pwc-archive/papers-with-abstracts huggingface dataset:
Asynchronous Spatial Image Convolutions for Event Cameras
Authors: Cedric Scheerlinck, Nick Barnes, Robert Mahony
Date: 2018-12-02 00:00:00

Spatial convolution is arguably the most fundamental of 2D image processing
operations. Conventional spatial image convolution can only be applied to a
conventional image, that is, an array of pixel values (or similar image
representation) that are associated with a single instant in time. Event
cameras have serial, asynchronous output with no natural notion of an image
frame, and each event arrives with a different timestamp. In this paper, we
propose a method to compute the convolution of a linear spatial kernel with the
output of an event camera. The approach operates on the event stream output of
the camera directly without synthesising pseudo-image frames as is common in
the literature. The key idea is the introduction of an internal state that
directly encodes the convolved image information, which is updated
asynchronously as each event arrives from the camera. The state can be read-off
as-often-as and whenever required for use in higher level vision algorithms for
real-time robotic systems. We demonstrate the application of our method to
corner detection, providing an implementation of a Harris corner-response
"state" that can be used in real-time for feature detection and tracking on
robotic systems.
Learning Representations of Social Media Users
Authors: Adrian Benton
Date: 2018-12-02 00:00:00
Tasks: Recommendation Systems

User representations are routinely used in recommendation systems by platform
developers, targeted advertisements by marketers, and by public policy
researchers to gauge public opinion across demographic groups. Computer
scientists consider the problem of inferring user representations more
abstractly; how does one extract a stable user representation - effective for
many downstream tasks - from a medium as noisy and complicated as social media?
  The quality of a user representation is ultimately task-dependent (e.g. does
it improve classifier performance, make more accurate recommendations in a
recommendation system) but there are proxies that are less sensitive to the
specific task. Is the representation predictive of latent properties such as a
person's demographic features, socioeconomic class, or mental health state? Is
it predictive of the user's future behavior?
  In this thesis, we begin by showing how user representations can be learned
from multiple types of user behavior on social media. We apply several
extensions of generalized canonical correlation analysis to learn these
representations and evaluate them at three tasks: predicting future hashtag
mentions, friending behavior, and demographic features. We then show how user
features can be employed as distant supervision to improve topic model fit.
Finally, we show how user features can be integrated into and improve existing
classifiers in the multitask learning framework. We treat user representations
- ground truth gender and mental health features - as auxiliary tasks to
improve mental health state prediction. We also use distributed user
representations learned in the first chapter to improve tweet-level stance
classifiers, showing that distant user information can inform classification
tasks at the granularity of a single message.
GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices
Authors: Yoni Kasten, Amnon Geifman, Meirav Galun, Ronen Basri
Date: 2018-12-02 00:00:00
Proceeding: CVPR 2019 6

This paper addresses the problem of recovering projective camera matrices from collections of fundamental matrices in multiview settings. We make two main contributions. First, given ${n \choose 2}$ fundamental matrices computed for $n$ images, we provide a complete algebraic characterization in the form of conditions that are both necessary and sufficient to enabling the recovery of camera matrices. These conditions are based on arranging the fundamental matrices as blocks in a single matrix, called the $n$-view fundamental matrix, and characterizing this matrix in terms of the signs of its eigenvalues and rank structures. Secondly, we propose a concrete algorithm for projective structure-from-motion that utilizes this characterization. Given a complete or partial collection of measured fundamental matrices, our method seeks camera matrices that minimize a global algebraic error for the measured fundamental matrices. In contrast to existing methods, our optimization, without any initialization, produces a consistent set of fundamental matrices that corresponds to a unique set of cameras (up to a choice of projective frame). Our experiments indicate that our method achieves state of the art performance in both accuracy and running time.
A multi-task deep learning model for the classification of Age-related Macular Degeneration
Authors: Qingyu Chen, Yifan Peng, Tiarnan Keenan, Shazia Dharssi, Elvira Agron, Wai T. Wong, Emily Y. Chew, Zhiyong Lu
Date: 2018-12-02 00:00:00
Tasks: Age-Related Macular Degeneration Classification, Classification, General Classification, Multi-Task Learning

Age-related Macular Degeneration (AMD) is a leading cause of blindness.
Although the Age-Related Eye Disease Study group previously developed a 9-step
AMD severity scale for manual classification of AMD severity from color fundus
images, manual grading of images is time-consuming and expensive. Built on our
previous work DeepSeeNet, we developed a novel deep learning model for
automated classification of images into the 9-step scale. Instead of predicting
the 9-step score directly, our approach simulates the reading center grading
process. It first detects four AMD characteristics (drusen area, geographic
atrophy, increased pigment, and depigmentation), then combines these to derive
the overall 9-step score. Importantly, we applied multi-task learning
techniques, which allowed us to train classification of the four
characteristics in parallel, share representation, and prevent overfitting.
Evaluation on two image datasets showed that the accuracy of the model exceeded
the current state-of-the-art model by > 10%.
Efficient Lifelong Learning with A-GEM
Authors: Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, Mohamed Elhoseiny
Date: 2018-12-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Lifelong learning

