pwc-archive/papers-with-abstracts huggingface dataset:
A novel database of Children's Spontaneous Facial Expressions (LIRIS-CSE)
Authors: Rizwan Ahmed Khan, Crenn Arthur, Alexandre Meyer, Saida Bouakaz
Date: 2018-12-04 00:00:00
Tasks: Benchmarking, Facial Expression Recognition, Facial Expression Recognition (FER), Transfer Learning

Computing environment is moving towards human-centered designs instead of
computer centered designs and human's tend to communicate wealth of information
through affective states or expressions. Traditional Human Computer Interaction
(HCI) based systems ignores bulk of information communicated through those
affective states and just caters for user's intentional input. Generally, for
evaluating and benchmarking different facial expression analysis algorithms,
standardized databases are needed to enable a meaningful comparison. In the
absence of comparative tests on such standardized databases it is difficult to
find relative strengths and weaknesses of different facial expression
recognition algorithms. In this article we present a novel video database for
Children's Spontaneous facial Expressions (LIRIS-CSE). Proposed video database
contains six basic spontaneous facial expressions shown by 12 ethnically
diverse children between the ages of 6 and 12 years with mean age of 7.3 years.
To the best of our knowledge, this database is first of its kind as it records
and shows spontaneous facial expressions of children. Previously there were few
database of children expressions and all of them show posed or exaggerated
expressions which are different from spontaneous or natural expressions. Thus,
this database will be a milestone for human behavior researchers. This database
will be a excellent resource for vision community for benchmarking and
comparing results. In this article, we have also proposed framework for
automatic expression recognition based on convolutional neural network (CNN)
architecture with transfer learning approach. Proposed architecture achieved
average classification accuracy of 75% on our proposed database i.e. LIRIS-CSE.
Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data
Authors: Vipul Satone, Rachneet Kaur, Faraz Faghri, Mike A. Nalls, Andrew B Singleton, Roy H. Campbell
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning

Alzheimer's disease (AD) is a degenerative brain disease impairing a person's
ability to perform day to day activities. The clinical manifestations of
Alzheimer's disease are characterized by heterogeneity in age, disease span,
progression rate, impairment of memory and cognitive abilities. Due to these
variabilities, personalized care and treatment planning, as well as patient
counseling about their individual progression is limited. Recent developments
in machine learning to detect hidden patterns in complex, multi-dimensional
datasets provides significant opportunities to address this critical need. In
this work, we use unsupervised and supervised machine learning approaches for
subtype identification and prediction. We apply machine learning methods to the
extensive clinical observations available at the Alzheimer's Disease
Neuroimaging Initiative (ADNI) data set to identify patient subtypes and to
predict disease progression. Our analysis depicts the progression space for the
Alzheimer's disease into low, moderate and high disease progression zones. The
proposed work will enable early detection and characterization of distinct
disease subtypes based on clinical heterogeneity. We anticipate that our models
will enable patient counseling, clinical trial design, and ultimately
individualized clinical care.
Batch Selection for Parallelisation of Bayesian Quadrature
Authors: Ed Wagstaff, Saad Hamid, Michael Osborne
Date: 2018-12-04 00:00:00
Tasks: Bayesian Optimisation, BIG-bench Machine Learning, Numerical Integration

