pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Retina Using A Hybrid Neural Network With Spatial Transform Capability
Authors: Richard Wood, Alexander McGlashan, C. B. Moon, W. Y. Kim
Date: 2018-11-26 00:00:00

This paper covers the design and programming of a hybrid (digital/analog)
neural network to function as an artificial retina with the ability to perform
a spatial discrete cosine transform. We describe the structure of the circuit,
which uses an analog cell that is interlinked using a programmable digital
array. The paper is broken into three main parts. First, we present the results
of a Matlab simulation. Then we show the circuit simulation in Spice. This is
followed by a demonstration of the practical device. This system has
intentionally separated components with the specialty analog circuits being
separated from the readily available digital field programmable gate array
(FPGA) components. Further development includes the use of rapid
manufacture-able organic electronics used for the analog components. The
planned uses for this platform include crowd development of software that uses
the underlying pulse based processing. The development package will include
simulators in the form of Matlab and Spice type software platforms.
Foreground Clustering for Joint Segmentation and Localization in Videos and Images
Authors: Abhishek Sharma
Date: 2018-11-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering, Image Segmentation, Object, Object Discovery, Segmentation, Semantic Segmentation, Superpixels

This paper presents a novel framework in which video/image segmentation and
localization are cast into a single optimization problem that integrates
information from low level appearance cues with that of high level localization
cues in a very weakly supervised manner. The proposed framework leverages two
representations at different levels, exploits the spatial relationship between
bounding boxes and superpixels as linear constraints and simultaneously
discriminates between foreground and background at bounding box and superpixel
level. Different from previous approaches that mainly rely on discriminative
clustering, we incorporate a foreground model that minimizes the histogram
difference of an object across all image frames. Exploiting the geometric
relation between the superpixels and bounding boxes enables the transfer of
segmentation cues to improve localization output and vice-versa. Inclusion of
the foreground model generalizes our discriminative framework to video data
where the background tends to be similar and thus, not discriminative. We
demonstrate the effectiveness of our unified framework on the YouTube Object
video dataset, Internet Object Discovery dataset and Pascal VOC 2007.
A Survey of Mobile Computing for the Visually Impaired
Authors: Martin Weiss, Margaux Luck, Roger Girgis, Chris Pal, Joseph Paul Cohen
Date: 2018-11-25 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, Model Compression, Survey

The number of visually impaired or blind (VIB) people in the world is
estimated at several hundred million. Based on a series of interviews with the
VIB and developers of assistive technology, this paper provides a survey of
machine-learning based mobile applications and identifies the most relevant
applications. We discuss the functionality of these apps, how they align with
the needs and requirements of the VIB users, and how they can be improved with
techniques such as federated learning and model compression. As a result of
this study we identify promising future directions of research in mobile
perception, micro-navigation, and content-summarization.
Variational End-to-End Navigation and Localization
Authors: Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus
Date: 2018-11-25 00:00:00

Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason about localization of the robot within the environment. In this paper, we extend end-to-end driving networks with the ability to perform point-to-point navigation as well as probabilistic localization using only noisy GPS data. We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map. Additionally, we formulate how our model can be used to localize the robot according to correspondences between the map and the observed visual road topology, inspired by the rough localization that human drivers can perform. We test our algorithms on real-world driving data that the vehicle has never driven through before, and integrate our point-to-point navigation algorithms onboard a full-scale autonomous vehicle for real-time performance. Our localization algorithm is also evaluated over a new set of roads and intersections to demonstrates rough pose localization even in situations without any GPS prior.
Evoplex: A platform for agent-based modeling on networks
Authors: Marcos Cardinot, Colm O'Riordan, Josephine Griffith, Matjaž Perc
Date: 2018-11-25 00:00:00

Agent-based modeling and network science have been used extensively to
advance our understanding of emergent collective behavior in systems that are
composed of a large number of simple interacting individuals or agents. With
the increasing availability of high computational power in affordable personal
computers, dedicated efforts to develop multi-threaded, scalable and
easy-to-use software for agent-based simulations are needed more than ever.
Evoplex meets this need by providing a fast, robust and extensible platform for
developing agent-based models and multi-agent systems on networks. Each agent
is represented as a node and interacts with its neighbors, as defined by the
network structure. Evoplex is ideal for modeling complex systems, for example
in evolutionary game theory and computational social science. In Evoplex, the
models are not coupled to the execution parameters or the visualization tools,
and there is a user-friendly graphical interface which makes it easy for all
users, ranging from newcomers to experienced, to create, analyze, replicate and
reproduce the experiments.
Cooperation in the spatial prisoner's dilemma game with probabilistic abstention
Authors: Marcos Cardinot, Josephine Griffith, Colm O'Riordan, Matjaz Perc
Date: 2018-11-25 00:00:00

