pwc-archive/papers-with-abstracts huggingface dataset:
Introduction to Multi-Armed Bandits
Authors: Aleksandrs Slivkins
Date: 2019-04-15 00:00:00
Tasks: Multi-Armed Bandits

Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a particular line of work, providing a self-contained, teachable technical introduction and a brief review of the further developments; many of the chapters conclude with exercises. The book is structured as follows. The first four chapters are on IID rewards, from the basic model to impossibility results to Bayesian priors to Lipschitz rewards. The next three chapters cover adversarial rewards, from the full-feedback version to adversarial bandits to extensions with linear rewards and combinatorially structured actions. Chapter 8 is on contextual bandits, a middle ground between IID and adversarial bandits in which the change in reward distributions is completely explained by observable contexts. The last three chapters cover connections to economics, from learning in repeated games to bandits with supply/budget constraints to exploration in the presence of incentives. The appendix provides sufficient background on concentration and KL-divergence. The chapters on "bandits with similarity information", "bandits with knapsacks" and "bandits and agents" can also be consumed as standalone surveys on the respective topics.
Low-Power Computer Vision: Status, Challenges, Opportunities
Authors: Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong Gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
Date: 2019-04-15 00:00:00

Computer vision has achieved impressive progress in recent years. Meanwhile,
mobile phones have become the primary computing platforms for millions of
people. In addition to mobile phones, many autonomous systems rely on visual
data for making decisions and some of these systems have limited energy (such
as unmanned aerial vehicles also called drones and mobile robots). These
systems rely on batteries and energy efficiency is critical. This article
serves two main purposes: (1) Examine the state-of-the-art for low-power
solutions to detect objects in images. Since 2015, the IEEE Annual
International Low-Power Image Recognition Challenge (LPIRC) has been held to
identify the most energy-efficient computer vision solutions. This article
summarizes 2018 winners' solutions. (2) Suggest directions for research as well
as opportunities for low-power computer vision.
Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments
Authors: Fethiye Irmak Doğan, Sinan Kalkan, Iolanda Leite
Date: 2019-04-15 00:00:00
Tasks: Referring Expression

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate ($\sim$30% better) and would prefer to use ($\sim$32% more often).
Multi-Objective Autonomous Braking System using Naturalistic Dataset
Authors: Rafael Vasquez, Bilal Farooq
Date: 2019-04-15 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A deep reinforcement learning based multi-objective autonomous braking system is presented. The design of the system is formulated in a continuous action space and seeks to maximize both pedestrian safety and perception as well as passenger comfort. The vehicle agent is trained against a large naturalistic dataset containing pedestrian road-crossing trials in which respondents walked across a road under various traffic conditions within an interactive virtual reality environment. The policy for brake control is learned through computer simulation using two reinforcement learning methods i.e. Proximal Policy Optimization and Deep Deterministic Policy Gradient and the efficiency of each are compared. Results show that the system is able to reduce the negative influence on passenger comfort by half while maintaining safe braking operation.
Focus Is All You Need: Loss Functions For Event-based Vision
Authors: Guillermo Gallego, Mathias Gehrig, Davide Scaramuzza
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: All, Event-based vision, Motion Compensation, Optical Flow Estimation

Event cameras are novel vision sensors that output pixel-level brightness
changes ("events") instead of traditional video frames. These asynchronous
sensors offer several advantages over traditional cameras, such as, high
temporal resolution, very high dynamic range, and no motion blur. To unlock the
potential of such sensors, motion compensation methods have been recently
proposed. We present a collection and taxonomy of twenty two objective
functions to analyze event alignment in motion compensation approaches (Fig.
1). We call them Focus Loss Functions since they have strong connections with
functions used in traditional shape-from-focus applications. The proposed loss
functions allow bringing mature computer vision tools to the realm of event
cameras. We compare the accuracy and runtime performance of all loss functions
on a publicly available dataset, and conclude that the variance, the gradient
and the Laplacian magnitudes are among the best loss functions. The
applicability of the loss functions is shown on multiple tasks: rotational
motion, depth and optical flow estimation. The proposed focus loss functions
allow to unlock the outstanding properties of event cameras.
Multiple kernel learning for integrative consensus clustering of 'omic datasets
Authors: Alessandra Cabassi, Paul D. W. Kirk
Date: 2019-04-15 00:00:00
Tasks: Clustering

