pwc-archive/papers-with-abstracts huggingface dataset:
On the choice of the low-dimensional domain for global optimization via random embeddings
Authors: Mickaël Binois, David Ginsbourger, Olivier Roustant
Date: 2017-04-18 00:00:00
Tasks: Bayesian Optimization, global-optimization

The challenge of taking many variables into account in optimization problems
may be overcome under the hypothesis of low effective dimensionality. Then, the
search of solutions can be reduced to the random embedding of a low dimensional
space into the original one, resulting in a more manageable optimization
problem. Specifically, in the case of time consuming black-box functions and
when the budget of evaluations is severely limited, global optimization with
random embeddings appears as a sound alternative to random search. Yet, in the
case of box constraints on the native variables, defining suitable bounds on a
low dimensional domain appears to be complex. Indeed, a small search domain
does not guarantee to find a solution even under restrictive hypotheses about
the function, while a larger one may slow down convergence dramatically. Here
we tackle the issue of low-dimensional domain selection based on a detailed
study of the properties of the random embedding, giving insight on the
aforementioned difficulties. In particular, we describe a minimal
low-dimensional set in correspondence with the embedded search space. We
additionally show that an alternative equivalent embedding procedure yields
simultaneously a simpler definition of the low-dimensional minimal set and
better properties in practice. Finally, the performance and robustness gains of
the proposed enhancements for Bayesian optimization are illustrated on
numerical examples.
Unsupervised Learning by Predicting Noise
Authors: Piotr Bojanowski, Armand Joulin
Date: 2017-04-18 00:00:00
Proceeding: ICML 2017 8

Convolutional neural networks provide visual features that perform remarkably
well in many computer vision applications. However, training these networks
requires significant amounts of supervision. This paper introduces a generic
framework to train deep networks, end-to-end, with no supervision. We propose
to fix a set of target representations, called Noise As Targets (NAT), and to
constrain the deep features to align to them. This domain agnostic approach
avoids the standard unsupervised learning issues of trivial solutions and
collapsing of features. Thanks to a stochastic batch reassignment strategy and
a separable square loss function, it scales to millions of images. The proposed
approach produces representations that perform on par with state-of-the-art
unsupervised methods on ImageNet and Pascal VOC.
Semantic Similarity from Natural Language and Ontology Analysis
Authors: Sébastien Harispe, Sylvie Ranwez, Stefan Janaqi, Jacky Montmain
Date: 2017-04-18 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Artificial Intelligence federates numerous scientific fields in the aim of
developing machines able to assist human operators performing complex
treatments -- most of which demand high cognitive skills (e.g. learning or
decision processes). Central to this quest is to give machines the ability to
estimate the likeness or similarity between things in the way human beings
estimate the similarity between stimuli.
  In this context, this book focuses on semantic measures: approaches designed
for comparing semantic entities such as units of language, e.g. words,
sentences, or concepts and instances defined into knowledge bases. The aim of
these measures is to assess the similarity or relatedness of such semantic
entities by taking into account their semantics, i.e. their meaning --
intuitively, the words tea and coffee, which both refer to stimulating
beverage, will be estimated to be more semantically similar than the words
toffee (confection) and coffee, despite that the last pair has a higher
syntactic similarity. The two state-of-the-art approaches for estimating and
quantifying semantic similarities/relatedness of semantic entities are
presented in detail: the first one relies on corpora analysis and is based on
Natural Language Processing techniques and semantic models while the second is
based on more or less formal, computer-readable and workable forms of knowledge
such as semantic networks, thesaurus or ontologies. (...) Beyond a simple
inventory and categorization of existing measures, the aim of this monograph is
to convey novices as well as researchers of these domains towards a better
understanding of semantic similarity estimation and more generally semantic
measures.
Template Matching with Deformable Diversity Similarity
Authors: Itamar Talmi, Roey Mechrez, Lihi Zelnik-Manor
Date: 2016-12-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Template Matching

