pwc-archive/papers-with-abstracts huggingface dataset:
Generating 3D Adversarial Point Clouds
Authors: Chong Xiang, Charles R. Qi, Bo Li
Date: 2018-09-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Shape Classification, Autonomous Driving

Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examples for 2D images and CNNs have been extensively studied, less attention has been paid to 3D data such as point clouds. Given many safety-critical 3D applications such as autonomous driving, it is important to study how adversarial point clouds could affect current deep 3D models. In this work, we propose several novel algorithms to craft adversarial point clouds against PointNet, a widely used deep neural network for point cloud processing. Our algorithms work in two ways: adversarial point perturbation and adversarial point generation. For point perturbation, we shift existing points negligibly. For point generation, we generate either a set of independent and scattered points or a small number (1-3) of point clusters with meaningful shapes such as balls and airplanes which could be hidden in the human psyche. In addition, we formulate six perturbation measurement metrics tailored to the attacks in point clouds and conduct extensive experiments to evaluate the proposed algorithms on the ModelNet40 3D shape classification dataset. Overall, our attack algorithms achieve a success rate higher than 99% for all targeted attacks
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error
Authors: Nontawat Charoenphakdee, Masashi Sugiyama
Date: 2018-09-19 00:00:00
Tasks: Binary Classification, Classification, Density Ratio Estimation, General Classification

Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfilled in practice. In this paper, we generalize PU classification to the class prior shift and asymmetric error scenarios. Based on the analysis of the Bayes optimal classifier, we show that given a test class prior, PU classification under class prior shift is equivalent to PU classification with asymmetric error. Then, we propose two different frameworks to handle these problems, namely, a risk minimization framework and density ratio estimation framework. Finally, we demonstrate the effectiveness of the proposed frameworks and compare both frameworks through experiments using benchmark datasets.
Light Field Neural Network
Authors: Yuchi Huo, Sung-Eui Yoon
Date: 2018-09-19 00:00:00

We introduce an optical neural network system made by off-the-shelf
components. In order to test the evaluate the physical property of the proposed
system, we are making a prototype. After further discussions with our
cooperators, we are agreed that the prototype implementation may take longer
time than we expected earlier. Therefore we reach a consensus on withdrawing
the paper until the physical data is available.
Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from Mixed-Variable Data: Method and Applications
Authors: Andrew Skabar
Date: 2018-09-19 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Clustering, Imputation, Outlier Detection

The ability to estimate joint, conditional and marginal probability
distributions over some set of variables is of great utility for many common
machine learning tasks. However, estimating these distributions can be
challenging, particularly in the case of data containing a mix of discrete and
continuous variables. This paper presents a non-parametric method for
estimating these distributions directly from a dataset. The data are first
represented as a graph consisting of object nodes and attribute value nodes.
Depending on the distribution to be estimated, an appropriate eigenvector
equation is then constructed. This equation is then solved to find the
corresponding stationary distribution of the graph, from which the required
distributions can then be estimated and sampled from. The paper demonstrates
how the method can be applied to many common machine learning tasks including
classification, regression, missing value imputation, outlier detection, random
vector generation, and clustering.
Leveraging Contact Forces for Learning to Grasp
Authors: Hamza Merzic, Miroslav Bogdanovic, Daniel Kappler, Ludovic Righetti, Jeannette Bohg
Date: 2018-09-19 00:00:00
Tasks: Deep Reinforcement Learning, Object, Reinforcement Learning

Grasping objects under uncertainty remains an open problem in robotics
research. This uncertainty is often due to noisy or partial observations of the
object pose or shape. To enable a robot to react appropriately to unforeseen
effects, it is crucial that it continuously takes sensor feedback into account.
While visual feedback is important for inferring a grasp pose and reaching for
an object, contact feedback offers valuable information during manipulation and
grasp acquisition. In this paper, we use model-free deep reinforcement learning
to synthesize control policies that exploit contact sensing to generate robust
grasping under uncertainty. We demonstrate our approach on a multi-fingered
hand that exhibits more complex finger coordination than the commonly used
two-fingered grippers. We conduct extensive experiments in order to assess the
performance of the learned policies, with and without contact sensing. While it
is possible to learn grasping policies without contact sensing, our results
suggest that contact feedback allows for a significant improvement of grasping
robustness under object pose uncertainty and for objects with a complex shape.
Interpretable Reinforcement Learning with Ensemble Methods
Authors: Alexander Brown, Marek Petrik
Date: 2018-09-19 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose to use boosted regression trees as a way to compute
human-interpretable solutions to reinforcement learning problems. Boosting
combines several regression trees to improve their accuracy without
significantly reducing their inherent interpretability. Prior work has focused
independently on reinforcement learning and on interpretable machine learning,
but there has been little progress in interpretable reinforcement learning. Our
experimental results show that boosted regression trees compute solutions that
are both interpretable and match the quality of leading reinforcement learning
methods.
Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions
Authors: Rima Arnaout, Lara Curran, Erin Chinn, Yili Zhao, Anita Moon-Grady
Date: 2018-09-19 00:00:00
Tasks: Binary Classification, Diagnostic, Sensitivity, Specificity

