pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing multiple instance datasets
Authors: Veronika Cheplygina, David M. J. Tax
Date: 2018-06-21 00:00:00
Tasks: Multiple Instance Learning

In many pattern recognition problems, a single feature vector is not
sufficient to describe an object. In multiple instance learning (MIL), objects
are represented by sets (\emph{bags}) of feature vectors (\emph{instances}).
This requires an adaptation of standard supervised classifiers in order to
train and evaluate on these bags of instances. Like for supervised
classification, several benchmark datasets and numerous classifiers are
available for MIL. When performing a comparison of different MIL classifiers,
it is important to understand the differences of the datasets, used in the
comparison. Seemingly different (based on factors such as dimensionality)
datasets may elicit very similar behaviour in classifiers, and vice versa. This
has implications for what kind of conclusions may be drawn from the comparison
results. We aim to give an overview of the variability of available benchmark
datasets and some popular MIL classifiers. We use a dataset dissimilarity
measure, based on the differences between the ROC-curves obtained by different
classifiers, and embed this dataset dissimilarity matrix into a low-dimensional
space. Our results show that conceptually similar datasets can behave very
differently. We therefore recommend examining such dataset characteristics when
making comparisons between existing and new MIL classifiers.
  The datasets are available via Figshare at \url{https://bit.ly/2K9iTja}.
Instance-Optimality in the Noisy Value-and Comparison-Model --- Accept, Accept, Strong Accept: Which Papers get in?
Authors: Vincent Cohen-Addad, Frederik Mallmann-Trenn, Claire Mathieu
Date: 2018-06-21 00:00:00
Tasks: Recommendation Systems

Motivated by crowdsourced computation, peer-grading, and recommendation
systems, Braverman, Mao and Weinberg [STOC'16] studied the \emph{query} and
\emph{round} complexity of fundamental problems such as finding the maximum
(\textsc{max}), finding all elements above a certain value
(\textsc{threshold-$v$}) or computing the top$-k$ elements (\textsc{Top}-$k$)
in a noisy environment.
  For example, consider the task of selecting papers for a conference. This
task is challenging due the crowdsourcing nature of peer reviews: the results
of reviews are noisy and it is necessary to parallelize the review process as
much as possible. We study the noisy value model and the noisy comparison
model: In the \emph{noisy value model}, a reviewer is asked to evaluate a
single element: "What is the value of paper $i$?" (\eg accept). In the
\emph{noisy comparison model} (introduced in the seminal work of Feige, Peleg,
Raghavan and Upfal [SICOMP'94]) a reviewer is asked to do a pairwise
comparison: "Is paper $i$ better than paper $j$?"
  In this paper, we show optimal worst-case query complexity for the
\textsc{max},\textsc{threshold-$v$} and \textsc{Top}-$k$ problems. For
\textsc{max} and \textsc{Top}-$k$, we obtain optimal worst-case upper and lower
bounds on the round vs query complexity in both models. For
\textsc{threshold}-$v$, we obtain optimal query complexity and nearly-optimal
round complexity, where $k$ is the size of the output) for both models.
  We then go beyond the worst-case and address the question of the importance
of knowledge of the instance by providing, for a large range of parameters,
instance-optimal algorithms with respect to the query complexity. Furthermore,
we show that the value model is strictly easier than the comparison model.
Crowd disagreement about medical images is informative
Authors: Veronika Cheplygina, Josien P. W. Pluim
Date: 2018-06-21 00:00:00
Tasks: Medical Image Analysis

Classifiers for medical image analysis are often trained with a single
consensus label, based on combining labels given by experts or crowds. However,
disagreement between annotators may be informative, and thus removing it may
not be the best strategy. As a proof of concept, we predict whether a skin
lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd
annotations of visual characteristics of that lesion. We compare using the mean
annotations, illustrating consensus, to standard deviations and other
distribution moments, illustrating disagreement. We show that the mean
annotations perform best, but that the disagreement measures are still
informative. We also make the crowd annotations used in this paper available at
\url{https://figshare.com/s/5cbbce14647b66286544}.
A convex method for classification of groups of examples
Authors: Dori Peleg
Date: 2018-06-21 00:00:00
Tasks: Classification, General Classification, global-optimization, image-classification, Image Classification, Multiple Instance Learning, Video Classification