We propose a novel measure for template matching named Deformable Diversity
Similarity -- based on the diversity of feature matches between a target image
window and the template. We rely on both local appearance and geometric
information that jointly lead to a powerful approach for matching. Our key
contribution is a similarity measure, that is robust to complex deformations,
significant background clutter, and occlusions. Empirical evaluation on the
most up-to-date benchmark shows that our method outperforms the current
state-of-the-art in its detection accuracy while improving computational
complexity.
Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models
Authors: Yannis Papanikolaou, Grigorios Tsoumakas, Manos Laliotis, Nikos Markantonatos, Ioannis Vlahavas
Date: 2017-04-18 00:00:00
Tasks: Articles, BIG-bench Machine Learning, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble method that incorporates a McNemar statistical
significance test in order to validate the combination of the constituent
machine learning algorithms. Some secondary contributions include a study on
the temporal aspects of the BioASQ corpus (observations apply also to the
BioASQ's super-set, the PubMed articles collection) and the proper adaptation
of the algorithms used to deal with this challenging classification task.
Results: The ensemble method we developed is compared to other approaches in
experimental scenarios with subsets of the BioASQ corpus giving positive
results. During the BioASQ 2014 challenge we obtained the first place during
the first batch and the third in the two following batches. Our success in the
BioASQ challenge proved that a fully automated machine-learning approach, which
does not implement any heuristics and rule-based approaches, can be highly
competitive and outperform other approaches in similar challenging contexts.
A Comment on "Analysis of Video Image Sequences Using Point and Line Correspondences"
Authors: Mieczysław A. Kłopotek
Date: 2017-04-18 00:00:00

In this paper we would like to deny the results of Wang et al. raising two
fundamental claims:
  * A line does not contribute anything to recognition of motion parameters
from two images
  * Four traceable points are not sufficient to recover motion parameters from
two perspective
  To be constructive, however, we show that four traceable points are
sufficient to recover motion parameters from two frames under orthogonal
projection and that five points are sufficient to simplify the solution of the
two-frame problem under orthogonal projection to solving a linear equation
system.
Multi-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization
Authors: Shekoufeh Gorgi Zadeh, Stephan Didas, Maximilian W. M. Wintergerst, Thomas Schultz
Date: 2016-11-21 00:00:00
Tasks: Vessel Detection

Ridge and valley enhancing filters are widely used in applications such as
vessel detection in medical image computing. When images are degraded by noise
or include vessels at different scales, such filters are an essential step for
meaningful and stable vessel localization. In this work, we propose a novel
multi-scale anisotropic fourth-order diffusion equation that allows us to
smooth along vessels, while sharpening them in the orthogonal direction. The
proposed filter uses a fourth order diffusion tensor whose eigentensors and
eigenvalues are determined from the local Hessian matrix, at a scale that is
automatically selected for each pixel. We discuss efficient implementation
using a Fast Explicit Diffusion scheme and demonstrate results on synthetic
images and vessels in fundus images. Compared to previous isotropic and
anisotropic fourth-order filters, as well as established second-order vessel
enhancing filters, our newly proposed one better restores the centerlines in
all cases.
Simple Problems: The Simplicial Gluing Structure of Pareto Sets and Pareto Fronts
Authors: Naoki Hamada
Date: 2017-04-18 00:00:00

Quite a few studies on real-world applications of multi-objective
optimization reported that their Pareto sets and Pareto fronts form a
topological simplex. Such a class of problems was recently named the simple
problems, and their Pareto set and Pareto front were observed to have a gluing
structure similar to the faces of a simplex. This paper gives a theoretical
justification for that observation by proving the gluing structure of the
Pareto sets/fronts of subproblems of a simple problem. The simplicity of
standard benchmark problems is studied.
Hot or not? Forecasting cellular network hot spots using sector performance indicators
Authors: Joan Serrà, Ilias Leontiadis, Alexandros Karatzoglou, Konstantina Papagiannaki
Date: 2017-04-18 00:00:00

