pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical Analysis of Stochastic Search Algorithms
Authors: Per Kristian Lehre, Pietro S. Oliveto
Date: 2017-09-04 00:00:00
Tasks: Evolutionary Algorithms

Theoretical analyses of stochastic search algorithms, albeit few, have always
existed since these algorithms became popular. Starting in the nineties a
systematic approach to analyse the performance of stochastic search heuristics
has been put in place. This quickly increasing basis of results allows,
nowadays, the analysis of sophisticated algorithms such as population-based
evolutionary algorithms, ant colony optimisation and artificial immune systems.
Results are available concerning problems from various domains including
classical combinatorial and continuous optimisation, single and multi-objective
optimisation, and noisy and dynamic optimisation. This chapter introduces the
mathematical techniques that are most commonly used in the runtime analysis of
stochastic search heuristics. Careful attention is given to the very popular
artificial fitness levels and drift analyses techniques for which several
variants are presented. To aid the reader's comprehension of the presented
mathematical methods, these are applied to the analysis of simple evolutionary
algorithms for artificial example functions. The chapter is concluded by
providing references to more complex applications and further extensions of the
techniques for the obtainment of advanced results.
Living a discrete life in a continuous world: Reference with distributed representations
Authors: Gemma Boleda, Sebastian Padó, Nghia The Pham, Marco Baroni
Date: 2017-02-06 00:00:00

Reference is a crucial property of language that allows us to connect
linguistic expressions to the world. Modeling it requires handling both
continuous and discrete aspects of meaning. Data-driven models excel at the
former, but struggle with the latter, and the reverse is true for symbolic
models.
  This paper (a) introduces a concrete referential task to test both aspects,
called cross-modal entity tracking; (b) proposes a neural network architecture
that uses external memory to build an entity library inspired in the DRSs of
DRT, with a mechanism to dynamically introduce new referents or add information
to referents that are already in the library.
  Our model shows promise: it beats traditional neural network architectures on
the task. However, it is still outperformed by Memory Networks, another model
with external memory.
Medical Image Analysis using Convolutional Neural Networks: A Review
Authors: Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum, Muhammad Awais, Majdi Alnowami, Muhammad Khurram Khan
Date: 2017-09-04 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Medical Image Analysis, Retrieval

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.
Factorized Bilinear Models for Image Recognition
Authors: Yanghao Li, Naiyan Wang, Jiaying Liu, Xiaodi Hou
Date: 2016-11-17 00:00:00
Proceeding: ICCV 2017 10

Although Deep Convolutional Neural Networks (CNNs) have liberated their power
in various computer vision tasks, the most important components of CNN,
convolutional layers and fully connected layers, are still limited to linear
transformations. In this paper, we propose a novel Factorized Bilinear (FB)
layer to model the pairwise feature interactions by considering the quadratic
terms in the transformations. Compared with existing methods that tried to
incorporate complex non-linearity structures into CNNs, the factorized
parameterization makes our FB layer only require a linear increase of
parameters and affordable computational cost. To further reduce the risk of
overfitting of the FB layer, a specific remedy called DropFactor is devised
during the training process. We also analyze the connection between FB layer
and some existing models, and show FB layer is a generalization to them.
Finally, we validate the effectiveness of FB layer on several widely adopted
datasets including CIFAR-10, CIFAR-100 and ImageNet, and demonstrate superior
results compared with various state-of-the-art deep models.
Learned Watershed: End-to-End Learning of Seeded Segmentation
Authors: Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred Hamprecht
Date: 2017-04-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Segmentation

Learned boundary maps are known to outperform hand- crafted ones as a basis
for the watershed algorithm. We show, for the first time, how to train
watershed computation jointly with boundary map prediction. The estimator for
the merging priorities is cast as a neural network that is con- volutional
(over space) and recurrent (over iterations). The latter allows learning of
complex shape priors. The method gives the best known seeded segmentation
results on the CREMI segmentation challenge.
An automatic water detection approach based on Dempster-Shafer theory for multi spectral images
Authors: Na Li, Arnaud Martin, Rémi Estival
Date: 2017-08-09 00:00:00
Tasks: Land Cover Classification