There are many applications where it important to perform well on a set of
examples as opposed to individual examples. For example in image or video
classification the question is does an object appear somewhere in the image or
video while there are several candidates of the object per image or video. In
this context, it is not important what is the performance per candidate.
Instead the performance per group is the ultimate objective.
  For such problems one popular approach assumes weak supervision where labels
exist for the entire group and then multiple instance learning is utilized.
Another approach is to optimize per candidate, assuming each candidate is
labeled, in the belief that this will achieve good performance per group.
  We will show that better results can be achieved if we offer a new
methodology which synthesizes the aforementioned approaches and directly
optimizes for the final optimization objective while consisting of a convex
optimization problem which solves the global optimization problem. The benefit
of grouping examples is demonstrated on an image classification task for
detecting polyps in images from capsule endoscopy of the colon. The algorithm
was designed to efficiently handle hundreds of millions of examples.
Furthermore, modifications to the penalty function of the standard SVM
algorithm, have proven to significantly improve performance in our test case.
Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances
Authors: Jose M. Peña
Date: 2018-06-21 00:00:00

In this paper, we prove that some Gaussian structural equation models with
dependent errors having equal variances are identifiable from their
corresponding Gaussian distributions. Specifically, we prove identifiability
for the Gaussian structural equation models that can be represented as
Andersson-Madigan-Perlman chain graphs (Andersson et al., 2001). These chain
graphs were originally developed to represent independence models. However,
they are also suitable for representing causal models with additive noise
(Pe\~na, 2016. Our result implies then that these causal models can be
identified from observational data alone. Our result generalizes the result by
Peters and B\"uhlmann (2014), who considered independent errors having equal
variances. The suitability of the equal error variances assumption should be
assessed on a per domain basis.
CaloriNet: From silhouettes to calorie estimation in private environments
Authors: Alessandro Masullo, Tilo Burghardt, Dima Damen, Sion Hannuna, Victor Ponce-López, Majid Mirmehdi
Date: 2018-06-21 00:00:00

We propose a novel deep fusion architecture, CaloriNet, for the online
estimation of energy expenditure for free living monitoring in private
environments, where RGB data is discarded and replaced by silhouettes. Our
fused convolutional neural network architecture is trainable end-to-end, to
estimate calorie expenditure, using temporal foreground silhouettes alongside
accelerometer data. The network is trained and cross-validated on a publicly
available dataset, SPHERE_RGBD + Inertial_calorie. Results show
state-of-the-art minimum error on the estimation of energy expenditure
(calories per minute), outperforming alternative, standard and single-modal
techniques.
Robust and Efficient Boosting Method using the Conditional Risk
Authors: Zhi Xiao, Zhe Luo, Bo Zhong, Xin Dang
Date: 2018-06-21 00:00:00

Well-known for its simplicity and effectiveness in classification, AdaBoost,
however, suffers from overfitting when class-conditional distributions have
significant overlap. Moreover, it is very sensitive to noise that appears in
the labels. This article tackles the above limitations simultaneously via
optimizing a modified loss function (i.e., the conditional risk). The proposed
approach has the following two advantages. (1) It is able to directly take into
account label uncertainty with an associated label confidence. (2) It
introduces a "trustworthiness" measure on training samples via the Bayesian
risk rule, and hence the resulting classifier tends to have finite sample
performance that is superior to that of the original AdaBoost when there is a
large overlap between class conditional distributions. Theoretical properties
of the proposed method are investigated. Extensive experimental results using
synthetic data and real-world data sets from UCI machine learning repository
are provided. The empirical study shows the high competitiveness of the
proposed method in predication accuracy and robustness when compared with the
original AdaBoost and several existing robust AdaBoost algorithms.
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
Authors: Antoine Liutkus, Umut Şimşekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter
Date: 2018-06-21 00:00:00

By building upon the recent theory that established the connection between implicit generative modeling (IGM) and optimal transport, in this study, we propose a novel parameter-free algorithm for learning the underlying distributions of complicated datasets and sampling from them. The proposed algorithm is based on a functional optimization problem, which aims at finding a measure that is close to the data distribution as much as possible and also expressive enough for generative modeling purposes. We formulate the problem as a gradient flow in the space of probability measures. The connections between gradient flows and stochastic differential equations let us develop a computationally efficient algorithm for solving the optimization problem. We provide formal theoretical analysis where we prove finite-time error guarantees for the proposed algorithm. To the best of our knowledge, the proposed algorithm is the first nonparametric IGM algorithm with explicit theoretical guarantees. Our experimental results support our theory and show that our algorithm is able to successfully capture the structure of different types of data distributions.
Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk
Authors: Romain Brégier, Frédéric Devernay, Laetitia Leyrit, James Crowley
Date: 2018-06-21 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Pose Estimation