Prenatal diagnosis of tetralogy of Fallot (TOF) and hypoplastic left heart
syndrome (HLHS), two serious congenital heart defects, improves outcomes and
can in some cases facilitate in utero interventions. In practice, however, the
fetal diagnosis rate for these lesions is only 30-50 percent in community
settings. Improving fetal diagnosis of congenital heart disease is therefore
critical. Deep learning is a cutting-edge machine learning technique for
finding patterns in images but has not yet been applied to prenatal diagnosis
of congenital heart disease. Using 685 retrospectively collected
echocardiograms from fetuses 18-24 weeks of gestational age from 2000-2018, we
trained convolutional and fully-convolutional deep learning models in a
supervised manner to (i) identify the five canonical screening views of the
fetal heart and (ii) segment cardiac structures to calculate fetal cardiac
biometrics. We then trained models to distinguish by view between normal
hearts, TOF, and HLHS. In a holdout test set of images, F-score for
identification of the five most important fetal cardiac views was 0.95. Binary
classification of unannotated cardiac views of normal heart vs. TOF reached an
overall sensitivity of 75% and a specificity of 76%, while normal vs. HLHS
reached a sensitivity of 100% and specificity of 90%, both well above average
diagnostic rates for these lesions. Furthermore, segmentation-based
measurements for cardiothoracic ratio (CTR), cardiac axis (CA), and ventricular
fractional area change (FAC) were compatible with clinically measured metrics
for normal, TOF, and HLHS hearts. Thus, using guideline-recommended imaging,
deep learning models can significantly improve detection of fetal congenital
heart disease compared to the common standard of care.
Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise
Authors: Fayeem Aziz, Aaron S. W. Wong, James S. Welsh, Stephan K. Chalup
Date: 2018-09-19 00:00:00

This study presents the results of a series of simulation experiments that
evaluate and compare four different manifold alignment methods under the
influence of noise. The data was created by simulating the dynamics of two
slightly different double pendulums in three-dimensional space. The method of
semi-supervised feature-level manifold alignment using global distance resulted
in the most convincing visualisations. However, the semi-supervised
feature-level local alignment methods resulted in smaller alignment errors.
These local alignment methods were also more robust to noise and faster than
the other methods.
Data-Driven Clustering via Parameterized Lloyd's Families
Authors: Maria-Florina Balcan, Travis Dick, Colin White
Date: 2018-09-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering

Algorithms for clustering points in metric spaces is a long-studied area of research. Clustering has seen a multitude of work both theoretically, in understanding the approximation guarantees possible for many objective functions such as k-median and k-means clustering, and experimentally, in finding the fastest algorithms and seeding procedures for Lloyd's algorithm. The performance of a given clustering algorithm depends on the specific application at hand, and this may not be known up front. For example, a "typical instance" may vary depending on the application, and different clustering heuristics perform differently depending on the instance. In this paper, we define an infinite family of algorithms generalizing Lloyd's algorithm, with one parameter controlling the initialization procedure, and another parameter controlling the local search procedure. This family of algorithms includes the celebrated k-means++ algorithm, as well as the classic farthest-first traversal algorithm. We design efficient learning algorithms which receive samples from an application-specific distribution over clustering instances and learn a near-optimal clustering algorithm from the class. We show the best parameters vary significantly across datasets such as MNIST, CIFAR, and mixtures of Gaussians. Our learned algorithms never perform worse than k-means++, and on some datasets we see significant improvements.
Wearable-based Mediation State Detection in Individuals with Parkinson's Disease
Authors: Murtadha D. Hssayeni, Michelle A. Burack, Joohi Jimenez-Shahed, M. D., Behnaz Ghoraani, Ph. D
Date: 2018-09-19 00:00:00
Tasks: Specificity