Research has shown that the addition of abstention as an option transforms
social dilemmas to rock-paper-scissor type games, where defectors dominate
cooperators, cooperators dominate abstainers (loners), and abstainers (loners),
in turn, dominate defectors. In this way, abstention can sustain cooperation
even under adverse conditions, although defection also persists due to cyclic
dominance. However, to abstain or to act as a loner has, to date, always been
considered as an independent, third strategy to complement traditional
cooperation and defection. Here we consider probabilistic abstention, where
each player is assigned a probability to abstain in a particular instance of
the game. In the two limiting cases, the studied game reverts to the prisoner's
dilemma game without loners or to the optional prisoner's dilemma game. For
intermediate probabilities, we have a new hybrid game, which turns out to be
most favorable for the successful evolution of cooperation. We hope this novel
hybrid game provides a more realistic view of the dilemma of optional/voluntary
participation.
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task
Authors: Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière, Julien Stirnemann, Emmanuel Spaggiari, Antoine Neuraz
Date: 2018-11-25 00:00:00
Tasks: Diagnostic, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work, we present our various contributions to the objective of
building a decision support tool for the diagnosis of rare diseases. Our goal
is to achieve a state of knowledge where the uncertainty about the patient's
disease is below a predetermined threshold. We aim to reach such states while
minimizing the average number of medical tests to perform. In doing so, we take
into account the need, in many medical applications, to avoid, as much as
possible, any misdiagnosis. To solve this optimization task, we investigate
several reinforcement learning algorithm and make them operable in our
high-dimensional and sparse-reward setting. We also present a way to combine
expert knowledge, expressed as conditional probabilities, with real clinical
data. This is crucial because the scarcity of data in the field of rare
diseases prevents any approach based solely on clinical data. Finally we show
that it is possible to integrate the ontological information about symptoms
while remaining in our probabilistic reasoning. It enables our decision support
tool to process information given at different level of precision by the user.
Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG
Authors: Abhay Koushik, Judith Amores, Pattie Maes
Date: 2018-11-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Sleep Stage Detection, Sleep Staging

We present the first real-time sleep staging system that uses deep learning
without the need for servers in a smartphone application for a wearable EEG. We
employ real-time adaptation of a single channel Electroencephalography (EEG) to
infer from a Time-Distributed 1-D Deep Convolutional Neural Network.
Polysomnography (PSG)-the gold standard for sleep staging, requires a human
scorer and is both complex and resource-intensive. Our work demonstrates an
end-to-end on-smartphone pipeline that can infer sleep stages in just single
30-second epochs, with an overall accuracy of 83.5% on 20-fold cross validation
for five-class classification of sleep stages using the open Sleep-EDF dataset.
The Anatomy of a Cryptocurrency Pump-and-Dump Scheme
Authors: Jiahua Xu, Benjamin Livshits
Date: 2018-11-25 00:00:00
Tasks: Anatomy

While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.
Sparse PCA from Sparse Linear Regression
Authors: Guy Bresler, Sung Min Park, Madalina Persu
Date: 2018-11-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: regression

Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression (SLR)
have a wide range of applications and have attracted a tremendous amount of
attention in the last two decades as canonical examples of statistical problems
in high dimension. A variety of algorithms have been proposed for both SPCA and
SLR, but an explicit connection between the two had not been made. We show how
to efficiently transform a black-box solver for SLR into an algorithm for SPCA:
assuming the SLR solver satisfies prediction error guarantees achieved by
existing efficient algorithms such as those based on the Lasso, the SPCA
algorithm derived from it achieves near state of the art guarantees for testing
and for support recovery for the single spiked covariance model as obtained by
the current best polynomialtime algorithms. Our reduction not only highlights
the inherent similarity between the two problems, but also, from a practical
standpoint, allows one to obtain a collection of algorithms for SPCA directly
from known algorithms for SLR. We provide experimental results on simulated
data comparing our proposed framework to other algorithms for SPCA.
Inexact SARAH Algorithm for Stochastic Optimization
Authors: Lam M. Nguyen, Katya Scheinberg, Martin Takáč
Date: 2018-11-25 00:00:00
Tasks: Stochastic Optimization

