of low rank matrix factorization which, however, leads to a non-convex
problem. In practice this approach works well, and it is often computationally
faster than standard convex solvers such as proximal gradient methods.
Nevertheless, it is not guaranteed to converge to a global optimum, and the
optimization can be trapped at poor stationary points. In this paper we show
that it is possible to characterize all critical points of the non-convex
problem. This allows us to provide an efficient criterion to determine whether
a critical point is also a global minimizer. Our analysis suggests an iterative
meta-algorithm that dynamically expands the parameter space and allows the
optimization to escape any non-global critical point, thereby converging to a
global minimizer. The algorithm can be applied to problems such as matrix
completion or multitask learning, and our analysis holds for any random
initialization of the factor matrices. Finally, we confirm the good performance
of the algorithm on synthetic and real datasets.
A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data
Authors: Sean Maudsley-Barton, Jamie McPheey, Anthony Bukowski, Daniel Leightley, Moi Hoon Yap
Date: 2017-07-27 00:00:00
Tasks: Time Series, Time Series Analysis

The analysis of human motion as a clinical tool can bring many benefits such
as the early detection of disease and the monitoring of recovery, so in turn
helping people to lead independent lives. However, it is currently under used.
Developments in depth cameras, such as Kinect, have opened up the use of motion
analysis in settings such as GP surgeries, care homes and private homes. To
provide an insight into the use of Kinect in the healthcare domain, we present
a review of the current state of the art. We then propose a method that can
represent human motions from time-series data of arbitrary length, as a single
vector. Finally, we demonstrate the utility of this method by extracting a set
of clinically significant features and using them to detect the age related
changes in the motions of a set of 54 individuals, with a high degree of
certainty (F1- score between 0.9 - 1.0). Indicating its potential application
in the detection of a range of age-related motion impairments.
Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range
Authors: M. Zeshan Alam, Bahadir K. Gunturk
Date: 2016-11-15 00:00:00
Tasks: Depth Estimation

Light field imaging involves capturing both angular and spatial distribution
of light; it enables new capabilities, such as post-capture digital refocusing,
camera aperture adjustment, perspective shift, and depth estimation. Micro-lens
array (MLA) based light field cameras provide a cost-effective approach to
light field imaging. There are two main limitations of MLA-based light field
cameras: low spatial resolution and narrow baseline. While low spatial
resolution limits the general purpose use and applicability of light field
cameras, narrow baseline limits the depth estimation range and accuracy. In
this paper, we present a hybrid stereo imaging system that includes a light
field camera and a regular camera. The hybrid system addresses both spatial
resolution and narrow baseline issues of the MLA-based light field cameras
while preserving light field imaging capabilities.