pwc-archive/papers-with-abstracts huggingface dataset:
maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images
Authors: Benjamin Irving
Date: 2016-06-30 00:00:00
Tasks: Clustering

Supervoxel methods such as Simple Linear Iterative Clustering (SLIC) are an
effective technique for partitioning an image or volume into locally similar
regions, and are a common building block for the development of detection,
segmentation and analysis methods. We introduce maskSLIC an extension of SLIC
to create supervoxels within regions-of-interest, and demonstrate,on examples
from 2-dimensions to 4-dimensions, that maskSLIC overcomes issues that affect
SLIC within an irregular mask. We highlight the benefits of this method through
examples, and show that it is able to better represent underlying tumour
subregions and achieves significantly better results than SLIC on the BRATS
2013 brain tumour challenge data (p=0.001) - outperforming SLIC on 18/20 scans.
Finally, we show an application of this method for the analysis of functional
tumour subregions and demonstrate that it is more effective than voxel
clustering.
A Learned Representation For Artistic Style
Authors: Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur
Date: 2016-10-24 00:00:00
Tasks: Diversity

The diversity of painting styles represents a rich visual vocabulary for the
construction of an image. The degree to which one may learn and parsimoniously
capture this visual vocabulary measures our understanding of the higher level
features of paintings, if not images in general. In this work we investigate
the construction of a single, scalable deep network that can parsimoniously
capture the artistic style of a diversity of paintings. We demonstrate that
such a network generalizes across a diversity of artistic styles by reducing a
painting to a point in an embedding space. Importantly, this model permits a
user to explore new painting styles by arbitrarily combining the styles learned
from individual paintings. We hope that this work provides a useful step
towards building rich models of paintings and offers a window on to the
structure of the learned representation of artistic style.
A scikit-based Python environment for performing multi-label classification
Authors: Piotr Szymański, Tomasz Kajdanowicz
Date: 2017-02-05 00:00:00
Tasks: General Classification, Management, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

scikit-multilearn is a Python library for performing multi-label
classification. The library is compatible with the scikit/scipy ecosystem and
uses sparse matrices for all internal operations. It provides native Python
implementations of popular multi-label classification methods alongside a novel
framework for label space partitioning and division. It includes modern
algorithm adaptation methods, network-based label space division approaches,
which extracts label dependency information and multi-label embedding
classifiers. It provides python wrapped access to the extensive multi-label
method stack from Java libraries and makes it possible to extend deep learning
single-label methods for multi-label tasks. The library allows multi-label
stratification and data set management. The implementation is more efficient in
problem transformation than other established libraries, has good test coverage
and follows PEP8. Source code and documentation can be downloaded from
http://scikit.ml and also via pip. The library follows BSD licensing scheme.
Coordinated Online Learning With Applications to Learning User Preferences
Authors: Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause
Date: 2017-02-09 00:00:00
Tasks: Multi-Task Learning

We study an online multi-task learning setting, in which instances of related
tasks arrive sequentially, and are handled by task-specific online learners. We
consider an algorithmic framework to model the relationship of these tasks via
a set of convex constraints. To exploit this relationship, we design a novel
algorithm -- COOL -- for coordinating the individual online learners: Our key
idea is to coordinate their parameters via weighted projections onto a convex
set. By adjusting the rate and accuracy of the projection, the COOL algorithm
allows for a trade-off between the benefit of coordination and the required
computation/communication. We derive regret bounds for our approach and analyze
how they are influenced by these trade-off factors. We apply our results on the
application of learning users' preferences on the Airbnb marketplace with the
goal of incentivizing users to explore under-reviewed apartments.
Minimax Lower Bounds for Ridge Combinations Including Neural Nets
Authors: Jason M. Klusowski, Andrew R. Barron
Date: 2017-02-09 00:00:00

Estimation of functions of $ d $ variables is considered using ridge
combinations of the form $ \textstyle\sum_{k=1}^m c_{1,k}
\phi(\textstyle\sum_{j=1}^d c_{0,j,k}x_j-b_k) $ where the activation function $
\phi $ is a function with bounded value and derivative. These include
single-hidden layer neural networks, polynomials, and sinusoidal models. From a
sample of size $ n $ of possibly noisy values at random sites $ X \in B =
[-1,1]^d $, the minimax mean square error is examined for functions in the
closure of the $ \ell_1 $ hull of ridge functions with activation $ \phi $. It
is shown to be of order $ d/n $ to a fractional power (when $ d $ is of smaller
order than $ n $), and to be of order $ (\log d)/n $ to a fractional power
(when $ d $ is of larger order than $ n $). Dependence on constraints $ v_0 $
and $ v_1 $ on the $ \ell_1 $ norms of inner parameter $ c_0 $ and outer
parameter $ c_1 $, respectively, is also examined. Also, lower and upper bounds
on the fractional power are given. The heart of the analysis is development of
information-theoretic packing numbers for these classes of functions.
Phase Transitions of the Typical Algorithmic Complexity of the Random Satisfiability Problem Studied with Linear Programming
Authors: Hendrik Schawe, Roman Bleim, Alexander K. Hartmann
Date: 2017-02-09 00:00:00

