pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Avoid Errors in GANs by Manipulating Input Spaces
Authors: Alexander B. Jung
Date: 2017-07-03 00:00:00
Tasks: Diversity

Despite recent advances, large scale visual artifacts are still a common
occurrence in images generated by GANs. Previous work has focused on improving
the generator's capability to accurately imitate the data distribution
$p_{data}$. In this paper, we instead explore methods that enable GANs to
actively avoid errors by manipulating the input space. The core idea is to
apply small changes to each noise vector in order to shift them away from areas
in the input space that tend to result in errors. We derive three different
architectures from that idea. The main one of these consists of a simple
residual module that leads to significantly less visual artifacts, while only
slightly decreasing diversity. The module is trivial to add to existing GANs
and costs almost zero computation and memory.
Appearance invariance in convolutional networks with neighborhood similarity
Authors: Tolga Tasdizen, Mehdi Sajjadi, Mehran Javanmardi, Nisha Ramesh
Date: 2017-07-03 00:00:00
Tasks: Cell Detection

We present a neighborhood similarity layer (NSL) which induces appearance
invariance in a network when used in conjunction with convolutional layers. We
are motivated by the observation that, even though convolutional networks have
low generalization error, their generalization capability does not extend to
samples which are not represented by the training data. For instance, while
novel appearances of learned concepts pose no problem for the human visual
system, feedforward convolutional networks are generally not successful in such
situations. Motivated by the Gestalt principle of grouping with respect to
similarity, the proposed NSL transforms its input feature map using the feature
vectors at each pixel as a frame of reference, i.e. center of attention, for
its surrounding neighborhood. This transformation is spatially varying, hence
not a convolution. It is differentiable; therefore, networks including the
proposed layer can be trained in an end-to-end manner. We analyze the
invariance of NSL to significant changes in appearance that are not represented
in the training data. We also demonstrate its advantages for digit recognition,
semantic labeling and cell detection problems.
struc2vec: Learning Node Representations from Structural Identity
Authors: Leonardo F. R. Ribeiro, Pedro H. P. Saverese, Daniel R. Figueiredo
Date: 2017-04-11 00:00:00
Tasks: Graph Embedding, Network Embedding, Node Classification

Implementation and experiments  of graph embedding algorithms.deep walk,LINE(Large-scale Information Network Embedding),node2vec,SDNE(Structural Deep Network Embedding),struc2vec
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
Authors: Dhanesh Ramachandram, Michal Lisicki, Timothy J. Shields, Mohamed R. Amer, Graham W. Taylor
Date: 2017-07-03 00:00:00
Tasks: Activity Recognition, Bayesian Optimization, Deep Learning, Human Activity Recognition

A popular testbed for deep learning has been multimodal recognition of human
activity or gesture involving diverse inputs such as video, audio, skeletal
pose and depth images. Deep learning architectures have excelled on such
problems due to their ability to combine modality representations at different
levels of nonlinear feature extraction. However, designing an optimal
architecture in which to fuse such learned representations has largely been a
non-trivial human engineering effort. We treat fusion structure optimization as
a hyper-parameter search and cast it as a discrete optimization problem under
the Bayesian optimization framework. We propose a novel graph-induced kernel to
compute structural similarities in the search space of tree-structured
multimodal architectures and demonstrate its effectiveness using two
challenging multimodal human activity recognition datasets.
Lens Distortion Rectification using Triangulation based Interpolation
Authors: Burak Benligiray, Cihan Topal
Date: 2016-11-29 00:00:00
Tasks: Camera Calibration

