pwc-archive/papers-with-abstracts huggingface dataset:
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network
Authors: Christopher J. Holder, Toby P. Breckon, Xiong Wei
Date: 2018-01-04 00:00:00
Tasks: Autonomous Vehicles, road scene understanding, Scene Understanding, Semantic Segmentation, Stereo Matching, Stereo Matching Hand

Scene understanding for autonomous vehicles is a challenging computer vision
task, with recent advances in convolutional neural networks (CNNs) achieving
results that notably surpass prior traditional feature driven approaches.
However, limited work investigates the application of such methods either
within the highly unstructured off-road environment or to RGBD input data. In
this work, we take an existing CNN architecture designed to perform semantic
segmentation of RGB images of urban road scenes, then adapt and retrain it to
perform the same task with multichannel RGBD images obtained under a range of
challenging off-road conditions. We compare two different stereo matching
algorithms and five different methods of encoding depth information, including
disparity, local normal orientation and HHA (horizontal disparity, height above
ground plane, angle with gravity), to create a total of ten experimental
variations of our dataset, each of which is used to train and test a CNN so
that classification performance can be evaluated against a CNN trained using
standard RGB input.
A Decision-theoretic Approach to Detection-based Target Search with a UAV
Authors: Aayush Gupta, Daniel Bessonov, Patrick Li
Date: 2018-01-04 00:00:00

Search and rescue missions and surveillance require finding targets in a
large area. These tasks often use unmanned aerial vehicles (UAVs) with cameras
to detect and move towards a target. However, common UAV approaches make two
simplifying assumptions. First, they assume that observations made from
different heights are deterministically correct. In practice, observations are
noisy, with the noise increasing as the height used for observations increases.
Second, they assume that a motion command executes correctly, which may not
happen due to wind and other environmental factors. To address these, we
propose a sequential algorithm that determines actions in real time based on
observations, using partially observable Markov decision processes (POMDPs).
Our formulation handles both observations and motion uncertainty and errors. We
run offline simulations and learn a policy. This policy is run on a UAV to find
the target efficiently. We employ a novel compact formulation to represent the
coordinates of the drone relative to the target coordinates. Our POMDP policy
finds the target up to 3.4 times faster when compared to a heuristic policy.
Bayesian Estimation of Multidimensional Latent Variables and Its Asymptotic Accuracy
Authors: Keisuke Yamazaki
Date: 2015-10-05 00:00:00
Tasks: Clustering

Hierarchical learning models, such as mixture models and Bayesian networks,
are widely employed for unsupervised learning tasks, such as clustering
analysis. They consist of observable and hidden variables, which represent the
given data and their hidden generation process, respectively. It has been
pointed out that conventional statistical analysis is not applicable to these
models, because redundancy of the latent variable produces singularities in the
parameter space. In recent years, a method based on algebraic geometry has
allowed us to analyze the accuracy of predicting observable variables when
using Bayesian estimation. However, how to analyze latent variables has not
been sufficiently studied, even though one of the main issues in unsupervised
learning is to determine how accurately the latent variable is estimated. A
previous study proposed a method that can be used when the range of the latent
variable is redundant compared with the model generating data. The present
paper extends that method to the situation in which the latent variables have
redundant dimensions. We formulate new error functions and derive their
asymptotic forms. Calculation of the error functions is demonstrated in
two-layered Bayesian networks.
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
Authors: Mohammad Haft-Javaherian, Linjing Fang, Victorine Muse, Chris B. Schaffer, Nozomi Nishimura, Mert R. Sabuncu
Date: 2018-01-03 00:00:00
Tasks: Segmentation