To manage and maintain large-scale cellular networks, operators need to know
which sectors underperform at any given time. For this purpose, they use the
so-called hot spot score, which is the result of a combination of multiple
network measurements and reflects the instantaneous overall performance of
individual sectors. While operators have a good understanding of the current
performance of a network and its overall trend, forecasting the performance of
each sector over time is a challenging task, as it is affected by both regular
and non-regular events, triggered by human behavior and hardware failures. In
this paper, we study the spatio-temporal patterns of the hot spot score and
uncover its regularities. Based on our observations, we then explore the
possibility to use recent measurements' history to predict future hot spots. To
this end, we consider tree-based machine learning models, and study their
performance as a function of time, amount of past data, and prediction horizon.
Our results indicate that, compared to the best baseline, tree-based models can
deliver up to 14% better forecasts for regular hot spots and 153% better
forecasts for non-regular hot spots. The latter brings strong evidence that,
for moderate horizons, forecasts can be made even for sectors exhibiting
isolated, non-regular behavior. Overall, our work provides insight into the
dynamics of cellular sectors and their predictability. It also paves the way
for more proactive network operations with greater forecasting horizons.
Image Fusion With Cosparse Analysis Operator
Authors: Rui Gao, Sergiy A. Vorobyov, Hong Zhao
Date: 2017-04-18 00:00:00
Tasks: Multi Focus Image Fusion, Operator learning

The paper addresses the image fusion problem, where multiple images captured
with different focus distances are to be combined into a higher quality
all-in-focus image. Most current approaches for image fusion strongly rely on
the unrealistic noise-free assumption used during the image acquisition, and
then yield limited robustness in fusion processing. In our approach, we
formulate the multi-focus image fusion problem in terms of an analysis sparse
model, and simultaneously perform the restoration and fusion of multi-focus
images. Based on this model, we propose an analysis operator learning, and
define a novel fusion function to generate an all-in-focus image. Experimental
evaluations confirm the effectiveness of the proposed fusion approach both
visually and quantitatively, and show that our approach outperforms
state-of-the-art fusion methods.
Fast 2-D Complex Gabor Filter with Kernel Decomposition
Authors: Suhyuk Um, Jaeyoon Kim, Dongbo Min
Date: 2017-04-18 00:00:00

2-D complex Gabor filtering has found numerous applications in the fields of
computer vision and image processing. Especially, in some applications, it is
often needed to compute 2-D complex Gabor filter bank consisting of the 2-D
complex Gabor filtering outputs at multiple orientations and frequencies.
Although several approaches for fast 2-D complex Gabor filtering have been
proposed, they primarily focus on reducing the runtime of performing the 2-D
complex Gabor filtering once at specific orientation and frequency. To obtain
the 2-D complex Gabor filter bank output, existing methods are repeatedly
applied with respect to multiple orientations and frequencies. In this paper,
we propose a novel approach that efficiently computes the 2-D complex Gabor
filter bank by reducing the computational redundancy that arises when
performing the Gabor filtering at multiple orientations and frequencies. The
proposed method first decomposes the Gabor basis kernels to allow a fast
convolution with the Gaussian kernel in a separable manner. This enables
reducing the runtime of the 2-D complex Gabor filter bank by reusing
intermediate results of the 2-D complex Gabor filtering computed at a specific
orientation. Furthermore, we extend this idea into 2-D localized sliding
discrete Fourier transform (SDFT) using the Gaussian kernel in the DFT
computation, which lends a spatial localization ability as in the 2-D complex
Gabor filter. Experimental results demonstrate that our method runs faster than
state-of-the-arts methods for fast 2-D complex Gabor filtering, while
maintaining similar filtering quality.
Know Your Master: Driver Profiling-based Anti-theft Method
Authors: Byung Il Kwak, JiYoung Woo, Huy Kang Kim
Date: 2017-04-18 00:00:00
Tasks: Driver Identification, feature selection

