pwc-archive/papers-with-abstracts huggingface dataset:
Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network
Authors: Chun-Ren Phang, Chee-Ming Ting, Fuad Noman, Hernando Ombao
Date: 2019-03-21 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), Functional Connectivity, General Classification

We exploit altered patterns in brain functional connectivity as features for
automatic discriminative analysis of neuropsychiatric patients. Deep learning
methods have been introduced to functional network classification only very
recently for fMRI, and the proposed architectures essentially focused on a
single type of connectivity measure. We propose a deep convolutional neural
network (CNN) framework for classification of electroencephalogram
(EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary
aspects of disrupted connectivity in SZ, we explore combination of various
connectivity features consisting of time and frequency-domain metrics of
effective connectivity based on vector autoregressive model and partial
directed coherence, and complex network measures of network topology. We design
a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of
1D and 2D CNNs to integrate the features from various domains and dimensions
using different fusion strategies. Hierarchical latent representations learned
by the multiple convolutional layers from EEG connectivity reveal apparent
group differences between SZ and healthy controls (HC). Results on a large
resting-state EEG dataset show that the proposed CNNs significantly outperform
traditional support vector machine classifiers. The MDC-CNN with combined
connectivity features further improves performance over single-domain CNNs
using individual features, achieving remarkable accuracy of $93.06\%$ with a
decision-level fusion. The proposed MDC-CNN by integrating information from
diverse brain connectivity descriptors is able to accurately discriminate SZ
from HC. The new framework is potentially useful for developing diagnostic
tools for SZ and other disorders.
OverSketched Newton: Fast Convex Optimization for Serverless Systems
Authors: Vipul Gupta, Swanand Kadhe, Thomas Courtade, Michael W. Mahoney, Kannan Ramchandran
Date: 2019-03-21 00:00:00
Tasks: Distributed Optimization

Motivated by recent developments in serverless systems for large-scale computation as well as improvements in scalable randomized matrix algorithms, we develop OverSketched Newton, a randomized Hessian-based optimization algorithm to solve large-scale convex optimization problems in serverless systems. OverSketched Newton leverages matrix sketching ideas from Randomized Numerical Linear Algebra to compute the Hessian approximately. These sketching methods lead to inbuilt resiliency against stragglers that are a characteristic of serverless architectures. Depending on whether the problem is strongly convex or not, we propose different iteration updates using the approximate Hessian. For both cases, we establish convergence guarantees for OverSketched Newton and empirically validate our results by solving large-scale supervised learning problems on real-world datasets. Experiments demonstrate a reduction of ~50% in total running time on AWS Lambda, compared to state-of-the-art distributed optimization schemes.
Linguistic Knowledge and Transferability of Contextual Representations
Authors: Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, Noah A. Smith
Date: 2019-03-21 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling

Contextual word representations derived from large-scale neural language
models are successful across a diverse set of NLP tasks, suggesting that they
encode useful and transferable features of language. To shed light on the
linguistic knowledge they capture, we study the representations produced by
several recent pretrained contextualizers (variants of ELMo, the OpenAI
transformer language model, and BERT) with a suite of seventeen diverse probing
tasks. We find that linear models trained on top of frozen contextual
representations are competitive with state-of-the-art task-specific models in
many cases, but fail on tasks requiring fine-grained linguistic knowledge
(e.g., conjunct identification). To investigate the transferability of
contextual word representations, we quantify differences in the transferability
of individual layers within contextualizers, especially between recurrent
neural networks (RNNs) and transformers. For instance, higher layers of RNNs
are more task-specific, while transformer layers do not exhibit the same
monotonic trend. In addition, to better understand what makes contextual word
representations transferable, we compare language model pretraining with eleven
supervised pretraining tasks. For any given task, pretraining on a closely
related task yields better performance than language model pretraining (which
is better on average) when the pretraining dataset is fixed. However, language
model pretraining on more data gives the best results.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Authors: Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
Date: 2019-03-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: Stochastic Optimization