Here we study the NP-complete $K$-SAT problem. Although the worst-case
complexity of NP-complete problems is conjectured to be exponential, there
exist parametrized random ensembles of problems where solutions can typically
be found in polynomial time for suitable ranges of the parameter. In fact,
random $K$-SAT, with $\alpha=M/N $ as control parameter, can be solved quickly
for small enough values of $\alpha$. It shows a phase transition between a
satisfiable phase and an unsatisfiable phase. For branch and bound algorithms,
which operate in the space of feasible Boolean configurations, the empirically
hardest problems are located only close to this phase transition. Here we study
$K$-SAT ($K=3,4$) and the related optimization problem MAX-SAT by a linear
programming approach, which is widely used for practical problems and allows
for polynomial run time. In contrast to branch and bound it operates outside
the space of feasible configurations. On the other hand, finding a solution
within polynomial time is not guaranteed. We investigated several variants like
including artificial objective functions, so called cutting-plane approaches,
and a mapping to the NP-complete vertex-cover problem. We observed several
easy-hard transitions, from where the problems are typically solvable (in
polynomial time) using the given algorithms, respectively, to where they are
not solvable in polynomial time. For the related vertex-cover problem on random
graphs these easy-hard transitions can be identified with structural properties
of the graphs, like percolation transitions. For the present random $K$-SAT
problem we have investigated numerous structural properties also exhibiting
clear transitions, but they appear not be correlated to the here observed
easy-hard transitions. This renders the behaviour of random $K$-SAT more
complex than, e.g., the vertex-cover problem.
Graph Based Relational Features for Collective Classification
Authors: Immanuel Bayer, Uwe Nagel, Steffen Rendle
Date: 2017-02-09 00:00:00
Tasks: Classification, General Classification, Relational Reasoning

Statistical Relational Learning (SRL) methods have shown that classification
accuracy can be improved by integrating relations between samples. Techniques
such as iterative classification or relaxation labeling achieve this by
propagating information between related samples during the inference process.
When only a few samples are labeled and connections between samples are sparse,
collective inference methods have shown large improvements over standard
feature-based ML methods. However, in contrast to feature based ML, collective
inference methods require complex inference procedures and often depend on the
strong assumption of label consistency among related samples. In this paper, we
introduce new relational features for standard ML methods by extracting
information from direct and indirect relations. We show empirically on three
standard benchmark datasets that our relational features yield results
comparable to collective inference methods. Finally we show that our proposal
outperforms these methods when additional information is available.
Attribute-controlled face photo synthesis from simple line drawing
Authors: Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu
Date: 2017-02-09 00:00:00
Tasks: Attribute

Face photo synthesis from simple line drawing is a one-to-many task as simple
line drawing merely contains the contour of human face. Previous exemplar-based
methods are over-dependent on the datasets and are hard to generalize to
complicated natural scenes. Recently, several works utilize deep neural
networks to increase the generalization, but they are still limited in the
controllability of the users. In this paper, we propose a deep generative model
to synthesize face photo from simple line drawing controlled by face attributes
such as hair color and complexion. In order to maximize the controllability of
face attributes, an attribute-disentangled variational auto-encoder (AD-VAE) is
firstly introduced to learn latent representations disentangled with respect to
specified attributes. Then we conduct photo synthesis from simple line drawing
based on AD-VAE. Experiments show that our model can well disentangle the
variations of attributes from other variations of face photos and synthesize
detailed photorealistic face images with desired attributes. Regarding
background and illumination as the style and human face as the content, we can
also synthesize face photos with the target style of a style photo.
Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
Authors: H. Bahadir Sahin, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren, Ozan Sonmez
Date: 2017-02-08 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Text Categorization

Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC)
dataset is a collection of automatically categorized and annotated sentences
obtained from Wikipedia. We constructed large-scale gazetteers by using a graph
crawler algorithm to extract relevant entity and domain information from a
semantic knowledge base, Freebase. The constructed gazetteers contains
approximately 300K entities with thousands of fine-grained entity types under
77 different domains. Since automated processes are prone to ambiguity, we also
introduce two new content specific noise reduction methodologies. Moreover, we
map fine-grained entity types to the equivalent four coarse-grained types:
person, loc, org, misc. Eventually, we construct six different dataset versions
and evaluate the quality of annotations by comparing ground truths from human
annotators. We make these datasets publicly available to support studies on
Turkish named-entity recognition (NER) and text categorization (TC).
L1-regularized Reconstruction Error as Alpha Matte
Authors: Jubin Johnson, Hisham Cholakkal, Deepu Rajan
Date: 2017-02-09 00:00:00
Tasks: Image Matting, Video Matting