One of the most prevalent complaints of individuals with mid-stage and
advanced Parkinson's disease (PD) is the fluctuating response to their
medication (i.e., ON state with maximum benefit from medication and OFF state
with no benefit from medication). In order to address these motor fluctuations,
the patients go through periodic clinical examination where the treating
physician reviews the patients' self-report about duration in different
medication states and optimize therapy accordingly. Unfortunately, the
patients' self-report can be unreliable and suffer from recall bias. There is a
need to a technology-based system that can provide objective measures about the
duration in different medication states that can be used by the treating
physician to successfully adjust the therapy. In this paper, we developed a
medication state detection algorithm to detect medication states using two
wearable motion sensors. A series of significant features are extracted from
the motion data and used in a classifier that is based on a support vector
machine with fuzzy labeling. The developed algorithm is evaluated using a
dataset with 19 PD subjects and a total duration of 1,052.24 minutes (17.54
hours). The algorithm resulted in an average classification accuracy of 90.5%,
sensitivity of 94.2%, and specificity of 85.4%.
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Authors: Shuochao Yao, Yiran Zhao, Huajie Shao, Shengzhong Liu, Dongxin Liu, Lu Su, Tarek Abdelzaher
Date: 2018-09-19 00:00:00

Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by $48\%$ to
$78\%$ and energy consumption by $37\%$ to $69\%$ compared with the
state-of-the-art compression algorithms.
A Study on Deep Learning Based Sauvegrain Method for Measurement of Puberty Bone Age
Authors: Seung Bin Baik, Keum Gang Cha
Date: 2018-09-18 00:00:00
Tasks: Age Estimation, Deep Learning

This study applies a technique to expand the number of images to a level that
allows deep learning. And the applicability of the Sauvegrain method through
deep learning with relatively few elbow X-rays is studied. The study was
composed of processes similar to the physicians' bone age assessment
procedures. The selected reference images were learned without being included
in the evaluation data, and at the same time, the data was extended to
accommodate the number of cases. In addition, we adjusted the X-ray images to
better images using U-Net and selected the ROI with RPN + so as to be able to
perform bone age estimation through CNN. The mean absolute error of the
Sauvegrain method based on deep learning is 2.8 months and the Mean Absolute
Percentage Error (MAPE) is 0.018. This result shows that X - ray analysis using
the Sauvegrain method shows higher accuracy than that of the age group of
puberty even in the deep learning base. This means that deep learning of the
Suvegrain method can be measured at a level similar to that of an expert, based
on the extended X-ray image with the image data extension technique. Finally,
we applied the Sauvegrain method to deep learning for accurate measurement of
bone age at puberty. As a result, the present study is based on deep learning,
and compared with the evaluation results of experts, it is possible to overcome
limitations of the method of measuring bone age based on machine learning which
was in TW3 or Greulich & Pyle due to lack of X- I confirmed the fact. And we
also presented the Sauvegrain method, which is applicable to adolescents as
well.
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
Authors: Yichong Xu, Xiaodong Liu, Yelong Shen, Jingjing Liu, Jianfeng Gao
Date: 2018-09-18 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Reading Comprehension, Machine Translation, Multi-Task Learning, Question Answering, Reading Comprehension, Translation

We propose a multi-task learning framework to learn a joint Machine Reading
Comprehension (MRC) model that can be applied to a wide range of MRC tasks in
different domains. Inspired by recent ideas of data selection in machine
translation, we develop a novel sample re-weighting scheme to assign
sample-specific weights to the loss. Empirical study shows that our approach
can be applied to many existing MRC models. Combined with contextual
representations from pre-trained language models (such as ELMo), we achieve new
state-of-the-art results on a set of MRC benchmark datasets. We release our
code at https://github.com/xycforgithub/MultiTask-MRC.
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
Authors: Dominic Richards, Patrick Rebeschini
Date: 2018-09-18 00:00:00