We develop and analyze a variant of the SARAH algorithm, which does not require computation of the exact gradient. Thus this new method can be applied to general expectation minimization problems rather than only finite sum problems. While the original SARAH algorithm, as well as its predecessor, SVRG, require an exact gradient computation on each outer iteration, the inexact variant of SARAH (iSARAH), which we develop here, requires only stochastic gradient computed on a mini-batch of sufficient size. The proposed method combines variance reduction via sample size selection and iterative stochastic gradient updates. We analyze the convergence rate of the algorithms for strongly convex and non-strongly convex cases, under smooth assumption with appropriate mini-batch size selected for each case. We show that with an additional, reasonable, assumption iSARAH achieves the best known complexity among stochastic methods in the case of non-strongly convex stochastic functions.
50 Years of Test (Un)fairness: Lessons for Machine Learning
Authors: Ben Hutchinson, Margaret Mitchell
Date: 2018-11-25 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Quantitative definitions of what is unfair and what is fair have been
introduced in multiple disciplines for well over 50 years, including in
education, hiring, and machine learning. We trace how the notion of fairness
has been defined within the testing communities of education and hiring over
the past half century, exploring the cultural and social context in which
different fairness definitions have emerged. In some cases, earlier definitions
of fairness are similar or identical to definitions of fairness in current
machine learning research, and foreshadow current formal work. In other cases,
insights into what fairness means and how to measure it have largely gone
overlooked. We compare past and current notions of fairness along several
dimensions, including the fairness criteria, the focus of the criteria (e.g., a
test, a model, or its use), the relationship of fairness to individuals,
groups, and subgroups, and the mathematical method for measuring fairness
(e.g., classification, regression). This work points the way towards future
research and measurement of (un)fairness that builds from our modern
understanding of fairness while incorporating insights from the past.
WarpGAN: Automatic Caricature Generation
Authors: Yichun Shi, Debayan Deb, Anil K. Jain
Date: 2018-11-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Photo-To-Caricature Translation

We propose, WarpGAN, a fully automatic network that can generate caricatures
given an input face photo. Besides transferring rich texture styles, WarpGAN
learns to automatically predict a set of control points that can warp the photo
into a caricature, while preserving identity. We introduce an
identity-preserving adversarial loss that aids the discriminator to distinguish
between different subjects. Moreover, WarpGAN allows customization of the
generated caricatures by controlling the exaggeration extent and the visual
styles. Experimental results on a public domain dataset, WebCaricature, show
that WarpGAN is capable of generating a diverse set of caricatures while
preserving the identities. Five caricature experts suggest that caricatures
generated by WarpGAN are visually similar to hand-drawn ones and only prominent
facial features are exaggerated.
Planning in Dynamic Environments with Conditional Autoregressive Models
Authors: Johanna Hansen, Kyle Kastner, Aaron Courville, Gregory Dudek
Date: 2018-11-25 00:00:00

We demonstrate the use of conditional autoregressive generative models (van
den Oord et al., 2016a) over a discrete latent space (van den Oord et al.,
2017b) for forward planning with MCTS. In order to test this method, we
introduce a new environment featuring varying difficulty levels, along with
moving goals and obstacles. The combination of high-quality frame generation
and classical planning approaches nearly matches true environment performance
for our task, demonstrating the usefulness of this method for model-based
planning in dynamic environments.
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
Authors: Xin Wang, Qiuyuan Huang, Asli Celikyilmaz, Jianfeng Gao, Dinghan Shen, Yuan-Fang Wang, William Yang Wang, Lei Zhang
Date: 2018-11-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL), Vision-Language Navigation, Visual Navigation

Vision-language navigation (VLN) is the task of navigating an embodied agent
to carry out natural language instructions inside real 3D environments. In this
paper, we study how to address three critical challenges for this task: the
cross-modal grounding, the ill-posed feedback, and the generalization problems.
First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that
enforces cross-modal grounding both locally and globally via reinforcement
learning (RL). Particularly, a matching critic is used to provide an intrinsic
reward to encourage global matching between instructions and trajectories, and
a reasoning navigator is employed to perform cross-modal grounding in the local
visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model
significantly outperforms previous methods by 10% on SPL and achieves the new
state-of-the-art performance. To improve the generalizability of the learned
policy, we further introduce a Self-Supervised Imitation Learning (SIL) method
to explore unseen environments by imitating its own past, good decisions. We
demonstrate that SIL can approximate a better and more efficient policy, which
tremendously minimizes the success rate performance gap between seen and unseen
environments (from 30.7% to 11.7%).
Learning to discover and localize visual objects with open vocabulary
Authors: Keren Ye, Mingda Zhang, Wei Li, Danfeng Qin, Adriana Kovashka, Jesse Berent
Date: 2018-11-25 00:00:00
Tasks: Object, object-detection, Object Detection