In lifelong learning, the learner is presented with a sequence of tasks,
incrementally building a data-driven prior which may be leveraged to speed up
learning of a new task. In this work, we investigate the efficiency of current
lifelong approaches, in terms of sample complexity, computational and memory
cost. Towards this end, we first introduce a new and a more realistic
evaluation protocol, whereby learners observe each example only once and
hyper-parameter selection is done on a small and disjoint set of tasks, which
is not used for the actual learning experience and evaluation. Second, we
introduce a new metric measuring how quickly a learner acquires a new skill.
Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017),
dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance
as GEM, while being almost as computationally and memory efficient as EWC
(Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we
show that all algorithms including A-GEM can learn even more quickly if they
are provided with task descriptors specifying the classification tasks under
consideration. Our experiments on several standard lifelong learning benchmarks
demonstrate that A-GEM has the best trade-off between accuracy and efficiency.
Imputation of Clinical Covariates in Time Series
Authors: Dimitris Bertsimas, Agni Orfanoudaki, Colin Pawlowski
Date: 2018-12-02 00:00:00
Tasks: Imputation, Time Series, Time Series Analysis

Missing data is a common problem in real-world settings and particularly
relevant in healthcare applications where researchers use Electronic Health
Records (EHR) and results of observational studies to apply analytics methods.
This issue becomes even more prominent for longitudinal data sets, where
multiple instances of the same individual correspond to different observations
in time. Standard imputation methods do not take into account patient specific
information incorporated in multivariate panel data. We introduce the novel
imputation algorithm MedImpute that addresses this problem, extending the
flexible framework of OptImpute suggested by Bertsimas et al. (2018). Our
algorithm provides imputations for data sets with missing continuous and
categorical features, and we present the formulation and implement scalable
first-order methods for a $K$-NN model. We test the performance of our
algorithm on longitudinal data from the Framingham Heart Study when data are
missing completely at random (MCAR). We demonstrate that MedImpute leads to
significant improvements in both imputation accuracy and downstream model AUC
compared to state-of-the-art methods.
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Authors: Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, Rob Malkin
Date: 2018-12-02 00:00:00
Tasks: Management

Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting. Snorkel DryBell builds on the Snorkel framework, extending it in three critical aspects: flexible, template-based ingestion of diverse organizational knowledge, cross-feature production serving, and scalable, sampling-free execution. On three classification tasks at Google, we find that Snorkel DryBell creates classifiers of comparable quality to ones trained with tens of thousands of hand-labeled examples, converts non-servable organizational resources to servable models for an average 52% performance improvement, and executes over millions of data points in tens of minutes.
Feature Selection Based on Unique Relevant Information for Health Data
Authors: Shiyu Liu, Mehul Motani
Date: 2018-12-02 00:00:00
Tasks: Diagnostic, feature selection

Feature selection, which searches for the most representative features in
observed data, is critical for health data analysis. Unlike feature extraction,
such as PCA and autoencoder based methods, feature selection preserves
interpretability, meaning that the selected features provide direct information
about certain health conditions (i.e., the label). Thus, feature selection
allows domain experts, such as clinicians, to understand the predictions made
by machine learning based systems, as well as improve their own diagnostic
skills. Mutual information is often used as a basis for feature selection since
it measures dependencies between features and labels. In this paper, we
introduce a novel mutual information based feature selection (MIBFS) method
called SURI, which boosts features with high unique relevant information. We
compare SURI to existing MIBFS methods using 3 different classifiers on 6
publicly available healthcare data sets. The results indicate that, in addition
to preserving interpretability, SURI selects more relevant feature subsets
which lead to higher classification performance. More importantly, we explore
the dynamics of mutual information on a public low-dimensional health data set
via exhaustive search. The results suggest the important role of unique
relevant information in feature selection and verify the principles behind
SURI.
Why Are Deep Representations Good Perceptual Quality Features?
Authors: Taimoor Tariq, Okan Tarhan Tursun, Munchurl Kim, Piotr Didyk
Date: 2018-12-02 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Quality Assessment, Image Reconstruction, Image Restoration, Image Super-Resolution, Image-to-Image Translation, Object Detection, SSIM, Super-Resolution