Sampling-based alpha matting methods have traditionally followed the
compositing equation to estimate the alpha value at a pixel from a pair of
foreground (F) and background (B) samples. The (F,B) pair that produces the
least reconstruction error is selected, followed by alpha estimation. The
significance of that residual error has been left unexamined. In this letter,
we propose a video matting algorithm that uses L1-regularized reconstruction
error of F and B samples as a measure of the alpha matte. A multi-frame
non-local means framework using coherency sensitive hashing is utilized to
ensure temporal coherency in the video mattes. Qualitative and quantitative
evaluations on a dataset exclusively for video matting demonstrate the
effectiveness of the proposed matting algorithm.
Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data
Authors: Juan F P J Abascal, Manuel Desco, Juan Parra-Robles
Date: 2017-02-09 00:00:00
Tasks: compressed sensing, Diffusion  MRI

Diffusion MRI measurements using hyperpolarized gases are generally acquired
during patient breath hold, which yields a compromise between achievable image
resolution, lung coverage and number of b-values. In this work, we propose a
novel method that accelerates the acquisition of MR diffusion data by
undersampling in both spatial and b-value dimensions, thanks to incorporating
knowledge about the signal decay into the reconstruction (SIDER). SIDER is
compared to total variation (TV) reconstruction by assessing their effect on
both the recovery of ventilation images and estimated mean alveolar dimensions
(MAD). Both methods are assessed by retrospectively undersampling diffusion
datasets of normal volunteers and COPD patients (n=8) for acceleration factors
between x2 and x10. TV led to large errors and artefacts for acceleration
factors equal or larger than x5. SIDER improved TV, presenting lower errors and
histograms of MAD closer to those obtained from fully sampled data for
accelerations factors up to x10. SIDER preserved image quality at all
acceleration factors but images were slightly smoothed and some details were
lost at x10. In conclusion, we have developed and validated a novel compressed
sensing method for lung MRI imaging and achieved high acceleration factors,
which can be used to increase the amount of data acquired during a breath-hold.
This methodology is expected to improve the accuracy of estimated lung
microstructure dimensions and widen the possibilities of studying lung diseases
with MRI.
Mining User/Movie Preferred Features Based on Reviews for Video Recommendation System
Authors: Xuan-Son Vu, Seong-Bae Park
Date: 2017-02-09 00:00:00

In this work, we present an approach for mining user preferences and
recommendation based on reviews. There have been various studies worked on
recommendation problem. However, most of the studies beyond one aspect user
generated- content such as user ratings, user feedback and so on to state user
preferences. There is a prob- lem in one aspect mining is lacking for stating
user preferences. As a demonstration, in collaborative filter recommendation,
we try to figure out the preference trend of crowded users, then use that trend
to predict current user preference. Therefore, there is a gap between real user
preferences and the trend of the crowded people. Additionally, user preferences
can be addressed from mining user reviews since user often comment about
various aspects of products. To solve this problem, we mainly focus on mining
product aspects and user aspects inside user reviews to directly state user
preferences. We also take into account Social Network Analysis for cold-start
item problem. With cold-start user problem, collaborative filter algorithm is
employed in our work. The framework is general enough to be applied to
different recommendation domains. Theoretically, our method would achieve a
significant enhancement.
Challenges in Providing Automatic Affective Feedback in Instant Messaging Applications
Authors: Chieh-Yang Huang, Ting-Hao, Huang, Lun-Wei Ku
Date: 2017-02-09 00:00:00

Instant messaging is one of the major channels of computer mediated
communication. However, humans are known to be very limited in understanding
others' emotions via text-based communication. Aiming on introducing emotion
sensing technologies to instant messaging, we developed EmotionPush, a system
that automatically detects the emotions of the messages end-users received on
Facebook Messenger and provides colored cues on their smartphones accordingly.
We conducted a deployment study with 20 participants during a time span of two
weeks. In this paper, we revealed five challenges, along with examples, that we
observed in our study based on both user's feedback and chat logs, including
(i)the continuum of emotions, (ii)multi-user conversations, (iii)different
dynamics between different users, (iv)misclassification of emotions and
(v)unconventional content. We believe this discussion will benefit the future
exploration of affective computing for instant messaging, and also shed light
on research of conversational emotion sensing.
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
Authors: Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, Yoav Goldberg
Date: 2016-08-15 00:00:00
Tasks: Prediction, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