Diverse applications - particularly in tumour subtyping - have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster-Of-Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets, or datasets that define conflicting clustering structures, is unclear. We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery. R packages "klic" and "coca" are available on the Comprehensive R Archive Network.
Three scenarios for continual learning
Authors: Gido M. van de Ven, Andreas S. Tolias
Date: 2019-04-15 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning, Lifelong learning, Permuted-MNIST

Standard artificial neural networks suffer from the well-known issue of
catastrophic forgetting, making continual or lifelong learning difficult for
machine learning. In recent years, numerous methods have been proposed for
continual learning, but due to differences in evaluation protocols it is
difficult to directly compare their performance. To enable more structured
comparisons, we describe three continual learning scenarios based on whether at
test time task identity is provided and--in case it is not--whether it must be
inferred. Any sequence of well-defined tasks can be performed according to each
scenario. Using the split and permuted MNIST task protocols, for each scenario
we carry out an extensive comparison of recently proposed continual learning
methods. We demonstrate substantial differences between the three scenarios in
terms of difficulty and in terms of how efficient different methods are. In
particular, when task identity must be inferred (i.e., class incremental
learning), we find that regularization-based approaches (e.g., elastic weight
consolidation) fail and that replaying representations of previous experiences
seems required for solving this scenario.
A Fast Dictionary Learning Method for Coupled Feature Space Learning
Authors: F. G. Veshki, S. A. Vorobyov
Date: 2019-04-15 00:00:00
Tasks: Dictionary Learning, General Classification, Sparse Representation-based Classification

In this letter, we propose a novel computationally efficient coupled
dictionary learning method that enforces pairwise correlation between the atoms
of dictionaries learned to represent the underlying feature spaces of two
different representations of the same signals, e.g., representations in
different modalities or representations of the same signals measured with
different qualities. The jointly learned correlated feature spaces represented
by coupled dictionaries are used in sparse representation based classification,
recognition and reconstruction tasks. The presented experimental results show
that the proposed coupled dictionary learning method has a significantly lower
computational cost. Moreover, the visual presentation of jointly learned
dictionaries shows that the pairwise correlations between the corresponding
atoms are ensured.
Estimation of Linear Motion in Dense Crowd Videos using Langevin Model
Authors: Shreetam Behera, Debi Prosad Dogra, Malay Kumar Bandyopadhyay, Partha Pratim Roy
Date: 2019-04-15 00:00:00
Tasks: Decision Making, Optical Flow Estimation

Crowd gatherings at social and cultural events are increasing in leaps and
bounds with the increase in population. Surveillance through computer vision
and expert decision making systems can help to understand the crowd phenomena
at large gatherings. Understanding crowd phenomena can be helpful in early
identification of unwanted incidents and their prevention. Motion flow is one
of the important crowd phenomena that can be instrumental in describing the
crowd behavior. Flows can be useful in understanding instabilities in the
crowd. However, extracting motion flows is a challenging task due to randomness
in crowd movement and limitations of the sensing device. Moreover, low-level
features such as optical flow can be misleading if the randomness is high. In
this paper, we propose a new model based on Langevin equation to analyze the
linear dominant flows in videos of densely crowded scenarios. We assume a force
model with three components, namely external force, confinement/drift force,
and disturbance force. These forces are found to be sufficient to describe the
linear or near-linear motion in dense crowd videos. The method is significantly
faster as compared to existing popular crowd segmentation methods. The
evaluation of the proposed model has been carried out on publicly available
datasets as well as using our dataset. It has been observed that the proposed
method is able to estimate and segment the linear flows in the dense crowd with
better accuracy as compared to state-of-the-art techniques with substantial
decrease in the computational overhead.
SpeechYOLO: Detection and Localization of Speech Objects
Authors: Yael Segal, Tzeviya Sylvia Fuchs, Joseph Keshet
Date: 2019-04-14 00:00:00
Tasks: General Classification, Keyword Spotting, Object, object-detection, Object Detection, speech-recognition, Speech Recognition