Nonlinear lens distortion rectification is a common first step in image
processing applications where the assumption of a linear camera model is
essential. For rectifying the lens distortion, forward distortion model needs
to be known. However, many self-calibration methods estimate the inverse
distortion model. In the literature, the inverse of the estimated model is
approximated for image rectification, which introduces additional error to the
system. We propose a novel distortion rectification method that uses the
inverse distortion model directly. The method starts by mapping the distorted
pixels to the rectified image using the inverse distortion model. The resulting
set of points with subpixel locations are triangulated. The pixel values of the
rectified image are linearly interpolated based on this triangulation. The
method is applicable to all camera calibration methods that estimate the
inverse distortion model and performs well across a large range of parameters.
Partially Occluded Leaf Recognition via Subgraph Matching and Energy Optimization
Authors: Ayan Chaudhury, John L. Barron
Date: 2017-04-28 00:00:00

We present an approach to match partially occluded plant leaves with
databases of full plant leaves. Although contour based 2D shape matching has
been studied extensively in the last couple of decades, matching occluded
leaves with full leaf databases is an open and little worked on problem.
Classifying occluded plant leaves is even more challenging than full leaf
matching because of large variations and complexity of leaf structures.
Matching an occluded contour with all the full contours in a database is an
NP-hard problem [Su et al. ICCV2015], so our algorithm is necessarily
suboptimal.
Greedy Search for Descriptive Spatial Face Features
Authors: Caner Gacav, Burak Benligiray, Cihan Topal
Date: 2017-01-07 00:00:00
Tasks: Descriptive, Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expression recognition methods use a combination of geometric and
appearance-based features. Spatial features are derived from displacements of
facial landmarks, and carry geometric information. These features are either
selected based on prior knowledge, or dimension-reduced from a large pool. In
this study, we produce a large number of potential spatial features using two
combinations of facial landmarks. Among these, we search for a descriptive
subset of features using sequential forward selection. The chosen feature
subset is used to classify facial expressions in the extended Cohn-Kanade
dataset (CK+), and delivered 88.7% recognition accuracy without using any
appearance-based features.
Multi-rank Sparse Hierarchical Clustering
Authors: Hongyang Zhang, Ruben H. Zamar
Date: 2014-09-02 00:00:00
Tasks: Clustering, feature selection

There has been a surge in the number of large and flat data sets - data sets
containing a large number of features and a relatively small number of
observations - due to the growing ability to collect and store information in
medical research and other fields. Hierarchical clustering is a widely used
clustering tool. In hierarchical clustering, large and flat data sets may allow
for a better coverage of clustering features (features that help explain the
true underlying clusters) but, such data sets usually include a large fraction
of noise features (non-clustering features) that may hide the underlying
clusters. Witten and Tibshirani (2010) proposed a sparse hierarchical
clustering framework to cluster the observations using an adaptively chosen
subset of the features, however, we show that this framework has some
limitations when the data sets contain clustering features with complex
structure. In this paper, we propose the Multi-rank sparse hierarchical
clustering (MrSHC). We show that, using simulation studies and real data
examples, MrSHC produces superior feature selection and clustering performance
comparing to the classical (of-the-shelf) hierarchical clustering and the
existing sparse hierarchical clustering framework.
Regression Phalanxes
Authors: Hongyang Zhang, William J. Welch, Ruben H. Zamar
Date: 2017-07-03 00:00:00
Tasks: Clustering, Drug Discovery, General Classification, Prediction, regression

Tomal et al. (2015) introduced the notion of "phalanxes" in the context of
rare-class detection in two-class classification problems. A phalanx is a
subset of features that work well for classification tasks. In this paper, we
propose a different class of phalanxes for application in regression settings.
We define a "Regression Phalanx" - a subset of features that work well together
for prediction. We propose a novel algorithm which automatically chooses
Regression Phalanxes from high-dimensional data sets using hierarchical
clustering and builds a prediction model for each phalanx for further
ensembling. Through extensive simulation studies and several real-life
applications in various areas (including drug discovery, chemical analysis of
spectra data, microarray analysis and climate projections) we show that an
ensemble of Regression Phalanxes improves prediction accuracy when combined
with effective prediction methods like Lasso or Random Forests.
Modeling preference time in middle distance triathlons
Authors: Iztok Fister, Andres Iglesias, Suash Deb, Dušan Fister, Iztok Fister Jr
Date: 2017-07-03 00:00:00