Sorting input objects is an important step in many machine learning
pipelines. However, the sorting operator is non-differentiable with respect to
its inputs, which prohibits end-to-end gradient-based optimization. In this
work, we propose NeuralSort, a general-purpose continuous relaxation of the
output of the sorting operator from permutation matrices to the set of unimodal
row-stochastic matrices, where every row sums to one and has a distinct arg
max. This relaxation permits straight-through optimization of any computational
graph involve a sorting operation. Further, we use this relaxation to enable
gradient-based stochastic optimization over the combinatorially large space of
permutations by deriving a reparameterized gradient estimator for the
Plackett-Luce family of distributions over permutations. We demonstrate the
usefulness of our framework on three tasks that require learning semantic
orderings of high-dimensional objects, including a fully differentiable,
parameterized extension of the k-nearest neighbors algorithm.
Parametic Classification of Handvein Patterns Based on Texture Features
Authors: Harbi AlMahafzah, Mohammad Imranand, Supreetha Gowda H. D.
Date: 2019-03-21 00:00:00
Tasks: Classification, General Classification

In this paper, we have developed Biometric recognition system adopting hand
based modality Handvein, which has the unique pattern for each individual and
it is impossible to counterfeit and fabricate as it is an internal feature. We
have opted in choosing feature extraction algorithms such as LBP-visual
descriptor ,LPQ-blur insensitive texture operator, Log-Gabor-Texture
descriptor. We have chosen well known classifiers such as KNN and SVM for
classification. We have experimented and tabulated results of single algorithm
recognition rate for Handvein under different distance measures and kernel
options. The feature level fusion is carried out which increased the
performance level.
Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation
Authors: Xipeng Chen, Kwan-Yee Lin, Wentao Liu, Chen Qian, Xiaogang Wang, Liang Lin
Date: 2019-03-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, 3D Human Pose Estimation, Decoder, Pose Estimation

Recent studies have shown remarkable advances in 3D human pose estimation
from monocular images, with the help of large-scale in-door 3D datasets and
sophisticated network architectures. However, the generalizability to different
environments remains an elusive goal. In this work, we propose a geometry-aware
3D representation for the human pose to address this limitation by using
multiple views in a simple auto-encoder model at the training stage and only 2D
keypoint information as supervision. A view synthesis framework is proposed to
learn the shared 3D representation between viewpoints with synthesizing the
human pose from one viewpoint to the other one. Instead of performing a direct
transfer in the raw image-level, we propose a skeleton-based encoder-decoder
mechanism to distil only pose-related representation in the latent space. A
learning-based representation consistency constraint is further introduced to
facilitate the robustness of latent 3D representation. Since the learnt
representation encodes 3D geometry information, mapping it to 3D pose will be
much easier than conventional frameworks that use an image or 2D coordinates as
the input of 3D pose estimator. We demonstrate our approach on the task of 3D
human pose estimation. Comprehensive experiments on three popular benchmarks
show that our model can significantly improve the performance of
state-of-the-art methods with simply injecting the representation as a robust
3D prior.
Towards Robust Curve Text Detection with Conditional Spatial Expansion
Authors: Zichuan Liu, Guosheng Lin, Sheng Yang, Fayao Liu, Weisi Lin, Wang Ling Goh
Date: 2019-03-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection, Text Detection

It is challenging to detect curve texts due to their irregular shapes and
varying sizes. In this paper, we first investigate the deficiency of the
existing curve detection methods and then propose a novel Conditional Spatial
Expansion (CSE) mechanism to improve the performance of curve text detection.
Instead of regarding the curve text detection as a polygon regression or a
segmentation problem, we treat it as a region expansion process. Our CSE starts
with a seed arbitrarily initialized within a text region and progressively
merges neighborhood regions based on the extracted local features by a CNN and
contextual information of merged regions. The CSE is highly parameterized and
can be seamlessly integrated into existing object detection frameworks.
Enhanced by the data-dependent CSE mechanism, our curve text detection system
provides robust instance-level text region extraction with minimal
post-processing. The analysis experiment shows that our CSE can handle texts
with various shapes, sizes, and orientations, and can effectively suppress the
false-positives coming from text-like textures or unexpected texts included in
the same RoI. Compared with the existing curve text detection algorithms, our
method is more robust and enjoys a simpler processing flow. It also creates a
new state-of-art performance on curve text benchmarks with F-score of up to
78.4$\%$.
Non-target Structural Displacement Measurement Using Reference Frame Based Deepflow
Authors: Jongbin Won, Jong-Woong Park, Do-Soo Moon
Date: 2019-03-21 00:00:00
Tasks: Structural Health Monitoring