While 3D object detection and pose estimation has been studied for a long
time, its evaluation is not yet completely satisfactory. Indeed, existing
datasets typically consist in numerous acquisitions of only a few scenes
because of the tediousness of pose annotation, and existing evaluation
protocols cannot handle properly objects with symmetries. This work aims at
addressing those two points. We first present automatic techniques to produce
fully annotated RGBD data of many object instances in arbitrary poses, with
which we produce a dataset of thousands of independent scenes of bulk parts
composed of both real and synthetic images. We then propose a consistent
evaluation methodology suitable for any rigid object, regardless of its
symmetries. We illustrate it with two reference object detection and pose
estimation methods on different objects, and show that incorporating symmetry
considerations into pose estimation methods themselves can lead to significant
performance gains. The proposed dataset is available at
http://rbregier.github.io/dataset2017.
Topological Data Analysis Made Easy with the Topology ToolKit
Authors: Guillaume Favelier, Charles Gueunet, Attila Gyulassy, Julien Kitware, Joshua Levine, Jonas Lukasczyk, Daisuke Sakurai, Maxime Soler, Julien Tierny, Will Usher, Qi Wu
Date: 2018-06-21 00:00:00
Tasks: Topological Data Analysis

This tutorial presents topological methods for the analysis and visualization
of scientific data from a user's perspective, with the Topology ToolKit (TTK),
a recently released open-source library for topological data analysis.
Topological methods have gained considerably in popularity and maturity over
the last twenty years and success stories of established methods have been
documented in a wide range of applications (combustion, chemistry,
astrophysics, material sciences, etc.) with both acquired and simulated data,
in both post-hoc and in-situ contexts. While reference textbooks have been
published on the topic, no tutorial at IEEE VIS has covered this area in recent
years, and never at a software level and from a user's point-of-view. This
tutorial fills this gap by providing a beginner's introduction to topological
methods for practitioners, researchers, students, and lecturers. In particular,
instead of focusing on theoretical aspects and algorithmic details, this
tutorial focuses on how topological methods can be useful in practice for
concrete data analysis tasks such as segmentation, feature extraction or
tracking. The tutorial describes in detail how to achieve these tasks with TTK.
First, after an introduction to topological methods and their application in
data analysis, a brief overview of TTK's main entry point for end users, namely
ParaView, will be presented. Second, an overview of TTK's main features will be
given. A running example will be described in detail, showcasing how to access
TTK's features via ParaView, Python, VTK/C++, and C++. Third, hands-on sessions
will concretely show how to use TTK in ParaView for multiple, representative
data analysis tasks. Fourth, the usage of TTK will be presented for developers,
in particular by describing several examples of visualization and data analysis
projects that were built on top of TTK. Finally, some feedback regarding the
usage of TTK as a teaching platform for topological analysis will be given.
Presenters of this tutorial include experts in topological methods, core
authors of TTK as well as active users, coming from academia, labs, or
industry. A large part of the tutorial will be dedicated to hands-on exercises
and a rich material package (including TTK pre-installs in virtual machines,
code, data, demos, video tutorials, etc.) will be provided to the participants.
This tutorial mostly targets students, practitioners and researchers who are
not experts in topological methods but who are interested in using them in
their daily tasks. We also target researchers already familiar to topological
methods and who are interested in using or contributing to TTK.
A New Approach for Resource Scheduling with Deep Reinforcement Learning
Authors: Yufei Ye, Xiaoqin Ren, Jin Wang, Lingxiao Xu, Wenxia Guo, Wenqiang Huang, Wenhong Tian
Date: 2018-06-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

With the rapid development of deep learning, deep reinforcement learning
(DRL) began to appear in the field of resource scheduling in recent years.
Based on the previous research on DRL in the literature, we introduce online
resource scheduling algorithm DeepRM2 and the offline resource scheduling
algorithm DeepRM_Off. Compared with the state-of-the-art DRL algorithm DeepRM
and heuristic algorithms, our proposed algorithms have faster convergence speed
and better scheduling efficiency with regarding to average slowdown time, job
completion time and rewards.
A data-driven model order reduction approach for Stokes flow through random porous media
Authors: Constantin Grigo, Phaedon-Stelios Koutsourelakis
Date: 2018-06-21 00:00:00
Tasks: Uncertainty Quantification

Direct numerical simulation of Stokes flow through an impermeable, rigid body
matrix by finite elements requires meshes fine enough to resolve the pore-size
scale and is thus a computationally expensive task. The cost is significantly
amplified when randomness in the pore microstructure is present and therefore
multiple simulations need to be carried out. It is well known that in the limit
of scale-separation, Stokes flow can be accurately approximated by Darcy's law
with an effective diffusivity field depending on viscosity and the pore-matrix
topology. We propose a fully probabilistic, Darcy-type, reduced-order model
which, based on only a few tens of full-order Stokes model runs, is capable of
learning a map from the fine-scale topology to the effective diffusivity and is
maximally predictive of the fine-scale response. The reduced-order model
learned can significantly accelerate uncertainty quantification tasks as well
as provide quantitative confidence metrics of the predictive estimates
produced.
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection
Authors: Johannes Hellrich, Sven Buechel, Udo Hahn
Date: 2018-06-21 00:00:00
Proceeding: WS 2019 6