Modeling preference time in triathlons means predicting the intermediate
times of particular sports disciplines by a given overall finish time in a
specific triathlon course for the athlete with the known personal best result.
This is a hard task for athletes and sport trainers due to a lot of different
factors that need to be taken into account, e.g., athlete's abilities, health,
mental preparations and even their current sports form. So far, this process
was calculated manually without any specific software tools or using the
artificial intelligence. This paper presents the new solution for modeling
preference time in middle distance triathlons based on particle swarm
optimization algorithm and archive of existing sports results. Initial results
are presented, which suggest the usefulness of proposed approach, while remarks
for future improvements and use are also emphasized.
Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks
Authors: Emmanuel Dufourq, Bruce A. Bassett
Date: 2017-07-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, regression, Sentiment Analysis

Regression or classification? This is perhaps the most basic question faced
when tackling a new supervised learning problem. We present an Evolutionary
Deep Learning (EDL) algorithm that automatically solves this by identifying the
question type with high accuracy, along with a proposed deep architecture.
Typically, a significant amount of human insight and preparation is required
prior to executing machine learning algorithms. For example, when creating deep
neural networks, the number of parameters must be selected in advance and
furthermore, a lot of these choices are made based upon pre-existing knowledge
of the data such as the use of a categorical cross entropy loss function.
Humans are able to study a dataset and decide whether it represents a
classification or a regression problem, and consequently make decisions which
will be applied to the execution of the neural network. We propose the
Automated Problem Identification (API) algorithm, which uses an evolutionary
algorithm interface to TensorFlow to manipulate a deep neural network to decide
if a dataset represents a classification or a regression problem. We test API
on 16 different classification, regression and sentiment analysis datasets with
up to 10,000 features and up to 17,000 unique target values. API achieves an
average accuracy of $96.3\%$ in identifying the problem type without hardcoding
any insights about the general characteristics of regression or classification
problems. For example, API successfully identifies classification problems even
with 1000 target values. Furthermore, the algorithm recommends which loss
function to use and also recommends a neural network architecture. Our work is
therefore a step towards fully automated machine learning.
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
Authors: Weilin Huang, Christopher P. Bridge, J. Alison Noble, Andrew Zisserman
Date: 2017-07-03 00:00:00

We present an automatic method to describe clinically useful information
about scanning, and to guide image interpretation in ultrasound (US) videos of
the fetal heart. Our method is able to jointly predict the visibility, viewing
plane, location and orientation of the fetal heart at the frame level. The
contributions of the paper are three-fold: (i) a convolutional neural network
architecture is developed for a multi-task prediction, which is computed by
sliding a 3x3 window spatially through convolutional maps. (ii) an anchor
mechanism and Intersection over Union (IoU) loss are applied for improving
localization accuracy. (iii) a recurrent architecture is designed to
recursively compute regional convolutional features temporally over sequential
frames, allowing each prediction to be conditioned on the whole video. This
results in a spatial-temporal model that precisely describes detailed heart
parameters in challenging US videos. We report results on a real-world clinical
dataset, where our method achieves performance on par with expert annotations.
Rank Determination for Low-Rank Data Completion
Authors: Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal
Date: 2017-07-03 00:00:00

Recently, fundamental conditions on the sampling patterns have been obtained
for finite completability of low-rank matrices or tensors given the
corresponding ranks. In this paper, we consider the scenario where the rank is
not given and we aim to approximate the unknown rank based on the location of
sampled entries and some given completion. We consider a number of data models,
including single-view matrix, multi-view matrix, CP tensor, tensor-train tensor
and Tucker tensor. For each of these data models, we provide an upper bound on
the rank when an arbitrary low-rank completion is given. We characterize these
bounds both deterministically, i.e., with probability one given that the
sampling pattern satisfies certain combinatorial properties, and
probabilistically, i.e., with high probability given that the sampling
probability is above some threshold. Moreover, for both single-view matrix and
CP tensor, we are able to show that the obtained upper bound is exactly equal
to the unknown rank if the lowest-rank completion is given. Furthermore, we
provide numerical experiments for the case of single-view matrix, where we use
nuclear norm minimization to find a low-rank completion of the sampled data and
we observe that in most of the cases the proposed upper bound on the rank is
equal to the true rank.
Including Dialects and Language Varieties in Author Profiling
Authors: Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi, Liviu P. Dinu
Date: 2017-07-03 00:00:00
Tasks: Author Profiling