Although many anti-theft technologies are implemented, auto-theft is still
increasing. Also, security vulnerabilities of cars can be used for auto-theft
by neutralizing anti-theft system. This keyless auto-theft attack will be
increased as cars adopt computerized electronic devices more. To detect
auto-theft efficiently, we propose the driver verification method that analyzes
driving patterns using measurements from the sensor in the vehicle. In our
model, we add mechanical features of automotive parts that are excluded in
previous works, but can be differentiated by drivers' driving behaviors. We
design the model that uses significant features through feature selection to
reduce the time cost of feature processing and improve the detection
performance. Further, we enrich the feature set by deriving statistical
features such as mean, median, and standard deviation. This minimizes the
effect of fluctuation of feature values per driver and finally generates the
reliable model. We also analyze the effect of the size of sliding window on
performance to detect the time point when the detection becomes reliable and to
inform owners the theft event as soon as possible. We apply our model with real
driving and show the contribution of our work to the literature of driver
identification.
Neural Question Generation from Text: A Preliminary Study
Authors: Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, Ming Zhou
Date: 2017-04-06 00:00:00
Tasks: Decoder, Position, Question Generation, Question-Generation, Sentence

Automatic question generation aims to generate questions from a text passage
where the generated questions can be answered by certain sub-spans of the given
passage. Traditional methods mainly use rigid heuristic rules to transform a
sentence into related questions. In this work, we propose to apply the neural
encoder-decoder model to generate meaningful and diverse questions from natural
language sentences. The encoder reads the input text and the answer position,
to produce an answer-aware input representation, which is fed to the decoder to
generate an answer focused question. We conduct a preliminary study on neural
question generation from text with the SQuAD dataset, and the experiment
results show that our method can produce fluent and diverse questions.
HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source
Authors: Shixiang Wan, Quan Zou
Date: 2017-04-18 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Predicting the subcellular localization of proteins is an important and
challenging problem. Traditional experimental approaches are often expensive
and time-consuming. Consequently, a growing number of research efforts employ a
series of machine learning approaches to predict the subcellular location of
proteins. There are two main challenges among the state-of-the-art prediction
methods. First, most of the existing techniques are designed to deal with
multi-class rather than multi-label classification, which ignores connections
between multiple labels. In reality, multiple locations of particular proteins
implies that there are vital and unique biological significances that deserve
special focus and cannot be ignored. Second, techniques for handling imbalanced
data in multi-label classification problems are necessary, but never employed.
For solving these two issues, we have developed an ensemble multi-label
classifier called HPSLPred, which can be applied for multi-label classification
with an imbalanced protein source. For convenience, a user-friendly webserver
has been established at http://server.malab.cn/HPSLPred.
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
Authors: Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, Zhe Wang
Date: 2017-04-18 00:00:00
Tasks: Click-Through Rate Prediction, Feature Engineering

CTR prediction in real-world business is a difficult machine learning problem
with large scale nonlinear sparse data. In this paper, we introduce an
industrial strength solution with model named Large Scale Piece-wise Linear
Model (LS-PLM). We formulate the learning problem with $L_1$ and $L_{2,1}$
regularizers, leading to a non-convex and non-smooth optimization problem.
Then, we propose a novel algorithm to solve it efficiently, based on
directional derivatives and quasi-Newton method. In addition, we design a
distributed system which can run on hundreds of machines parallel and provides
us with the industrial scalability. LS-PLM model can capture nonlinear patterns
from massive sparse data, saving us from heavy feature engineering jobs. Since
2012, LS-PLM has become the main CTR prediction model in Alibaba's online
display advertising system, serving hundreds of millions users every day.
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks
Authors: Nazrul Haque, N. Dinesh Reddy, K. Madhava Krishna
Date: 2017-04-18 00:00:00
Tasks: Motion Segmentation, Optical Flow Estimation, Scene Understanding, Segmentation