The health and function of tissue rely on its vasculature network to provide
reliable blood perfusion. Volumetric imaging approaches, such as multiphoton
microscopy, are able to generate detailed 3D images of blood vessels that could
contribute to our understanding of the role of vascular structure in normal
physiology and in disease mechanisms. The segmentation of vessels, a core image
analysis problem, is a bottleneck that has prevented the systematic comparison
of 3D vascular architecture across experimental populations. We explored the
use of convolutional neural networks to segment 3D vessels within volumetric in
vivo images acquired by multiphoton microscopy. We evaluated different network
architectures and machine learning techniques in the context of this
segmentation problem. We show that our optimized convolutional neural network
architecture, which we call DeepVess, yielded a segmentation accuracy that was
better than both the current state-of-the-art and a trained human annotator,
while also being orders of magnitude faster. To explore the effects of aging
and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of
cortical blood vessels in young and old mouse models of Alzheimer's disease and
wild type littermates. We found little difference in the distribution of
capillary diameter or tortuosity between these groups, but did note a decrease
in the number of longer capillary segments ($>75\mu m$) in aged animals as
compared to young, in both wild type and Alzheimer's disease mouse models.
Generalized Similarity U: A Non-parametric Test of Association Based on Similarity
Authors: Changshuai Wei, Qing Lu
Date: 2018-01-04 00:00:00

Second generation sequencing technologies are being increasingly used for
genetic association studies, where the main research interest is to identify
sets of genetic variants that contribute to various phenotype. The phenotype
can be univariate disease status, multivariate responses and even
high-dimensional outcomes. Considering the genotype and phenotype as two
complex objects, this also poses a general statistical problem of testing
association between complex objects. We here proposed a similarity-based test,
generalized similarity U (GSU), that can test the association between complex
objects. We first studied the theoretical properties of the test in a general
setting and then focused on the application of the test to sequencing
association studies. Based on theoretical analysis, we proposed to use
Laplacian kernel based similarity for GSU to boost power and enhance
robustness. Through simulation, we found that GSU did have advantages over
existing methods in terms of power and robustness. We further performed a whole
genome sequencing (WGS) scan for Alzherimer Disease Neuroimaging Initiative
(ADNI) data, identifying three genes, APOE, APOC1 and TOMM40, associated with
imaging phenotype. We developed a C++ package for analysis of whole genome
sequencing data using GSU. The source codes can be downloaded at
https://github.com/changshuaiwei/gsu.
Accounting for hidden common causes when inferring cause and effect from observational data
Authors: David Heckerman
Date: 2018-01-02 00:00:00

Identifying causal relationships from observation data is difficult, in large
part, due to the presence of hidden common causes. In some cases, where just
the right patterns of conditional independence and dependence lie in the
data---for example, Y-structures---it is possible to identify cause and effect.
In other cases, the analyst deliberately makes an uncertain assumption that
hidden common causes are absent, and infers putative causal relationships to be
tested in a randomized trial. Here, we consider a third approach, where there
are sufficient clues in the data such that hidden common causes can be
inferred.
Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach
Authors: Theodora S. Brisimi, Tingting Xu, Taiyao Wang, Wuyang Dai, William G. Adams, Ioannis Ch. Paschalidis
Date: 2018-01-03 00:00:00
Tasks: Binary Classification, Clustering, General Classification

Urban living in modern large cities has significant adverse effects on
health, increasing the risk of several chronic diseases. We focus on the two
leading clusters of chronic disease, heart disease and diabetes, and develop
data-driven methods to predict hospitalizations due to these conditions. We
base these predictions on the patients' medical history, recent and more
distant, as described in their Electronic Health Records (EHR). We formulate
the prediction problem as a binary classification problem and consider a
variety of machine learning methods, including kernelized and sparse Support
Vector Machines (SVM), sparse logistic regression, and random forests. To
strike a balance between accuracy and interpretability of the prediction, which
is important in a medical setting, we propose two novel methods: K-LRT, a
likelihood ratio test-based method, and a Joint Clustering and Classification
(JCC) method which identifies hidden patient clusters and adapts classifiers to
each cluster. We develop theoretical out-of-sample guarantees for the latter
method. We validate our algorithms on large datasets from the Boston Medical
Center, the largest safety-net hospital system in New England.
Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
Authors: Ali Dabouei, Hadi Kazemi, Seyed Mehdi Iranmanesh, Jeremi Dawson, Nasser M. Nasrabadi
Date: 2018-01-03 00:00:00