This paper presents a computational approach to author profiling taking
gender and language variety into account. We apply an ensemble system with the
output of multiple linear SVM classifiers trained on character and word
$n$-grams. We evaluate the system using the dataset provided by the organizers
of the 2017 PAN lab on author profiling. Our approach achieved 75% average
accuracy on gender identification on tweets written in four languages and 97%
accuracy on language variety identification for Portuguese.
Geometric calibration of Colour and Stereo Surface Imaging System of ESA's Trace Gas Orbiter
Authors: Stepan Tulyakov, Anton Ivanov, Nicolas Thomas, Victoria Roloff, Antoine Pommerol, Gabriele Cremonese, Thomas Weigel, Francois Fleuret
Date: 2017-07-03 00:00:00

There are many geometric calibration methods for "standard" cameras. These
methods, however, cannot be used for the calibration of telescopes with large
focal lengths and complex off-axis optics. Moreover, specialized calibration
methods for the telescopes are scarce in literature. We describe the
calibration method that we developed for the Colour and Stereo Surface Imaging
System (CaSSIS) telescope, on board of the ExoMars Trace Gas Orbiter (TGO).
Although our method is described in the context of CaSSIS, with camera-specific
experiments, it is general and can be applied to other telescopes. We further
encourage re-use of the proposed method by making our calibration code and data
available on-line.
Pattern representation and recognition with accelerated analog neuromorphic systems
Authors: Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn, David Stöckel, Anna Schroeder, Guillaume Bellec, Johannes Bill, Oliver Breitwieser, Ilja Bytschok, Andreas Grübl, Maurice Güttler, Andreas Hartel, Stephan Hartmann, Dan Husmann, Kai Husmann, Sebastian Jeltsch, Vitali Karasenko, Mitja Kleider, Christoph Koke, Alexander Kononov, Christian Mauch, Eric Müller, Paul Müller, Johannes Partzsch, Thomas Pfeil, Stefan Schiefer, Stefan Scholze, Anand Subramoney, Vasilis Thanasoulis, Bernhard Vogginger, Robert Legenstein, Wolfgang Maass, René Schüffny, Christian Mayr, Johannes Schemmel, Karlheinz Meier
Date: 2017-03-17 00:00:00

Despite being originally inspired by the central nervous system, artificial
neural networks have diverged from their biological archetypes as they have
been remodeled to fit particular tasks. In this paper, we review several
possibilites to reverse map these architectures to biologically more realistic
spiking networks with the aim of emulating them on fast, low-power neuromorphic
hardware. Since many of these devices employ analog components, which cannot be
perfectly controlled, finding ways to compensate for the resulting effects
represents a key challenge. Here, we discuss three different strategies to
address this problem: the addition of auxiliary network components for
stabilizing activity, the utilization of inherently robust architectures and a
training method for hardware-emulated networks that functions without perfect
knowledge of the system's dynamics and parameters. For all three scenarios, we
corroborate our theoretical considerations with experimental results on
accelerated analog neuromorphic platforms.
An In-Depth Analysis of Visual Tracking with Siamese Neural Networks
Authors: Roman Pflugfelder
Date: 2017-07-03 00:00:00
Tasks: Visual Tracking