Dynamic scene understanding is a challenging problem and motion segmentation
plays a crucial role in solving it. Incorporating semantics and motion enhances
the overall perception of the dynamic scene. For applications of outdoor
robotic navigation, joint learning methods have not been extensively used for
extracting spatio-temporal features or adding different priors into the
formulation. The task becomes even more challenging without stereo information
being incorporated. This paper proposes an approach to fuse semantic features
and motion clues using CNNs, to address the problem of monocular semantic
motion segmentation. We deduce semantic and motion labels by integrating
optical flow as a constraint with semantic features into dilated convolution
network. The pipeline consists of three main stages i.e Feature extraction,
Feature amplification and Multi Scale Context Aggregation to fuse the semantics
and flow features. Our joint formulation shows significant improvements in
monocular motion segmentation over the state of the art methods on challenging
KITTI tracking dataset.
On 3D Face Reconstruction via Cascaded Regression in Shape Space
Authors: Feng Liu, Dan Zeng, Jing Li, Qijun Zhao
Date: 2015-09-21 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, regression

Cascaded regression has been recently applied to reconstructing 3D faces from
single 2D images directly in shape space, and achieved state-of-the-art
performance. This paper investigates thoroughly such cascaded regression based
3D face reconstruction approaches from four perspectives that are not well
studied yet: (i) The impact of the number of 2D landmarks; (ii) the impact of
the number of 3D vertices; (iii) the way of using standalone automated landmark
detection methods; and (iv) the convergence property. To answer these
questions, a simplified cascaded regression based 3D face reconstruction method
is devised, which can be integrated with standalone automated landmark
detection methods and reconstruct 3D face shapes that have the same pose and
expression as the input face images, rather than normalized pose and
expression. Moreover, an effective training method is proposed by disturbing
the automatically detected landmarks. Comprehensive evaluation experiments have
been done with comparison to other 3D face reconstruction methods. The results
not only deepen the understanding of cascaded regression based 3D face
reconstruction approaches, but also prove the effectiveness of proposed method.
LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques
Authors: Joao Paulo Papa, Gustavo Henrique Rosa, Douglas Rodrigues, Xin-She Yang
Date: 2017-04-18 00:00:00
Tasks: Benchmarking

Optimization techniques play an important role in several scientific and
real-world applications, thus becoming of great interest for the community. As
a consequence, a number of open-source libraries are available in the
literature, which ends up fostering the research and development of new
techniques and applications. In this work, we present a new library for the
implementation and fast prototyping of nature-inspired techniques called
LibOPT. Currently, the library implements 15 techniques and 112 benchmarking
functions, as well as it also supports 11 hypercomplex-based optimization
approaches, which makes it one of the first of its kind. We showed how one can
easily use and also implement new techniques in LibOPT under the C paradigm.
Examples are provided with samples of source-code using benchmarking functions.
Video Object Segmentation using Supervoxel-Based Gerrymandering
Authors: Brent A. Griffin, Jason J. Corso
Date: 2017-04-18 00:00:00
Tasks: Object, Semantic Segmentation, Superpixels, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation, Video Understanding

Pixels operate locally. Superpixels have some potential to collect
information across many pixels; supervoxels have more potential by implicitly
operating across time. In this paper, we explore this well established notion
thoroughly analyzing how supervoxels can be used in place of and in conjunction
with other means of aggregating information across space-time. Focusing on the
problem of strictly unsupervised video object segmentation, we devise a method
called supervoxel gerrymandering that links masks of foregroundness and
backgroundness via local and non-local consensus measures. We pose and answer a
series of critical questions about the ability of supervoxels to adequately
sway local voting; the questions regard type and scale of supervoxels as well
as local versus non-local consensus, and the questions are posed in a general
way so as to impact the broader knowledge of the use of supervoxels in video
understanding. We work with the DAVIS dataset and find that our analysis yields
an unsupervised method that outperforms all other known unsupervised methods
and even many supervised ones.