Structural displacement is crucial for structural health monitoring, although
it is very challenging to measure in field conditions. Most existing
displacement measurement methods are costly, labor intensive, and
insufficiently accurate for measuring small dynamic displacements. Computer
vision (CV) based methods incorporate optical devices with advanced image
processing algorithms to accurately, cost-effectively, and remotely measure
structural displacement with easy installation. However, non-target based CV
methods are still limited by insufficient feature points, incorrect feature
point detection, occlusion, and drift induced by tracking error accumulation.
This paper presents a reference frame based Deepflow algorithm integrated with
masking and signal filtering for non-target based displacement measurements.
The proposed method allows the user to select points of interest for images
with a low gradient for displacement tracking and directly calculate
displacement without drift accumulated by measurement error. The proposed
method is experimentally validated on a cantilevered beam under ambient and
occluded test conditions. The accuracy of the proposed method is compared with
that of a reference laser displacement sensor for validation. The significant
advantage of the proposed method is its flexibility in extracting structural
displacement in any region on structures that do not have distinct natural
features.
Exact slice sampler for Hierarchical Dirichlet Processes
Authors: Arash A. Amini, Marina Paez, Lizhen Lin, Zahra S. Razaee
Date: 2019-03-21 00:00:00

We propose an exact slice sampler for Hierarchical Dirichlet process (HDP)
and its associated mixture models (Teh et al., 2006). Although there are
existing MCMC algorithms for sampling from the HDP, a slice sampler has been
missing from the literature. Slice sampling is well-known for its desirable
properties including its fast mixing and its natural potential for
parallelization. On the other hand, the hierarchical nature of HDPs poses
challenges to adopting a full-fledged slice sampler that automatically
truncates all the infinite measures involved without ad-hoc modifications. In
this work, we adopt the powerful idea of Bayesian variable augmentation to
address this challenge. By introducing new latent variables, we obtain a full
factorization of the joint distribution that is suitable for slice sampling.
Our algorithm has several appealing features such as (1) fast mixing; (2)
remaining exact while allowing natural truncation of the underlying
infinite-dimensional measures, as in (Kalli et al., 2011), resulting in updates
of only a finite number of necessary atoms and weights in each iteration; and
(3) being naturally suited to parallel implementations. The underlying
principle for joint factorization of the full likelihood is simple and can be
applied to many other settings, such as designing sampling algorithms for
general dependent Dirichlet process (DDP) models.
Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data
Authors: Caoqiang Liu, Hui Ji, Anqi Qiu
Date: 2019-03-21 00:00:00

We developed a convolution neural network (CNN) on semi-regular triangulated
meshes whose vertices have 6 neighbours. The key blocks of the proposed CNN,
including convolution and down-sampling, are directly defined in a vertex
domain. By exploiting the ordering property of semi-regular meshes, the
convolution is defined on a vertex domain with strong motivation from the
spatial definition of classic convolution. Moreover, the down-sampling of a
semi-regular mesh embedded in a 3D Euclidean space can achieve a down-sampling
rate of 4, 16, 64, etc. We demonstrated the use of this vertex-based graph CNN
for the classification of mild cognitive impairment (MCI) and Alzheimer's
disease (AD) based on 3169 MRI scans of the Alzheimer's Disease Neuroimaging
Initiative (ADNI). We compared the performance of the vertex-based graph CNN
with that of the spectral graph CNN.
Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration
Authors: Xing Liu, Masanori Suganuma, Zhun Sun, Takayuki Okatani
Date: 2019-03-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Restoration

In this paper, we study design of deep neural networks for tasks of image
restoration. We propose a novel style of residual connections dubbed "dual
residual connection", which exploits the potential of paired operations, e.g.,
up- and down-sampling or convolution with large- and small-size kernels. We
design a modular block implementing this connection style; it is equipped with
two containers to which arbitrary paired operations are inserted. Adopting the
"unraveled" view of the residual networks proposed by Veit et al., we point out
that a stack of the proposed modular blocks allows the first operation in a
block interact with the second operation in any subsequent blocks. Specifying
the two operations in each of the stacked blocks, we build a complete network
for each individual task of image restoration. We experimentally evaluate the
proposed approach on five image restoration tasks using nine datasets. The
results show that the proposed networks with properly chosen paired operations
outperform previous methods on almost all of the tasks and datasets.
Empirical Evaluations of Seed Set Selection Strategies for Predictive Coding
Authors: Christian J. Mahoney, Nathaniel Huber-Fliflet, Katie Jensen, Haozhen Zhao, Robert Neary, Shi Ye
Date: 2019-03-21 00:00:00