Integration over non-negative integrands is a central problem in machine
learning (e.g. for model averaging, (hyper-)parameter marginalisation, and
computing posterior predictive distributions). Bayesian Quadrature is a
probabilistic numerical integration technique that performs promisingly when
compared to traditional Markov Chain Monte Carlo methods. However, in contrast
to easily-parallelised MCMC methods, Bayesian Quadrature methods have, thus
far, been essentially serial in nature, selecting a single point to sample at
each step of the algorithm. We deliver methods to select batches of points at
each step, based upon those recently presented in the Batch Bayesian
Optimisation literature. Such parallelisation significantly reduces computation
time, especially when the integrand is expensive to sample.
Exploration versus exploitation in reinforcement learning: a stochastic control approach
Authors: Haoran Wang, Thaleia Zariphopoulou, Xunyu Zhou
Date: 2018-12-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider reinforcement learning (RL) in continuous time and study the
problem of achieving the best trade-off between exploration of a black box
environment and exploitation of current knowledge. We propose an
entropy-regularized reward function involving the differential entropy of the
distributions of actions, and motivate and devise an exploratory formulation
for the feature dynamics that captures repetitive learning under exploration.
The resulting optimization problem is a revitalization of the classical relaxed
stochastic control. We carry out a complete analysis of the problem in the
linear--quadratic (LQ) setting and deduce that the optimal feedback control
distribution for balancing exploitation and exploration is Gaussian. This in
turn interprets and justifies the widely adopted Gaussian exploration in RL,
beyond its simplicity for sampling. Moreover, the exploitation and exploration
are captured, respectively and mutual-exclusively, by the mean and variance of
the Gaussian distribution. We also find that a more random environment contains
more learning opportunities in the sense that less exploration is needed. We
characterize the cost of exploration, which, for the LQ case, is shown to be
proportional to the entropy regularization weight and inversely proportional to
the discount rate. Finally, as the weight of exploration decays to zero, we
prove the convergence of the solution of the entropy-regularized LQ problem to
the one of the classical LQ problem.
Particle identification in ground-based gamma-ray astronomy using convolutional neural networks
Authors: E. B. Postnikov, I. V. Bychkov, J. Y. Dubenskaya, O. L. Fedorov, Y. A. Kazarina, E. E. Korosteleva, A. P. Kryukov, A. A. Mikhailov, M. D. Nguyen, S. P. Polyakov, A. O. Shigarov, D. A. Shipilov, D. P. Zhurov
Date: 2018-12-04 00:00:00
Tasks: Astronomy, Deep Learning

Modern detectors of cosmic gamma-rays are a special type of imaging
telescopes (air Cherenkov telescopes) supplied with cameras with a relatively
large number of photomultiplier-based pixels. For example, the camera of the
TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such
cameras can be analysed by deep learning techniques to extract numerous
physical and geometrical parameters and/or for incoming particle
identification. The most powerful deep learning technique for image analysis,
the so-called convolutional neural network (CNN), was implemented in this
study. Two open source libraries for machine learning, PyTorch and TensorFlow,
were tested as possible software platforms for particle identification in
imaging air Cherenkov telescopes. Monte Carlo simulation was performed to
analyse images of gamma-rays and background particles (protons) as well as
estimate identification accuracy. Further steps of implementation and
improvement of this technique are discussed.
Towards generative adversarial networks as a new paradigm for radiology education
Authors: Samuel G. Finlayson, Hyunkwang Lee, Isaac S. Kohane, Luke Oakden-Rayner
Date: 2018-12-04 00:00:00
Tasks: Generative Adversarial Network

Medical students and radiology trainees typically view thousands of images in
order to "train their eye" to detect the subtle visual patterns necessary for
diagnosis. Nevertheless, infrastructural and legal constraints often make it
difficult to access and quickly query an abundance of images with a
user-specified feature set. In this paper, we use a conditional generative
adversarial network (GAN) to synthesize $1024\times1024$ pixel pelvic
radiographs that can be queried with conditioning on fracture status. We
demonstrate that the conditional GAN learns features that distinguish fractures
from non-fractures by training a convolutional neural network exclusively on
images sampled from the GAN and achieving an AUC of $>0.95$ on a held-out set
of real images. We conduct additional analysis of the images sampled from the
GAN and describe ongoing work to validate educational efficacy.
LSCP: Locally Selective Combination in Parallel Outlier Ensembles
Authors: Yue Zhao, Zain Nasrullah, Maciej K. Hryniewicki, Zheng Li
Date: 2018-12-04 00:00:00
Tasks: Anomaly Detection, Outlier Detection, outlier ensembles