Elastic distortion of fingerprints has a negative effect on the performance
of fingerprint recognition systems. This negative effect brings inconvenience
to users in authentication applications. However, in the negative recognition
scenario where users may intentionally distort their fingerprints, this can be
a serious problem since distortion will prevent recognition system from
identifying malicious users. Current methods aimed at addressing this problem
still have limitations. They are often not accurate because they estimate
distortion parameters based on the ridge frequency map and orientation map of
input samples, which are not reliable due to distortion. Secondly, they are not
efficient and requiring significant computation time to rectify samples. In
this paper, we develop a rectification model based on a Deep Convolutional
Neural Network (DCNN) to accurately estimate distortion parameters from the
input image. Using a comprehensive database of synthetic distorted samples, the
DCNN learns to accurately estimate distortion bases ten times faster than the
dictionary search methods used in the previous approaches. Evaluating the
proposed method on public databases of distorted samples shows that it can
significantly improve the matching performance of distorted samples.
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
Authors: Jinghui Chen, Lingxiao Wang, Xiao Zhang, Quanquan Gu
Date: 2017-04-20 00:00:00
Tasks: Retrieval

We consider the robust phase retrieval problem of recovering the unknown
signal from the magnitude-only measurements, where the measurements can be
contaminated by both sparse arbitrary corruption and bounded random noise. We
propose a new nonconvex algorithm for robust phase retrieval, namely Robust
Wirtinger Flow to jointly estimate the unknown signal and the sparse
corruption. We show that our proposed algorithm is guaranteed to converge
linearly to the unknown true signal up to a minimax optimal statistical
precision in such a challenging setting. Compared with existing robust phase
retrieval methods, we achieve an optimal sample complexity of $O(n)$ in both
noisy and noise-free settings. Thorough experiments on both synthetic and real
datasets corroborate our theory.
Parameter-free online learning via model selection
Authors: Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Date: 2017-12-30 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: model, Model Selection

We introduce an efficient algorithmic framework for model selection in online
learning, also known as parameter-free online learning. Departing from previous
work, which has focused on highly structured function classes such as nested
balls in Hilbert space, we propose a generic meta-algorithm framework that
achieves online model selection oracle inequalities under minimal structural
assumptions. We give the first computationally efficient parameter-free
algorithms that work in arbitrary Banach spaces under mild smoothness
assumptions; previous results applied only to Hilbert spaces. We further derive
new oracle inequalities for matrix classes, non-nested convex sets, and
$\mathbb{R}^{d}$ with generic regularizers. Finally, we generalize these
results by providing oracle inequalities for arbitrary non-linear classes in
the online supervised learning model. These results are all derived through a
unified meta-algorithm scheme using a novel "multi-scale" algorithm for
prediction with expert advice based on random playout, which may be of
independent interest.
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Authors: Aditya Grover, Manik Dhar, Stefano Ermon
Date: 2017-05-24 00:00:00
Tasks: Generative Adversarial Network

Adversarial learning of probabilistic models has recently emerged as a
promising alternative to maximum likelihood. Implicit models such as generative
adversarial networks (GAN) often generate better samples compared to explicit
models trained by maximum likelihood. Yet, GANs sidestep the characterization
of an explicit density which makes quantitative evaluations challenging. To
bridge this gap, we propose Flow-GANs, a generative adversarial network for
which we can perform exact likelihood evaluation, thus supporting both
adversarial and maximum likelihood training. When trained adversarially,
Flow-GANs generate high-quality samples but attain extremely poor
log-likelihood scores, inferior even to a mixture model memorizing the training
data; the opposite is true when trained by maximum likelihood. Results on MNIST
and CIFAR-10 demonstrate that hybrid training can attain high held-out
likelihoods while retaining visual fidelity in the generated samples.
Mitigating the Curse of Correlation in Security Games by Entropy Maximization
Authors: Haifeng Xu, Milind Tambe, Shaddin Dughmi, Venil Loyd Noronha
Date: 2017-03-11 00:00:00
Tasks: Scheduling