This survey presents a deep analysis of the learning and inference
capabilities in nine popular trackers. It is neither intended to study the
whole literature nor is it an attempt to review all kinds of neural networks
proposed for visual tracking. We focus instead on Siamese neural networks which
are a promising starting point for studying the challenging problem of
tracking. These networks integrate efficiently feature learning and the
temporal matching and have so far shown state-of-the-art performance. In
particular, the branches of Siamese networks, their layers connecting these
branches, specific aspects of training and the embedding of these networks into
the tracker are highlighted. Quantitative results from existing papers are
compared with the conclusion that the current evaluation methodology shows
problems with the reproducibility and the comparability of results. The paper
proposes a novel Lisp-like formalism for a better comparison of trackers. This
assumes a certain functional design and functional decomposition of trackers.
The paper tries to give foundation for tracker design by a formulation of the
problem based on the theory of machine learning and by the interpretation of a
tracker as a decision function. The work concludes with promising lines of
research and suggests future work.
Efficient Eye Typing with 9-direction Gaze Estimation
Authors: Chi Zhang, Rui Yao, Jinpeng Cai
Date: 2017-07-03 00:00:00
Tasks: Gaze Estimation

Vision based text entry systems aim to help disabled people achieve text
communication using eye movement. Most previous methods have employed an
existing eye tracker to predict gaze direction and design an input method based
upon that. However, these methods can result in eye tracking quality becoming
easily affected by various factors and lengthy amounts of time for calibration.
Our paper presents a novel efficient gaze based text input method, which has
the advantage of low cost and robustness. Users can type in words by looking at
an on-screen keyboard and blinking. Rather than estimate gaze angles directly
to track eyes, we introduce a method that divides the human gaze into nine
directions. This method can effectively improve the accuracy of making a
selection by gaze and blinks. We build a Convolutional Neural Network (CNN)
model for 9-direction gaze estimation. On the basis of the 9-direction gaze, we
use a nine-key T9 input method which is widely used in candy bar phones. Bar
phones were very popular in the world decades ago and have cultivated strong
user habits and language models. To train a robust gaze estimator, we created a
large-scale dataset with images of eyes sourced from 25 people. According to
the results from our experiments, our CNN model is able to accurately estimate
different people's gaze under various lighting conditions by different devices.
In considering disable people's needs, we removed the complex calibration
process. The input methods can run in screen mode and portable off-screen mode.
Moreover, The datasets used in our experiments are made available to the
community to allow further experimentation.
Empirical entropy, minimax regret and minimax risk
Authors: Alexander Rakhlin, Karthik Sridharan, Alexandre B. Tsybakov
Date: 2013-08-06 00:00:00
Tasks: Math, regression

We consider the random design regression model with square loss. We propose a
method that aggregates empirical minimizers (ERM) over appropriately chosen
random subsets and reduces to ERM in the extreme case, and we establish sharp
oracle inequalities for its risk. We show that, under the $\varepsilon^{-p}$
growth of the empirical $\varepsilon$-entropy, the excess risk of the proposed
method attains the rate $n^{-2/(2+p)}$ for $p\in(0,2)$ and $n^{-1/p}$ for $p>2$
where $n$ is the sample size. Furthermore, for $p\in(0,2)$, the excess risk
rate matches the behavior of the minimax risk of function estimation in
regression problems under the well-specified model. This yields a conclusion
that the rates of statistical estimation in well-specified models (minimax
risk) and in misspecified models (minimax regret) are equivalent in the regime
$p\in(0,2)$. In other words, for $p\in(0,2)$ the problem of statistical
learning enjoys the same minimax rate as the problem of statistical estimation.
On the contrary, for $p>2$ we show that the rates of the minimax regret are, in
general, slower than for the minimax risk. Our oracle inequalities also imply
the $v\log(n/v)/n$ rates for Vapnik-Chervonenkis type classes of dimension $v$
without the usual convexity assumption on the class; we show that these rates
are optimal. Finally, for a slightly modified method, we derive a bound on the
excess risk of $s$-sparse convex aggregation improving that of Lounici [Math.
Methods Statist. 16 (2007) 246-259] and providing the optimal rate.