We propose graph-dependent implicit regularisation strategies for distributed
stochastic subgradient descent (Distributed SGD) for convex problems in
multi-agent learning. Under the standard assumptions of convexity, Lipschitz
continuity, and smoothness, we establish statistical learning rates that
retain, up to logarithmic terms, centralised statistical guarantees through
implicit regularisation (step size tuning and early stopping) with appropriate
dependence on the graph topology. Our approach avoids the need for explicit
regularisation in decentralised learning problems, such as adding constraints
to the empirical risk minimisation rule. Particularly for distributed methods,
the use of implicit regularisation allows the algorithm to remain simple,
without projections or dual methods. To prove our results, we establish
graph-independent generalisation bounds for Distributed SGD that match the
centralised setting (using algorithmic stability), and we establish
graph-dependent optimisation bounds that are of independent interest. We
present numerical experiments to show that the qualitative nature of the upper
bounds we derive can be representative of real behaviours.
Mind Your POV: Convergence of Articles and Editors Towards Wikipedia's Neutrality Norm
Authors: Umashanthi Pavalanathan, Xiaochuang Han, Jacob Eisenstein
Date: 2018-09-18 00:00:00
Tasks: Articles, Time Series, Time Series Analysis

Wikipedia has a strong norm of writing in a 'neutral point of view' (NPOV).
Articles that violate this norm are tagged, and editors are encouraged to make
corrections. But the impact of this tagging system has not been quantitatively
measured. Does NPOV tagging help articles to converge to the desired style? Do
NPOV corrections encourage editors to adopt this style? We study these
questions using a corpus of NPOV-tagged articles and a set of lexicons
associated with biased language. An interrupted time series analysis shows that
after an article is tagged for NPOV, there is a significant decrease in biased
language in the article, as measured by several lexicons. However, for
individual editors, NPOV corrections and talk page discussions yield no
significant change in the usage of words in most of these lexicons, including
Wikipedia's own list of 'words to watch.' This suggests that NPOV tagging and
discussion does improve content, but has less success enculturating editors to
the site's linguistic norms.
Exploration vs. Exploitation in Team Formation
Authors: Ramesh Johari, Vijay Kamble, Anilesh K. Krishnaswamy, Hannah Li
Date: 2018-09-18 00:00:00

An online labor platform faces an online learning problem in matching workers
with jobs and using the performance on these jobs to create better future
matches. This learning problem is complicated by the rise of complex tasks on
these platforms, such as web development and product design, that require a
team of workers to complete. The success of a job is now a function of the
skills and contributions of all workers involved, which may be unknown to both
the platform and the client who posted the job. These team matchings result in
a structured correlation between what is known about the individuals and this
information can be utilized to create better future matches. We analyze two
natural settings where the performance of a team is dictated by its strongest
and its weakest member, respectively. We find that both problems pose an
exploration-exploitation tradeoff between learning the performance of untested
teams and repeating previously tested teams that resulted in a good
performance. We establish fundamental regret bounds and design near-optimal
algorithms that uncover several insights into these tradeoffs.
Predictive Collective Variable Discovery with Deep Bayesian Models
Authors: Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Date: 2018-09-18 00:00:00
Tasks: Bayesian Inference, Variational Inference

Extending spatio-temporal scale limitations of models for complex atomistic
systems considered in biochemistry and materials science necessitates the
development of enhanced sampling methods. The potential acceleration in
exploring the configurational space by enhanced sampling methods depends on the
choice of collective variables (CVs). In this work, we formulate the discovery
of CVs as a Bayesian inference problem and consider the CVs as hidden
generators of the full-atomistic trajectory. The ability to generate samples of
the fine-scale atomistic configurations using limited training data allows us
to compute estimates of observables as well as our probabilistic confidence on
them. The methodology is based on emerging methodological advances in machine
learning and variational inference. The discovered CVs are related to
physicochemical properties which are essential for understanding mechanisms
especially in unexplored complex systems. We provide a quantitative assessment
of the CVs in terms of their predictive ability for alanine dipeptide (ALA-2)
and ALA-15 peptide.
Improving Moderation of Online Discussions via Interpretable Neural Models
Authors: Andrej Švec, Matúš Pikuliak, Marián Šimko, Mária Bieliková
Date: 2018-09-18 00:00:00

Growing amount of comments make online discussions difficult to moderate by
human moderators only. Antisocial behavior is a common occurrence that often
discourages other users from participating in discussion. We propose a neural
network based method that partially automates the moderation process. It
consists of two steps. First, we detect inappropriate comments for moderators
to see. Second, we highlight inappropriate parts within these comments to make
the moderation faster. We evaluated our method on data from a major Slovak news
discussion platform.