There is a lot of research interest in encoding variable length sentences
into fixed length vectors, in a way that preserves the sentence meanings. Two
common methods include representations based on averaging word vectors, and
representations based on the hidden states of recurrent neural networks such as
LSTMs. The sentence vectors are used as features for subsequent machine
learning tasks or for pre-training in the context of deep learning. However,
not much is known about the properties that are encoded in these sentence
representations and about the language information they capture. We propose a
framework that facilitates better understanding of the encoded representations.
We define prediction tasks around isolated aspects of sentence structure
(namely sentence length, word content, and word order), and score
representations by the ability to train a classifier to solve each prediction
task when using the representation as input. We demonstrate the potential
contribution of the approach by analyzing different sentence representation
mechanisms. The analysis sheds light on the relative strengths of different
sentence embedding methods with respect to these low level prediction tasks,
and on the effect of the encoded vector's dimensionality on the resulting
representations.
Effective face landmark localization via single deep network
Authors: Zongping Deng, Ke Li, Qijun Zhao, Yi Zhang, Hu Chen
Date: 2017-02-09 00:00:00
Tasks: Data Augmentation, Face Alignment

In this paper, we propose a novel face alignment method using single deep
network (SDN) on existing limited training data. Rather than using a
max-pooling layer followed one convolutional layer in typical convolutional
neural networks (CNN), SDN adopts a stack of 3 layer groups instead. Each group
layer contains two convolutional layers and a max-pooling layer, which can
extract the features hierarchically. Moreover, an effective data augmentation
strategy and corresponding training skills are also proposed to over-come the
lack of training images on COFW and 300-W da-tasets. The experiment results
show that our method outper-forms state-of-the-art methods in both detection
accuracy and speed.
Predicting Privileged Information for Height Estimation
Authors: Nikolaos Sarafianos, Christophoros Nikou, Ioannis A. Kakadiaris
Date: 2017-02-09 00:00:00
Tasks: regression

In this paper, we propose a novel regression-based method for employing
privileged information to estimate the height using human metrology. The actual
values of the anthropometric measurements are difficult to estimate accurately
using state-of-the-art computer vision algorithms. Hence, we use ratios of
anthropometric measurements as features. Since many anthropometric measurements
are not available at test time in real-life scenarios, we employ a learning
using privileged information (LUPI) framework in a regression setup. Instead of
using the LUPI paradigm for regression in its original form (i.e.,
\epsilon-SVR+), we train regression models that predict the privileged
information at test time. The predictions are then used, along with observable
features, to perform height estimation. Once the height is estimated, a mapping
to classes is performed. We demonstrate that the proposed approach can estimate
the height better and faster than the \epsilon-SVR+ algorithm and report
results for different genders and quartiles of humans.
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning
Authors: Rongjie Lai, Jia Li
Date: 2017-02-09 00:00:00
Tasks: Image Inpainting, Image Reconstruction, Image Restoration, Image Super-Resolution, Super-Resolution

Low-rank structures play important role in recent advances of many problems
in image science and data science. As a natural extension of low-rank
structures for data with nonlinear structures, the concept of the
low-dimensional manifold structure has been considered in many data processing
problems. Inspired by this concept, we consider a manifold based low-rank
regularization as a linear approximation of manifold dimension. This
regularization is less restricted than the global low-rank regularization, and
thus enjoy more flexibility to handle data with nonlinear structures. As
applications, we demonstrate the proposed regularization to classical inverse
problems in image sciences and data sciences including image inpainting, image
super-resolution, X-ray computer tomography (CT) image reconstruction and
semi-supervised learning. We conduct intensive numerical experiments in several
image restoration problems and a semi-supervised learning problem of
classifying handwritten digits using the MINST data. Our numerical tests
demonstrate the effectiveness of the proposed methods and illustrate that the
new regularization methods produce outstanding results by comparing with many
existing methods.
Energy Saving Additive Neural Network
Authors: Arman Afrasiyabi, Ozan Yildiz, Baris Nasir, Fatos T. Yarman Vural, A. Enis Cetin
Date: 2017-02-09 00:00:00
Tasks: Efficient Neural Network

In recent years, machine learning techniques based on neural networks for
mobile computing become increasingly popular. Classical multi-layer neural
networks require matrix multiplications at each stage. Multiplication operation
is not an energy efficient operation and consequently it drains the battery of
the mobile device. In this paper, we propose a new energy efficient neural
network with the universal approximation property over space of Lebesgue
integrable functions. This network, called, additive neural network, is very
suitable for mobile computing. The neural structure is based on a novel vector
product definition, called ef-operator, that permits a multiplier-free
implementation. In ef-operation, the "product" of two real numbers is defined
as the sum of their absolute values, with the sign determined by the sign of
the product of the numbers. This "product" is used to construct a vector
product in $R^N$. The vector product induces the $l_1$ norm. The proposed
additive neural network successfully solves the XOR problem. The experiments on
MNIST dataset show that the classification performances of the proposed
additive neural networks are very similar to the corresponding multi-layer
perceptron and convolutional neural networks (LeNet).