Recently, intermediate feature maps of pre-trained convolutional neural networks have shown significant perceptual quality improvements, when they are used in the loss function for training new networks. It is believed that these features are better at encoding the perceptual quality and provide more efficient representations of input images compared to other perceptual metrics such as SSIM and PSNR. However, there have been no systematic studies to determine the underlying reason. Due to the lack of such an analysis, it is not possible to evaluate the performance of a particular set of features or to improve the perceptual quality even more by carefully selecting a subset of features from a pre-trained CNN. This work shows that the capabilities of pre-trained deep CNN features in optimizing the perceptual quality are correlated with their success in capturing basic human visual perception characteristics. In particular, we focus our analysis on fundamental aspects of human perception, such as the contrast sensitivity and orientation selectivity. We introduce two new formulations to measure the frequency and orientation selectivity of the features learned by convolutional layers for evaluating deep features learned by widely-used deep CNNs such as VGG-16. We demonstrate that the pre-trained CNN features which receive higher scores are better at predicting human quality judgment. Furthermore, we show the possibility of using our method to select deep features to form a new loss function, which improves the image reconstruction quality for the well-known single-image super-resolution problem.
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
Authors: Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li
Date: 2018-12-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Anti-Spoofing, Face Recognition

Face anti-spoofing is essential to prevent face recognition systems from a
security breach. Much of the progresses have been made by the availability of
face anti-spoofing benchmark datasets in recent years. However, existing face
anti-spoofing benchmarks have limited number of subjects ($\le\negmedspace170$)
and modalities ($\leq\negmedspace2$), which hinder the further development of
the academic community. To facilitate face anti-spoofing research, we introduce
a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest
publicly available dataset for face anti-spoofing in terms of both subjects and
visual modalities. Specifically, it consists of $1,000$ subjects with $21,000$
videos and each sample has $3$ modalities (i.e., RGB, Depth and IR). We also
provide a measurement set, evaluation protocol and training/validation/testing
subsets, developing a new benchmark for face anti-spoofing. Moreover, we
present a new multi-modal fusion method as baseline, which performs feature
re-weighting to select the more informative channel features while suppressing
the less useful ones for each modal. Extensive experiments have been conducted
on the proposed dataset to verify its significance and generalization
capability. The dataset is available at
https://sites.google.com/qq.com/chalearnfacespoofingattackdete
Kiki Kills: Identifying Dangerous Challenge Videos from Social Media
Authors: Nupur Baghel, Yaman Kumar, Paavini Nanda, Rajiv Ratn Shah, Debanjan Mahata, Roger Zimmermann
Date: 2018-12-02 00:00:00

There has been upsurge in the number of people participating in challenges
made popular through social media channels. One of the examples of such a
challenge is the Kiki Challenge, in which people step out of their moving cars
and dance to the tunes of the song, 'Kiki, Do you love me?'. Such an action
makes the people taking the challenge prone to accidents and can also create
nuisance for the others traveling on the road. In this work, we introduce the
prevalence of such challenges in social media and show how the machine learning
community can aid in preventing dangerous situations triggered by them by
developing models that can distinguish between dangerous and non-dangerous
challenge videos. Towards this objective, we release a new dataset namely
MIDAS-KIKI dataset, consisting of manually annotated dangerous and
non-dangerous Kiki challenge videos. Further, we train a deep learning model to
identify dangerous and non-dangerous videos, and report our results.
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions
Authors: Jasper Linmans, Bob van de Velde, Evangelos Kanoulas
Date: 2018-12-02 00:00:00
Tasks: Word Embeddings

Detecting controversy in general web pages is a daunting task, but
increasingly essential to efficiently moderate discussions and effectively
filter problematic content. Unfortunately, controversies occur across many
topics and domains, with great changes over time. This paper investigates
neural classifiers as a more robust methodology for controversy detection in
general web pages. Current models have often cast controversy detection on
general web pages as Wikipedia linking, or exact lexical matching tasks. The
diverse and changing nature of controversies suggest that semantic approaches
are better able to detect controversy. We train neural networks that can
capture semantic information from texts using weak signal data. By leveraging
the semantic properties of word embeddings we robustly improve on existing
controversy detection methods. To evaluate model stability over time and to
unseen topics, we asses model performance under varying training conditions to
test cross-temporal, cross-topic, cross-domain performance and annotator
congruence. In doing so, we demonstrate that weak-signal based neural
approaches are closer to human estimates of controversy and are more robust to
the inherent variability of controversies.
Towards Automatic Discovery of Cybercrime Supply Chains
Authors: Rasika Bhalerao, Maxwell Aliapoulios, Ilia Shumailov, Sadia Afroz, Damon McCoy
Date: 2018-12-02 00:00:00