In unsupervised outlier ensembles, the absence of ground truth makes the
combination of base outlier detectors a challenging task. Specifically,
existing parallel outlier ensembles lack a reliable way of selecting competent
base detectors, affecting accuracy and stability, during model combination. In
this paper, we propose a framework---called Locally Selective Combination in
Parallel Outlier Ensembles (LSCP)---which addresses the issue by defining a
local region around a test instance using the consensus of its nearest
neighbors in randomly selected feature subspaces. The top-performing base
detectors in this local region are selected and combined as the model's final
output. Four variants of the LSCP framework are compared with seven widely used
parallel frameworks. Experimental results demonstrate that one of these
variants, LSCP_AOM, consistently outperforms baselines on the majority of
twenty real-world datasets.
Leveraging Multi-grained Sentiment Lexicon Information for Neural Sequence Models
Authors: Yan Zeng, YangYang Lan, Yazhou Hao, Chen Li, Qinhua Zheng
Date: 2018-12-04 00:00:00
Tasks: General Classification, Negation, Sentence, Sentiment Analysis, Sentiment Classification

Neural sequence models have achieved great success in sentence-level sentiment classification. However, some models are exceptionally complex or based on expensive features. Some other models recognize the value of existed linguistic resource but utilize it insufficiently. This paper proposes a novel and general method to incorporate lexicon information, including sentiment lexicons(+/-), negation words and intensifiers. Words are annotated in fine-grained and coarse-grained labels. The proposed method first encodes the fine-grained labels into sentiment embedding and concatenates it with word embedding. Second, the coarse-grained labels are utilized to enhance the attention mechanism to give large weight on sentiment-related words. Experimental results show that our method can increase classification accuracy for neural sequence models on both SST-5 and MR dataset. Specifically, the enhanced Bi-LSTM model can even compare with a Tree-LSTM which uses expensive phrase-level annotations. Further analysis shows that in most cases the lexicon resource can offer the right annotations. Besides, the proposed method is capable of overcoming the effect from inevitably wrong annotations.
A Face-to-Face Neural Conversation Model
Authors: Hang Chu, Daiqing Li, Sanja Fidler
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, model

Neural networks have recently become good at engaging in dialog. However,
current approaches are based solely on verbal text, lacking the richness of a
real face-to-face conversation. We propose a neural conversation model that
aims to read and generate facial gestures alongside with text. This allows our
model to adapt its response based on the "mood" of the conversation. In
particular, we introduce an RNN encoder-decoder that exploits the movement of
facial muscles, as well as the verbal conversation. The decoder consists of two
layers, where the lower layer aims at generating the verbal response and coarse
facial expressions, while the second layer fills in the subtle gestures, making
the generated output more smooth and natural. We train our neural network by
having it "watch" 250 movies. We showcase our joint face-text model in
generating more natural conversations through automatic metrics and a human
study. We demonstrate an example application with a face-to-face chatting
avatar.
SurfConv: Bridging 3D and 2D Convolution for RGBD Images
Authors: Hang Chu, Wei-Chiu Ma, Kaustav Kundu, Raquel Urtasun, Sanja Fidler
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Semantic Segmentation, Semantic Segmentation

We tackle the problem of using 3D information in convolutional neural
networks for down-stream recognition tasks. Using depth as an additional
channel alongside the RGB input has the scale variance problem present in image
convolution based approaches. On the other hand, 3D convolution wastes a large
amount of memory on mostly unoccupied 3D space, which consists of only the
surface visible to the sensor. Instead, we propose SurfConv, which "slides"
compact 2D filters along the visible 3D surface. SurfConv is formulated as a
simple depth-aware multi-scale 2D convolution, through a new Data-Driven Depth
Discretization (D4) scheme. We demonstrate the effectiveness of our method on
indoor and outdoor 3D semantic segmentation datasets. Our method achieves
state-of-the-art performance with less than 30% parameters used by the 3D
convolution-based approaches.
Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels
Authors: Pawel Korus, Nasir Memon
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Manipulation, Image Manipulation Detection