To alleviate the cost of obtaining accurate bounding boxes for training
today's state-of-the-art object detection models, recent weakly supervised
detection work has proposed techniques to learn from image-level labels.
However, requiring discrete image-level labels is both restrictive and
suboptimal. Real-world "supervision" usually consists of more unstructured
text, such as captions. In this work we learn association maps between images
and captions. We then use a novel objectness criterion to rank the resulting
candidate boxes, such that high-ranking boxes have strong gradients along all
edges. Thus, we can detect objects beyond a fixed object category vocabulary,
if those objects are frequent and distinctive enough. We show that our
objectness criterion improves the proposed bounding boxes in relation to prior
weakly supervised detection methods. Further, we show encouraging results on
object detection from image-level captions only.
The promises and pitfalls of Stochastic Gradient Langevin Dynamics
Authors: Nicolas Brosse, Alain Durmus, Eric Moulines
Date: 2018-11-25 00:00:00
Proceeding: NeurIPS 2018 12

Stochastic Gradient Langevin Dynamics (SGLD) has emerged as a key MCMC
algorithm for Bayesian learning from large scale datasets. While SGLD with
decreasing step sizes converges weakly to the posterior distribution, the
algorithm is often used with a constant step size in practice and has
demonstrated successes in machine learning tasks. The current practice is to
set the step size inversely proportional to $N$ where $N$ is the number of
training samples. As $N$ becomes large, we show that the SGLD algorithm has an
invariant probability measure which significantly departs from the target
posterior and behaves like Stochastic Gradient Descent (SGD). This difference
is inherently due to the high variance of the stochastic gradients. Several
strategies have been suggested to reduce this effect; among them, SGLD Fixed
Point (SGLDFP) uses carefully designed control variates to reduce the variance
of the stochastic gradients. We show that SGLDFP gives approximate samples from
the posterior distribution, with an accuracy comparable to the Langevin Monte
Carlo (LMC) algorithm for a computational cost sublinear in the number of data
points. We provide a detailed analysis of the Wasserstein distances between
LMC, SGLD, SGLDFP and SGD and explicit expressions of the means and covariance
matrices of their invariant distributions. Our findings are supported by
limited numerical experiments.
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
Authors: Andrey Kuehlkamp, Allan Pinto, Anderson Rocha, Kevin Bowyer, Adam Czajka
Date: 2018-11-25 00:00:00
Tasks: Cross-Domain Iris Presentation Attack Detection, Iris Recognition, MULTI-VIEW LEARNING

The adoption of large-scale iris recognition systems around the world has
brought to light the importance of detecting presentation attack images
(textured contact lenses and printouts). This work presents a new approach in
iris Presentation Attack Detection (PAD), by exploring combinations of
Convolutional Neural Networks (CNNs) and transformed input spaces through
binarized statistical image features (BSIF). Our method combines lightweight
CNNs to classify multiple BSIF views of the input image. Following explorations
on complementary input spaces leading to more discriminative features to detect
presentation attacks, we also propose an algorithm to select the best (and most
discriminative) predictors for the task at hand.An ensemble of predictors makes
use of their expected individual performances to aggregate their results into a
final prediction. Results show that this technique improves on the current
state of the art in iris PAD, outperforming the winner of LivDet-Iris2017
competition both for intra- and cross-dataset scenarios, and illustrating the
very difficult nature of the cross-dataset scenario.
Predicting Gender from Iris Texture May Be Harder Than It Seems
Authors: Andrey Kuehlkamp, Kevin Bowyer
Date: 2018-11-25 00:00:00
Tasks: Gender Prediction

Predicting gender from iris images has been reported by several researchers
as an application of machine learning in biometrics. Recent works on this topic
have suggested that the preponderance of the gender cues is located in the
periocular region rather than in the iris texture itself. This paper focuses on
teasing out whether the information for gender prediction is in the texture of
the iris stroma, the periocular region, or both. We present a larger dataset
for gender from iris, and evaluate gender prediction accuracy using linear SVM
and CNN, comparing hand-crafted and deep features. We use probabilistic
occlusion masking to gain insight on the problem. Results suggest the
discriminative power of the iris texture for gender is weaker than previously
thought, and that the gender-related information is primarily in the periocular
region.