In Stackelberg security games, a defender seeks to randomly allocate limited
security resources to protect critical targets from an attack. In this paper,
we study a fundamental, yet underexplored, phenomenon in security games, which
we term the \emph{Curse of Correlation} (CoC). Specifically, we observe that
there are inevitable correlations among the protection status of different
targets. Such correlation is a crucial concern, especially in
\emph{spatio-temporal} domains like conservation area patrolling, where
attackers can surveil patrollers at certain areas and then infer their
patrolling routes using such correlations. To mitigate this issue, we propose
to design entropy-maximizing defending strategies for spatio-temporal security
games, which frequently suffer from CoC. We prove that the problem is \#P-hard
in general. However, it admits efficient algorithms in well-motivated special
settings. Our experiments show significant advantages of max-entropy algorithms
over previous algorithms. A scalable implementation of our algorithm is
currently under pre-deployment testing for integration into FAMS software to
improve the scheduling of US federal air marshals.
Coins and Logic
Authors: Tanya Khovanova
Date: 2018-01-03 00:00:00

We establish fun parallels between coin-weighing puzzles and
knights-and-knaves puzzles.
Variational auto-encoding of protein sequences
Authors: Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak
Date: 2017-12-09 00:00:00
Tasks: Protein Design

Proteins are responsible for the most diverse set of functions in biology.
The ability to extract information from protein sequences and to predict the
effects of mutations is extremely valuable in many domains of biology and
medicine. However the mapping between protein sequence and function is complex
and poorly understood. Here we present an embedding of natural protein
sequences using a Variational Auto-Encoder and use it to predict how mutations
affect protein function. We use this unsupervised approach to cluster natural
variants and learn interactions between sets of positions within a protein.
This approach generally performs better than baseline methods that consider no
interactions within sequences, and in some cases better than the
state-of-the-art approaches that use the inverse-Potts model. This generative
model can be used to computationally guide exploration of protein sequence
space and to better inform rational and automatic protein design.
Cortical microcircuits as gated-recurrent neural networks
Authors: Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford, Nando de Freitas, Tim P. Vogels
Date: 2017-11-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: image-classification, Image Classification, Language Modelling, Sequential Image Classification

Cortical circuits exhibit intricate recurrent architectures that are
remarkably similar across different brain areas. Such stereotyped structure
suggests the existence of common computational principles. However, such
principles have remained largely elusive. Inspired by gated-memory networks,
namely long short-term memory networks (LSTMs), we introduce a recurrent neural
network in which information is gated through inhibitory cells that are
subtractive (subLSTM). We propose a natural mapping of subLSTMs onto known
canonical excitatory-inhibitory cortical microcircuits. Our empirical
evaluation across sequential image classification and language modelling tasks
shows that subLSTM units can achieve similar performance to LSTM units. These
results suggest that cortical circuits can be optimised to solve complex
contextual problems and proposes a novel view on their computational function.
Overall our work provides a step towards unifying recurrent networks as used in
machine learning with their biological counterparts.
Learning More Universal Representations for Transfer-Learning
Authors: Youssef Tamaazousti, Hervé Le Borgne, Céline Hudelot, Mohamed El Amine Seddik, Mohamed Tamaazousti
Date: 2017-12-27 00:00:00
Tasks: Transfer Learning