Forensic analysis of digital photo provenance relies on intrinsic traces left
in the photograph at the time of its acquisition. Such analysis becomes
unreliable after heavy post-processing, such as down-sampling and
re-compression applied upon distribution in the Web. This paper explores
end-to-end optimization of the entire image acquisition and distribution
workflow to facilitate reliable forensic analysis at the end of the
distribution channel. We demonstrate that neural imaging pipelines can be
trained to replace the internals of digital cameras, and jointly optimized for
high-fidelity photo development and reliable provenance analysis. In our
experiments, the proposed approach increased image manipulation detection
accuracy from 45% to over 90%. The findings encourage further research towards
building more reliable imaging pipelines with explicit provenance-guaranteeing
properties.
Continuous User Authentication by Contactless Wireless Sensing
Authors: Fei Wang, Zhenjiang Li, Jinsong Han
Date: 2018-12-04 00:00:00
Tasks: User Identification

This paper presents BodyPIN, which is a continuous user authentication system
by contactless wireless sensing using commodity Wi-Fi. BodyPIN can track the
current user's legal identity throughout a computer system's execution. In case
the authentication fails, the consequent accesses will be denied to protect the
system. The recent rich wireless-based user identification designs cannot be
applied to BodyPIN directly, because they identify a user's various activities,
rather than the user herself. The enforced to be performed activities can thus
interrupt the user's operations on the system, highly inconvenient and not
user-friendly. In this paper, we leverage the bio-electromagnetics domain human
model for quantifying the impact of human body on the bypassing Wi-Fi signals
and deriving the component that indicates a user's identity. Then we extract
suitable Wi-Fi signal features to fully represent such an identity component,
based on which we fulfill the continuous user authentication design. We
implement a BodyPIN prototype by commodity Wi-Fi NICs without any extra or
dedicated wireless hardware. We show that BodyPIN achieves promising
authentication performances, which is also lightweight and robust under various
practical settings.
Domain Attentive Fusion for End-to-end Dialect Identification with Unknown Target Domain
Authors: Suwon Shon, Ahmed Ali, James Glass
Date: 2018-12-04 00:00:00
Tasks: Dialect Identification, Language Identification

End-to-end deep learning language or dialect identification systems operate on the spectrogram or other acoustic feature and directly generate identification scores for each class. An important issue for end-to-end systems is to have some knowledge of the application domain, because the system can be vulnerable to use cases that were not seen in the training phase; such a scenario is often referred to as a domain mismatched condition. In general, we assume that there is enough variation in the training dataset to expose the system to multiple domains. In this work, we study how to best make use a training dataset in order to have maximum effectiveness on unknown target domains. Our goal is to process the input without any knowledge of the target domain while preserving robust performance on other domains as well. To accomplish this objective, we propose a domain attentive fusion approach for end-to-end dialect/language identification systems. To help with experimentation, we collect a dataset from three different domains, and create experimental protocols for a domain mismatched condition. The results of our proposed approach, which were tested on a variety of broadcast and YouTube data, shows significant performance gain compared to traditional approaches, even without any prior target domain information.
Topological Map Extraction from Overhead Images
Authors: Zuoyue Li, Jan Dirk Wegner, Aurélien Lucchi
Date: 2018-12-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: Semantic Segmentation

We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly. PolyMapper directly extracts the topological map of a city from overhead images as collections of building footprints and road networks. In order to unify the shape representation for different types of objects, we also propose a novel sequentialization method that reformulates a graph structure as closed polygons. Experiments are conducted on both existing and self-collected large-scale datasets of several cities. Our empirical results demonstrate that our end-to-end learnable model is capable of drawing polygons of building footprints and road networks that very closely approximate the structure of existing online map services, in a fully automated manner. Quantitative and qualitative comparison to the state-of-the-art also shows that our approach achieves good levels of performance. To the best of our knowledge, the automatic extraction of large-scale topological maps is a novel contribution in the remote sensing community that we believe will help develop models with more informed geometrical constraints.
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
Authors: Wenwen Li, Jian Lou, Shuo Zhou, Haiping Lu
Date: 2018-12-04 00:00:00
Tasks: regression