In this paper, we propose to apply object detection methods from the vision domain on the speech recognition domain, by treating audio fragments as objects. More specifically, we present SpeechYOLO, which is inspired by the YOLO algorithm for object detection in images. The goal of SpeechYOLO is to localize boundaries of utterances within the input signal, and to correctly classify them. Our system is composed of a convolutional neural network, with a simple least-mean-squares loss function. We evaluated the system on several keyword spotting tasks, that include corpora of read speech and spontaneous speech. Our system compares favorably with other algorithms trained for both localization and classification.
Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models
Authors: Prateek Bansal, Rico Krueger, Michel Bierlaire, Ricardo A. Daziano, Taha H. Rashidi
Date: 2019-04-13 00:00:00
Tasks: Data Augmentation

The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves
sampling from conditional densities of utility parameters using
Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for
logit kernel. To address this non-conjugacy concern, we propose the application
of P\'olygamma data augmentation (PG-DA) technique for the MMNL estimation. The
posterior estimates of the augmented and the default Gibbs sampler are similar
for two-alternative scenario (binary choice), but we encounter empirical
identification issues in the case of more alternatives ($J \geq 3$).
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey
Authors: Qiang Jipeng, Qian Zhenyu, Li Yun, Yuan Yunhao, Wu Xindong
Date: 2019-04-13 00:00:00
Tasks: Survey

Analyzing short texts infers discriminative and coherent latent topics that
is a critical and fundamental task since many real-world applications require
semantic understanding of short texts. Traditional long text topic modeling
algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this
problem very well since only very limited word co-occurrence information is
available in short texts. Therefore, short text topic modeling has already
attracted much attention from the machine learning research community in recent
years, which aims at overcoming the problem of sparseness in short texts. In
this survey, we conduct a comprehensive review of various short text topic
modeling techniques proposed in the literature. We present three categories of
methods based on Dirichlet multinomial mixture, global word co-occurrences, and
self-aggregation, with example of representative approaches in each category
and analysis of their performance on various tasks. We develop the first
comprehensive open-source library, called STTM, for use in Java that integrates
all surveyed algorithms within a unified interface, benchmark datasets, to
facilitate the expansion of new methods in this research field. Finally, we
evaluate these state-of-the-art methods on many real-world datasets and compare
their performance against one another and versus long text topic modeling
algorithm.
Low-Rank Deep Convolutional Neural Network for Multi-Task Learning
Authors: Fang Su, Hai-Yang Shang, Jing-Yan Wang
Date: 2019-04-12 00:00:00
Tasks: Attribute, Multi-Task Learning

In this paper, we propose a novel multi-task learning method based on the
deep convolutional network. The proposed deep network has four convolutional
layers, three max-pooling layers, and two parallel fully connected layers. To
adjust the deep network to multi-task learning problem, we propose to learn a
low-rank deep network so that the relation among different tasks can be
explored. We proposed to minimize the number of independent parameter rows of
one fully connected layer to explore the relations among different tasks, which
is measured by the nuclear norm of the parameter of one fully connected layer,
and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize
another fully connected layer by sparsity penalty, so that the useful features
learned by the lower layers can be selected. The learning problem is solved by
an iterative algorithm based on gradient descent and back-propagation
algorithms. The proposed algorithm is evaluated over benchmark data sets of
multiple face attribute prediction, multi-task natural language processing, and
joint economics index predictions. The evaluation results show the advantage of
the low-rank deep CNN model over multi-task problems.
Graph Wavelet Neural Network
Authors: Bingbing Xu, Hua-Wei Shen, Qi Cao, Yunqi Qiu, Xue-Qi Cheng
Date: 2019-04-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification

We present graph wavelet neural network (GWNN), a novel graph convolutional
neural network (CNN), leveraging graph wavelet transform to address the
shortcomings of previous spectral graph CNN methods that depend on graph
Fourier transform. Different from graph Fourier transform, graph wavelet
transform can be obtained via a fast algorithm without requiring matrix
eigendecomposition with high computational cost. Moreover, graph wavelets are
sparse and localized in vertex domain, offering high efficiency and good
interpretability for graph convolution. The proposed GWNN significantly
outperforms previous spectral graph CNNs in the task of graph-based
semi-supervised classification on three benchmark datasets: Cora, Citeseer and
Pubmed.
Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders
Authors: Andrei Damian, Laurentiu Piciu, Sergiu Turlea, Nicolae Tapus
Date: 2019-04-11 00:00:00
Tasks: Activity Prediction, Product Recommendation, Recommendation Systems, Time Series Analysis

Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and directions of recommender systems advancement. Worth to mention is the fact that in past years multiple traditional "offline" retail business are gearing more and more towards employing inferential and even predictive analytics both to stock-related problems such as predictive replenishment but also to enrich customer interaction experience. One of the most important areas of recommender systems research and development is that of Deep Learning based models which employ representational learning to model consumer behavioral patterns. Current state of the art in Deep Learning based recommender systems uses multiple approaches ranging from already classical methods such as the ones based on learning product representation vector, to recurrent analysis of customer transactional time-series and up to generative models based on adversarial training. Each of these methods has multiple advantages and inherent weaknesses such as inability of understanding the actual user-journey, ability to propose only single product recommendation or top-k product recommendations without prediction of actual next-best-offer. In our work we will present a new and innovative architectural approach of applying state-of-the-art hierarchical multi-module encoder-decoder architecture in order to solve several of current state-of-the-art recommender systems issues. Our approach will also produce by-products such as product need-based segmentation and customer behavioral segmentation - all in an end-to-end trainable approach. Finally, we will present a couple methods that solve known retail & distribution pain-points based on the proposed architecture.
Automatic alignment of surgical videos using kinematic data
Authors: Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, François Petitjean, Lhassane Idoumghar, Pierre-Alain Muller
Date: 2019-04-03 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

Over the past one hundred years, the classic teaching methodology of "see
one, do one, teach one" has governed the surgical education systems worldwide.
With the advent of Operation Room 2.0, recording video, kinematic and many
other types of data during the surgery became an easy task, thus allowing
artificial intelligence systems to be deployed and used in surgical and medical
practice. Recently, surgical videos has been shown to provide a structure for
peer coaching enabling novice trainees to learn from experienced surgeons by
replaying those videos. However, the high inter-operator variability in
surgical gesture duration and execution renders learning from comparing novice
to expert surgical videos a very difficult task. In this paper, we propose a
novel technique to align multiple videos based on the alignment of their
corresponding kinematic multivariate time series data. By leveraging the
Dynamic Time Warping measure, our algorithm synchronizes a set of videos in
order to show the same gesture being performed at different speed. We believe
that the proposed approach is a valuable addition to the existing learning
tools for surgery.
Medical device surveillance with electronic health records
Authors: Alison Callahan, Jason A. Fries, Christopher Ré, James I Huddleston III, Nicholas J Giori, Scott Delp, Nigam H. Shah
Date: 2019-04-03 00:00:00
Tasks: Reading Comprehension

Post-market medical device surveillance is a challenge facing manufacturers,
regulatory agencies, and health care providers. Electronic health records are
valuable sources of real world evidence to assess device safety and track
device-related patient outcomes over time. However, distilling this evidence
remains challenging, as information is fractured across clinical notes and
structured records. Modern machine learning methods for machine reading promise
to unlock increasingly complex information from text, but face barriers due to
their reliance on large and expensive hand-labeled training sets. To address
these challenges, we developed and validated state-of-the-art deep learning
methods that identify patient outcomes from clinical notes without requiring
hand-labeled training data. Using hip replacements as a test case, our methods
accurately extracted implant details and reports of complications and pain from
electronic health records with up to 96.3% precision, 98.5% recall, and 97.4%
F1, improved classification performance by 12.7- 53.0% over rule-based methods,
and detected over 6 times as many complication events compared to using
structured data alone. Using these events to assess complication-free
survivorship of different implant systems, we found significant variation
between implants, including for risk of revision surgery, which could not be
detected using coded data alone. Patients with revision surgeries had more hip
pain mentions in the post-hip replacement, pre-revision period compared to
patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs.
3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or
lower rates of hip pain mentions. Our methods complement existing surveillance
mechanisms by requiring orders of magnitude less hand-labeled training data,
offering a scalable solution for national medical device surveillance.