Detection of surface water in natural environment via multi-spectral imagery
has been widely utilized in many fields, such land cover identification.
However, due to the similarity of the spectra of water bodies, built-up areas,
approaches based on high-resolution satellites sometimes confuse these
features. A popular direction to detect water is spectral index, often
requiring the ground truth to find appropriate thresholds manually. As for
traditional machine learning methods, they identify water merely via
differences of spectra of various land covers, without taking specific
properties of spectral reflection into account. In this paper, we propose an
automatic approach to detect water bodies based on Dempster-Shafer theory,
combining supervised learning with specific property of water in spectral band
in a fully unsupervised context. The benefits of our approach are twofold. On
the one hand, it performs well in mapping principle water bodies, including
little streams and branches. On the other hand, it labels all objects usually
confused with water as `ignorance', including half-dry watery areas, built-up
areas and semi-transparent clouds and shadows. `Ignorance' indicates not only
limitations of the spectral properties of water and supervised learning itself
but insufficiency of information from multi-spectral bands as well, providing
valuable information for further land cover classification.
Neural Distributed Autoassociative Memories: A Survey
Authors: V. I. Gritsenko, D. A. Rachkovskij, A. A. Frolov, R. Gayler, D. Kleyko, E. Osipov
Date: 2017-09-04 00:00:00
Tasks: Retrieval, Survey

Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) for approximate nearest neighbors among vectors of high dimension. The
purpose of this paper is to review models of autoassociative, distributed
memory that can be naturally implemented by neural networks (mainly with local
learning rules and iterative dynamics based on information locally available to
neurons). Scope. The survey is focused mainly on the networks of Hopfield,
Willshaw and Potts, that have connections between pairs of neurons and operate
on sparse binary vectors. We discuss not only autoassociative memory, but also
the generalization properties of these networks. We also consider neural
networks with higher-order connections and networks with a bipartite graph
structure for non-binary data with linear constraints. Conclusions. In
conclusion we discuss the relations to similarity search, advantages and
drawbacks of these techniques, and topics for further research. An interesting
and still not completely resolved question is whether neural autoassociative
memories can search for approximate nearest neighbors faster than other index
structures for similarity search, in particular for the case of very high
dimensional vectors.
Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction
Authors: Andre S. Yoon, Taehoon Lee, Yongsub Lim, Deokwoo Jung, Philgyun Kang, Dongwon Kim, Keuntae Park, Yongjin Choi
Date: 2017-09-04 00:00:00

This work presents a novel semi-supervised learning approach for data-driven
modeling of asset failures when health status is only partially known in
historical data. We combine a generative model parameterized by deep neural
networks with non-linear embedding technique. It allows us to build prognostic
models with the limited amount of health status information for the precise
prediction of future asset reliability. The proposed method is evaluated on a
publicly available dataset for remaining useful life (RUL) estimation, which
shows significant improvement even when a fraction of the data with known
health status is as sparse as 1% of the total. Our study suggests that the
non-linear embedding based on a deep generative model can efficiently
regularize a complex model with deep architectures while achieving high
prediction accuracy that is far less sensitive to the availability of health
status information.
Extending the scope of the small-ball method
Authors: Shahar Mendelson
Date: 2017-09-04 00:00:00

The small-ball method was introduced as a way of obtaining a high probability, isomorphic lower bound on the quadratic empirical process, under weak assumptions on the indexing class. The key assumption was that class members satisfy a uniform small-ball estimate: that $Pr(|f| \geq \kappa\|f\|_{L_2}) \geq \delta$ for given constants $\kappa$ and $\delta$. Here we extend the small-ball method and obtain a high probability, almost-isometric (rather than isomorphic) lower bound on the quadratic empirical process. The scope of the result is considerably wider than the small-ball method: there is no need for class members to satisfy a uniform small-ball condition, and moreover, motivated by the notion of tournament learning procedures, the result is stable under a `majority vote'.
Maintaining Ad-Hoc Communication Network in Area Protection Scenarios with Adversarial Agents
Authors: Marika Ivanová, Pavel Surynek, Diep Thi Ngoc Nguyen
Date: 2017-09-04 00:00:00