While functional magnetic resonance imaging (fMRI) is important for
healthcare/neuroscience applications, it is challenging to classify or
interpret due to its multi-dimensional structure, high dimensionality, and
small number of samples available. Recent sparse multilinear regression methods
based on tensor are emerging as promising solutions for fMRI, yet existing
works rely on unfolding/folding operations and a tensor rank relaxation with
limited tightness. The newly proposed tensor singular value decomposition
(t-SVD) sheds light on new directions. In this work, we study t-SVD for sparse
multilinear regression and propose a Sparse tubal-regularized multilinear
regression (Sturm) method for fMRI. Specifically, the Sturm model performs
multilinear regression with two regularization terms: a tubal tensor nuclear
norm based on t-SVD and a standard L1 norm. We further derive the algorithm
under the alternating direction method of multipliers framework. We perform
experiments on four classification problems, including both resting-state fMRI
for disease diagnosis and task-based fMRI for neural decoding. The results show
the superior performance of Sturm in classifying fMRI using just a small number
of voxels.
Expanding search in the space of empirical ML
Authors: Bronwyn Woods
Date: 2018-12-04 00:00:00
Tasks: BIG-bench Machine Learning

As researchers and practitioners of applied machine learning, we are given a
set of requirements on the problem to be solved, the plausibly obtainable data,
and the computational resources available. We aim to find (within those bounds)
reliably useful combinations of problem, data, and algorithm. An emphasis on
algorithmic or technical novelty in ML conference publications leads to
exploration of one dimension of this space. Data collection and ML deployment
at scale in industry settings offers an environment for exploring the others.
Our conferences and reviewing criteria can better support empirical ML by
soliciting and incentivizing experimentation and synthesis independent of
algorithmic innovation.
Natural Option Critic
Authors: Saket Tiwari, Philip S. Thomas
Date: 2018-12-04 00:00:00
Tasks: Hierarchical Reinforcement Learning, Reinforcement Learning

The recently proposed option-critic architecture Bacon et al. provide a
stochastic policy gradient approach to hierarchical reinforcement learning.
Specifically, they provide a way to estimate the gradient of the expected
discounted return with respect to parameters that define a finite number of
temporally extended actions, called \textit{options}. In this paper we show how
the option-critic architecture can be extended to estimate the natural gradient
of the expected discounted return. To this end, the central questions that we
consider in this paper are: 1) what is the definition of the natural gradient
in this context, 2) what is the Fisher information matrix associated with an
option's parameterized policy, 3) what is the Fisher information matrix
associated with an option's parameterized termination function, and 4) how can
a compatible function approximation approach be leveraged to obtain natural
gradient estimates for both the parameterized policy and parameterized
termination functions of an option with per-time-step time and space complexity
linear in the total number of parameters. Based on answers to these questions
we introduce the natural option critic algorithm. Experimental results showcase
improvement over the vanilla gradient approach.
Hyperbolic Embeddings for Learning Options in Hierarchical Reinforcement Learning
Authors: Saket Tiwari, M. Prannoy
Date: 2018-12-04 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical reinforcement learning deals with the problem of breaking down
large tasks into meaningful sub-tasks. Autonomous discovery of these sub-tasks
has remained a challenging problem. We propose a novel method of learning
sub-tasks by combining paradigms of routing in computer networks and graph
based skill discovery within the options framework to define meaningful
sub-goals. We apply the recent advancements of learning embeddings using
Riemannian optimisation in the hyperbolic space to embed the state set into the
hyperbolic space and create a model of the environment. In doing so we enforce
a global topology on the states and are able to exploit this topology to learn
meaningful sub-tasks. We demonstrate empirically, both in discrete and
continuous domains, how these embeddings can improve the learning of meaningful
sub-tasks.