To understand historical texts, we must be aware that language -- including
the emotional connotation attached to words -- changes over time. In this
paper, we aim at estimating the emotion which is associated with a given word
in former language stages of English and German. Emotion is represented
following the popular Valence-Arousal-Dominance (VAD) annotation scheme. While
being more expressive than polarity alone, existing word emotion induction
methods are typically not suited for addressing it. To overcome this
limitation, we present adaptations of two popular algorithms to VAD. To measure
their effectiveness in diachronic settings, we present the first gold standard
for historical word emotions, which was created by scholars with proficiency in
the respective language stages and covers both English and German. In contrast
to claims in previous work, our findings indicate that hand-selecting small
sets of seed words with supposedly stable emotional meaning is actually harmful
rather than helpful.
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks
Authors: Moshe Kravchik, Asaf Shabtai
Date: 2018-06-21 00:00:00
Tasks: Anomaly Detection

This paper presents a study on detecting cyberattacks on industrial control
systems (ICS) using unsupervised deep neural networks, specifically,
convolutional neural networks. The study was performed on a SecureWater
Treatment testbed (SWaT) dataset, which represents a scaled-down version of a
real-world industrial water treatment plant. e suggest a method for anomaly
detection based on measuring the statistical deviation of the predicted value
from the observed value.We applied the proposed method by using a variety of
deep neural networks architectures including different variants of
convolutional and recurrent networks. The test dataset from SWaT included 36
different cyberattacks. The proposed method successfully detects the vast
majority of the attacks with a low false positive rate thus improving on
previous works based on this data set. The results of the study show that 1D
convolutional networks can be successfully applied to anomaly detection in
industrial control systems and outperform more complex recurrent networks while
being much smaller and faster to train.
Ensemble p-Laplacian Regularization for Remote Sensing Image Recognition
Authors: Xueqi Ma, Weifeng Liu, Dapeng Tao, Yicong Zhou
Date: 2018-06-21 00:00:00

Recently, manifold regularized semi-supervised learning (MRSSL) received
considerable attention because it successfully exploits the geometry of the
intrinsic data probability distribution including both labeled and unlabeled
samples to leverage the performance of a learning model. As a natural nonlinear
generalization of graph Laplacian, p-Laplacian has been proved having the rich
theoretical foundations to better preserve the local structure. However, it is
difficult to determine the fitting graph p-Lapalcian i.e. the parameter which
is a critical factor for the performance of graph p-Laplacian. Therefore, we
develop an ensemble p-Laplacian regularization (EpLapR) to fully approximate
the intrinsic manifold of the data distribution. EpLapR incorporates multiple
graphs into a regularization term in order to sufficiently explore the
complementation of graph p-Laplacian. Specifically, we construct a fused graph
by introducing an optimization approach to assign suitable weights on different
p-value graphs. And then, we conduct semi-supervised learning framework on the
fused graph. Extensive experiments on UC-Merced data set demonstrate the
effectiveness and efficiency of the proposed method.
Hypergraph p-Laplacian Regularization for Remote Sensing Image Recognition
Authors: Xueqi Ma, Weifeng Liu, Shuying Li, Yicong Zhou
Date: 2018-06-21 00:00:00

It is of great importance to preserve locality and similarity information in
semi-supervised learning (SSL) based applications. Graph based SSL and manifold
regularization based SSL including Laplacian regularization (LapR) and
Hypergraph Laplacian regularization (HLapR) are representative SSL methods and
have achieved prominent performance by exploiting the relationship of sample
distribution. However, it is still a great challenge to exactly explore and
exploit the local structure of the data distribution. In this paper, we present
an effect and effective approximation algorithm of Hypergraph p-Laplacian and
then propose Hypergraph p-Laplacian regularization (HpLapR) to preserve the
geometry of the probability distribution. In particular, p-Laplacian is a
nonlinear generalization of the standard graph Laplacian and Hypergraph is a
generalization of a standard graph. Therefore, the proposed HpLapR provides
more potential to exploiting the local structure preserving. We apply HpLapR to
logistic regression and conduct the implementations for remote sensing image
recognition. We compare the proposed HpLapR to several popular manifold
regularization based SSL methods including LapR, HLapR and HpLapR on UC-Merced
dataset. The experimental results demonstrate the superiority of the proposed
HpLapR.
Lamarckian Evolution of Convolutional Neural Networks
Authors: Jonas Prellberg, Oliver Kramer
Date: 2018-06-21 00:00:00

Convolutional neural networks belong to the most successul image classifiers,
but the adaptation of their network architecture to a particular problem is
computationally expensive. We show that an evolutionary algorithm saves
training time during the network architecture optimization, if learned network
weights are inherited over generations by Lamarckian evolution. Experiments on
typical image datasets show similar or significantly better test accuracies and
improved convergence speeds compared to two different baselines without weight
inheritance. On CIFAR-10 and CIFAR-100 a 75 % improvement in data efficiency is
observed.