We address a problem of area protection in graph-based scenarios with
multiple mobile agents where connectivity is maintained among agents to ensure
they can communicate. The problem consists of two adversarial teams of agents
that move in an undirected graph shared by both teams. Agents are placed in
vertices of the graph; at most one agent can occupy a vertex; and they can move
into adjacent vertices in a conflict free way. Teams have asymmetric goals: the
aim of one team - attackers - is to invade into given area while the aim of the
opponent team - defenders - is to protect the area from being entered by
attackers by occupying selected vertices. The team of defenders need to
maintain connectivity of vertices occupied by its own agents in a visibility
graph. The visibility graph models possibility of communication between pairs
of vertices.
  We study strategies for allocating vertices to be occupied by the team of
defenders to block attacking agents where connectivity is maintained at the
same time. To do this we reserve a subset of defending agents that do not try
to block the attackers but instead are placed to support connectivity of the
team. The performance of strategies is tested in multiple benchmarks. The
success of a strategy is heavily dependent on the type of the instance, and so
one of the contributions of this work is that we identify suitable strategies
for diverse instance types.
Hyperspectral Light Field Stereo Matching
Authors: Kang Zhu, Yujia Xue, Qiang Fu, Sing Bing Kang, Xilin Chen, Jingyi Yu
Date: 2017-09-04 00:00:00
Tasks: Disparity Estimation, Stereo Matching, Stereo Matching Hand

In this paper, we describe how scene depth can be extracted using a
hyperspectral light field capture (H-LF) system. Our H-LF system consists of a
5 x 6 array of cameras, with each camera sampling a different narrow band in
the visible spectrum. There are two parts to extracting scene depth. The first
part is our novel cross-spectral pairwise matching technique, which involves a
new spectral-invariant feature descriptor and its companion matching metric we
call bidirectional weighted normalized cross correlation (BWNCC). The second
part, namely, H-LF stereo matching, uses a combination of spectral-dependent
correspondence and defocus cues that rely on BWNCC. These two new cost terms
are integrated into a Markov Random Field (MRF) for disparity estimation.
Experiments on synthetic and real H-LF data show that our approach can produce
high-quality disparity maps. We also show that these results can be used to
produce the complete plenoptic cube in addition to synthesizing all-focus and
defocused color images under different sensor spectral responses.
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings
Authors: Nishant Gurnani
Date: 2017-09-04 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Translation, Two-sample testing, Word Embeddings

We introduce the cross-match test - an exact, distribution free,
high-dimensional hypothesis test as an intrinsic evaluation metric for word
embeddings. We show that cross-match is an effective means of measuring
distributional similarity between different vector representations and of
evaluating the statistical significance of different vector embedding models.
Additionally, we find that cross-match can be used to provide a quantitative
measure of linguistic similarity for selecting bridge languages for machine
translation. We demonstrate that the results of the hypothesis test align with
our expectations and note that the framework of two sample hypothesis testing
is not limited to word embeddings and can be extended to all vector
representations.
From Review to Rating: Exploring Dependency Measures for Text Classification
Authors: Samuel Cunningham-Nelson, Mahsa Baktashmotlagh, Wageeh Boles
Date: 2017-09-04 00:00:00
Tasks: Classification, feature selection, General Classification, Sentiment Analysis, text-classification, Text Classification

Various text analysis techniques exist, which attempt to uncover unstructured
information from text. In this work, we explore using statistical dependence
measures for textual classification, representing text as word vectors. Student
satisfaction scores on a 3-point scale and their free text comments written
about university subjects are used as the dataset. We have compared two textual
representations: a frequency word representation and term frequency
relationship to word vectors, and found that word vectors provide a greater
accuracy. However, these word vectors have a large number of features which
aggravates the burden of computational complexity. Thus, we explored using a
non-linear dependency measure for feature selection by maximizing the
dependence between the text reviews and corresponding scores. Our quantitative
and qualitative analysis on a student satisfaction dataset shows that our
approach achieves comparable accuracy to the full feature vector, while being
an order of magnitude faster in testing. These text analysis and feature
reduction techniques can be used for other textual data applications such as
sentiment analysis.
Non-rigid image registration using fully convolutional networks with deep self-supervision
Authors: Hongming Li, Yong Fan
Date: 2017-09-04 00:00:00
Tasks: Image Registration