Cybercrime forums enable modern criminal entrepreneurs to collaborate with
other criminals into increasingly efficient and sophisticated criminal
endeavors. Understanding the connections between different products and
services can often illuminate effective interventions. However, generating this
understanding of supply chains currently requires time-consuming manual effort.
  In this paper, we propose a language-agnostic method to automatically extract
supply chains from cybercrime forum posts and replies. Our supply chain
detection algorithm can identify 36% and 58% relevant chains within major
English and Russian forums, respectively, showing improvements over the
baselines of 13% and 36%, respectively. Our analysis of the automatically
generated supply chains demonstrates underlying connections between products
and services within these forums. For example, the extracted supply chain
illuminated the connection between hack-for-hire services and the selling of
rare and valuable `OG' accounts, which has only recently been reported. The
understanding of connections between products and services exposes potentially
effective intervention points.
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Authors: Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Ng, Nigam H. Shah
Date: 2018-12-02 00:00:00
Tasks: Management

Identifying patients who will be discharged within 24 hours can improve
hospital resource management and quality of care. We studied this problem using
eight years of Electronic Health Records (EHR) data from Stanford Hospital. We
fit models to predict 24 hour discharge across the entire inpatient population.
The best performing models achieved an area under the receiver-operator
characteristic curve (AUROC) of 0.85 and an AUPRC of 0.53 on a held out test
set. This model was also well calibrated. Finally, we analyzed the utility of
this model in a decision theoretic framework to identify regions of ROC space
in which using the model increases expected utility compared to the trivial
always negative or always positive classifiers.
Quick Best Action Identification in Linear Bandit Problems
Authors: Jun Geng, Lifeng Lai
Date: 2018-12-02 00:00:00

In this paper, we consider a best action identification problem in the
stochastic linear bandit setup with a fixed confident constraint. In the
considered best action identification problem, instead of minimizing the
accumulative regret as done in existing works, the learner aims to obtain an
accurate estimate of the underlying parameter based on his action and reward
sequences. To improve the estimation efficiency, the learner is allowed to
select his action based his historical information; hence the whole procedure
is designed in a sequential adaptive manner. We first show that the existing
algorithms designed to minimize the accumulative regret is not a consistent
estimator and hence is not a good policy for our problem. We then characterize
a lower bound on the estimation error for any policy. We further design a
simple policy and show that the estimation error of the designed policy
achieves the same scaling order as that of the derived lower bound.
Accelerate CNN via Recursive Bayesian Pruning
Authors: Yuefu Zhou, Ya zhang, Yan-Feng Wang, Qi Tian
Date: 2018-12-02 00:00:00
Proceeding: ICCV 2019 10

Channel Pruning, widely used for accelerating Convolutional Neural Networks,
is an NP-hard problem due to the inter-layer dependency of channel redundancy.
Existing methods generally ignored the above dependency for computation
simplicity. To solve the problem, under the Bayesian framework, we here propose
a layer-wise Recursive Bayesian Pruning method (RBP). A new dropout-based
measurement of redundancy, which facilitate the computation of posterior
assuming inter-layer dependency, is introduced. Specifically, we model the
noise across layers as a Markov chain and target its posterior to reflect the
inter-layer dependency. Considering the closed form solution for posterior is
intractable, we derive a sparsity-inducing Dirac-like prior which regularizes
the distribution of the designed noise to automatically approximate the
posterior. Compared with the existing methods, no additional overhead is
required when the inter-layer dependency assumed. The redundant channels can be
simply identified by tiny dropout noise and directly pruned layer by layer.
Experiments on popular CNN architectures have shown that the proposed method
outperforms several state-of-the-arts. Particularly, we achieve up to
$\bf{5.0\times}$ and $\bf{2.2\times}$ FLOPs reduction with little accuracy loss
on the large scale dataset ILSVRC2012 for VGG16 and ResNet50, respectively.
A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents
Authors: Heriberto Cuayáhuitl, Seonghan Ryu, Donghyeon Lee, Jihie Kim
Date: 2018-12-02 00:00:00

The amount of dialogue history to include in a conversational agent is often
underestimated and/or set in an empirical and thus possibly naive way. This
suggests that principled investigations into optimal context windows are
urgently needed given that the amount of dialogue history and corresponding
representations can play an important role in the overall performance of a
conversational system. This paper studies the amount of history required by
conversational agents for reliably predicting dialogue rewards. The task of
dialogue reward prediction is chosen for investigating the effects of varying
amounts of dialogue history and their impact on system performance.
Experimental results using a dataset of 18K human-human dialogues report that
lengthy dialogue histories of at least 10 sentences are preferred (25 sentences
being the best in our experiments) over short ones, and that lengthy histories
are useful for training dialogue reward predictors with strong positive
correlations between target dialogue rewards and predicted ones.