A representation is supposed universal if it encodes any element of the
visual world (e.g., objects, scenes) in any configuration (e.g., scale,
context). While not expecting pure universal representations, the goal in the
literature is to improve the universality level, starting from a representation
with a certain level. To do so, the state-of-the-art consists in learning
CNN-based representations on a diversified training problem (e.g., ImageNet
modified by adding annotated data). While it effectively increases
universality, such approach still requires a large amount of efforts to satisfy
the needs in annotated data. In this work, we propose two methods to improve
universality, but pay special attention to limit the need of annotated data. We
also propose a unified framework of the methods based on the diversifying of
the training problem. Finally, to better match Atkinson's cognitive study about
universal human representations, we proposed to rely on the transfer-learning
scheme as well as a new metric to evaluate universality. This latter, aims us
to demonstrates the interest of our methods on 10 target-problems, relating to
the classification task and a variety of visual domains.
Development and evaluation of a deep learning model for protein-ligand binding affinity prediction
Authors: Marta M. Stepniewska-Dziubinska, Piotr Zielenkiewicz, Pawel Siedlecki
Date: 2017-12-19 00:00:00
Tasks: Drug Discovery

Structure based ligand discovery is one of the most successful approaches for
augmenting the drug discovery process. Currently, there is a notable shift
towards machine learning (ML) methodologies to aid such procedures. Deep
learning has recently gained considerable attention as it allows the model to
"learn" to extract features that are relevant for the task at hand. We have
developed a novel deep neural network estimating the binding affinity of
ligand-receptor complexes. The complex is represented with a 3D grid, and the
model utilizes a 3D convolution to produce a feature map of this
representation, treating the atoms of both proteins and ligands in the same
manner. Our network was tested on the CASF "scoring power" benchmark and Astex
Diverse Set and outperformed classical scoring functions. The model, together
with usage instructions and examples, is available as a git repository at
http://gitlab.com/cheminfIBB/pafnucy
Robust Real-Time Multi-View Eye Tracking
Authors: Nuri Murat Arar, Jean-Philippe Thiran
Date: 2017-11-15 00:00:00

Despite significant advances in improving the gaze tracking accuracy under
controlled conditions, the tracking robustness under real-world conditions,
such as large head pose and movements, use of eyeglasses, illumination and eye
type variations, remains a major challenge in eye tracking. In this paper, we
revisit this challenge and introduce a real-time multi-camera eye tracking
framework to improve the tracking robustness. First, differently from previous
work, we design a multi-view tracking setup that allows for acquiring multiple
eye appearances simultaneously. Leveraging multi-view appearances enables to
more reliably detect gaze features under challenging conditions, particularly
when they are obstructed in conventional single-view appearance due to large
head movements or eyewear effects. The features extracted on various
appearances are then used for estimating multiple gaze outputs. Second, we
propose to combine estimated gaze outputs through an adaptive fusion mechanism
to compute user's overall point of regard. The proposed mechanism firstly
determines the estimation reliability of each gaze output according to user's
momentary head pose and predicted gazing behavior, and then performs a
reliability-based weighted fusion. We demonstrate the efficacy of our framework
with extensive simulations and user experiments on a collected dataset
featuring 20 subjects. Our results show that in comparison with
state-of-the-art eye trackers, the proposed framework provides not only a
significant enhancement in accuracy but also a notable robustness. Our
prototype system runs at 30 frames-per-second (fps) and achieves 1 degree
accuracy under challenging experimental scenarios, which makes it suitable for
applications demanding high accuracy and robustness.
Intrinsic Gaussian processes on complex constrained domains
Authors: Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, David Dunson
Date: 2018-01-03 00:00:00
Tasks: Gaussian Processes, valid

We propose a class of intrinsic Gaussian processes (in-GPs) for
interpolation, regression and classification on manifolds with a primary focus
on complex constrained domains or irregular shaped spaces arising as subsets or
submanifolds of R, R2, R3 and beyond. For example, in-GPs can accommodate
spatial domains arising as complex subsets of Euclidean space. in-GPs respect
the potentially complex boundary or interior conditions as well as the
intrinsic geometry of the spaces. The key novelty of the proposed approach is
to utilise the relationship between heat kernels and the transition density of
Brownian motion on manifolds for constructing and approximating valid and
computationally feasible covariance kernels. This enables in-GPs to be
practically applied in great generality, while existing approaches for
smoothing on constrained domains are limited to simple special cases. The broad
utilities of the in-GP approach is illustrated through simulation studies and
data examples.