We propose a novel non-rigid image registration algorithm that is built upon
fully convolutional networks (FCNs) to optimize and learn spatial
transformations between pairs of images to be registered. Different from most
existing deep learning based image registration methods that learn spatial
transformations from training data with known corresponding spatial
transformations, our method directly estimates spatial transformations between
pairs of images by maximizing an image-wise similarity metric between fixed and
deformed moving images, similar to conventional image registration algorithms.
At the same time, our method also learns FCNs for encoding the spatial
transformations at the same spatial resolution of images to be registered,
rather than learning coarse-grained spatial transformation information. The
image registration is implemented in a multi-resolution image registration
framework to jointly optimize and learn spatial transformations and FCNs at
different resolutions with deep self-supervision through typical feedforward
and backpropagation computation. Since our method simultaneously optimizes and
learns spatial transformations for the image registration, our method can be
directly used to register a pair of images, and the registration of a set of
images is also a training procedure for FCNs so that the trained FCNs can be
directly adopted to register new images by feedforward computation of the
learned FCNs without any optimization. The proposed method has been evaluated
for registering 3D structural brain magnetic resonance (MR) images and obtained
better performance than state-of-the-art image registration algorithms.
Salient Object Detection: A Survey
Authors: Ali Borji, Ming-Ming Cheng, Qibin Hou, Huaizu Jiang, Jia Li
Date: 2014-11-18 00:00:00
Tasks: Object, object-detection, Object Detection, Object Proposal Generation, RGB Salient Object Detection, Salient Object Detection, Scene Segmentation, Survey

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.
Lensless-camera based machine learning for image classification
Authors: Ganghun Kim, Stefan Kapetanovic, Rachael Palmer, Rajesh Menon
Date: 2017-09-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Decision Making, General Classification, image-classification, Image Classification

Machine learning (ML) has been widely applied to image classification. Here,
we extend this application to data generated by a camera comprised of only a
standard CMOS image sensor with no lens. We first created a database of
lensless images of handwritten digits. Then, we trained a ML algorithm on this
dataset. Finally, we demonstrated that the trained ML algorithm is able to
classify the digits with accuracy as high as 99% for 2 digits. Our approach
clearly demonstrates the potential for non-human cameras in machine-based
decision-making scenarios.
Estimation of interventional effects of features on prediction
Authors: Patrick Blöbaum, Shohei Shimizu
Date: 2017-09-03 00:00:00
Tasks: Prediction

The interpretability of prediction mechanisms with respect to the underlying
prediction problem is often unclear. While several studies have focused on
developing prediction models with meaningful parameters, the causal
relationships between the predictors and the actual prediction have not been
considered. Here, we connect the underlying causal structure of a data
generation process and the causal structure of a prediction mechanism. To
achieve this, we propose a framework that identifies the feature with the
greatest causal influence on the prediction and estimates the necessary causal
intervention of a feature such that a desired prediction is obtained. The
general concept of the framework has no restrictions regarding data linearity;
however, we focus on an implementation for linear data here. The framework
applicability is evaluated using artificial data and demonstrated using
real-world data.
CSI: A Hybrid Deep Model for Fake News Detection
Authors: Natali Ruchansky, Sungyong Seo, Yan Liu
Date: 2017-03-20 00:00:00
Tasks: Articles, Fake News Detection, Misinformation

The topic of fake news has drawn attention both from the public and the
academic communities. Such misinformation has the potential of affecting public
opinion, providing an opportunity for malicious parties to manipulate the
outcomes of public events such as elections. Because such high stakes are at
play, automatically detecting fake news is an important, yet challenging
problem that is not yet well understood. Nevertheless, there are three
generally agreed upon characteristics of fake news: the text of an article, the
user response it receives, and the source users promoting it. Existing work has
largely focused on tailoring solutions to one particular characteristic which
has limited their success and generality. In this work, we propose a model that
combines all three characteristics for a more accurate and automated
prediction. Specifically, we incorporate the behavior of both parties, users
and articles, and the group behavior of users who propagate fake news.
Motivated by the three characteristics, we propose a model called CSI which is
composed of three modules: Capture, Score, and Integrate. The first module is
based on the response and text; it uses a Recurrent Neural Network to capture
the temporal pattern of user activity on a given article. The second module
learns the source characteristic based on the behavior of users, and the two
are integrated with the third module to classify an article as fake or not.
Experimental analysis on real-world data demonstrates that CSI achieves higher
accuracy than existing models, and extracts meaningful latent representations
of both users and articles.