Training documents have a significant impact on the performance of predictive
models in the legal domain. Yet, there is limited research that explores the
effectiveness of the training document selection strategy - in particular, the
strategy used to select the seed set, or the set of documents an attorney
reviews first to establish an initial model. Since there is limited research on
this important component of predictive coding, the authors of this paper set
out to identify strategies that consistently perform well. Our research
demonstrated that the seed set selection strategy can have a significant impact
on the precision of a predictive model. Enabling attorneys with the results of
this study will allow them to initiate the most effective predictive modeling
process to comb through the terabytes of data typically present in modern
litigation. This study used documents from four actual legal cases to evaluate
eight different seed set selection strategies. Attorneys can use the results
contained within this paper to enhance their approach to predictive coding.
Prostate Segmentation from Ultrasound Images using Residual Fully Convolutional Network
Authors: M. S. Hossain, A. P. Paplinski, J. M. Betts
Date: 2019-03-21 00:00:00
Tasks: Segmentation

Medical imaging based prostate cancer diagnosis procedure uses
intra-operative transrectal ultrasound (TRUS) imaging to visualize the prostate
shape and location to collect tissue samples. Correct tissue sampling from
prostate requires accurate prostate segmentation in TRUS images. To achieve
this, this study uses a novel residual connection based fully convolutional
network. The advantage of this segmentation technique is that it requires no
pre-processing of TRUS images to perform the segmentation. Thus, it offers a
faster and straightforward prostate segmentation from TRUS images. Results show
that the proposed technique can achieve around 86% Dice Similarity accuracy
using only few TRUS datasets.
Networks for Joint Affine and Non-parametric Image Registration
Authors: Zhengyang Shen, Xu Han, Zhenlin Xu, Marc Niethammer
Date: 2019-03-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Registration, Medical Image Registration

We introduce an end-to-end deep-learning framework for 3D medical image
registration. In contrast to existing approaches, our framework combines two
registration methods: an affine registration and a vector
momentum-parameterized stationary velocity field (vSVF) model. Specifically, it
consists of three stages. In the first stage, a multi-step affine network
predicts affine transform parameters. In the second stage, we use a Unet-like
network to generate a momentum, from which a velocity field can be computed via
smoothing. Finally, in the third stage, we employ a self-iterable map-based
vSVF component to provide a non-parametric refinement based on the current
estimate of the transformation map. Once the model is trained, a registration
is completed in one forward pass. To evaluate the performance, we conducted
longitudinal and cross-subject experiments on 3D magnetic resonance images
(MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show
that our framework achieves comparable performance to state-of-the-art medical
image registration approaches, but it is much faster, with a better control of
transformation regularity including the ability to produce approximately
symmetric transformations, and combining affine and non-parametric
registration.
A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain
Authors: Tao Wang
Date: 2019-03-21 00:00:00
Tasks: BIG-bench Machine Learning

Traditional machine learning algorithms use data from databases that are
mutable, and therefore the data cannot be fully trusted. Also, the machine
learning process is difficult to automate. This paper proposes building a
trustable machine learning system by using blockchain technology, which can
store data in a permanent and immutable way. In addition, smart contracts are
used to automate the machine learning process. This paper makes three
contributions. First, it establishes a link between machine learning technology
and blockchain technology. Previously, machine learning and blockchain have
been considered two independent technologies without an obvious link. Second,
it proposes a unified analytical framework for trustable machine learning by
using blockchain technology. This unified framework solves both the
trustability and automation issues in machine learning. Third, it enables a
computer to translate core machine learning implementation from a single thread
on a single machine to multiple threads on multiple machines running with
blockchain by using a unified approach. The paper uses association rule mining
as an example to demonstrate how trustable machine learning can be implemented
with blockchain, and it shows how this approach can be used to analyze opioid
prescriptions to help combat the opioid crisis.
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
Authors: Richard Cheng, Gabor Orosz, Richard M. Murray, Joel W. Burdick
Date: 2019-03-21 00:00:00
Tasks: continuous-control, Continuous Control, Gaussian Processes, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Reinforcement Learning (RL) algorithms have found limited success beyond
simulated applications, and one main reason is the absence of safety guarantees
during the learning process. Real world systems would realistically fail or
break before an optimal controller can be learned. To address this issue, we
propose a controller architecture that combines (1) a model-free RL-based
controller with (2) model-based controllers utilizing control barrier functions
(CBFs) and (3) on-line learning of the unknown system dynamics, in order to
ensure safety during learning. Our general framework leverages the success of
RL algorithms to learn high-performance controllers, while the CBF-based
controllers both guarantee safety and guide the learning process by
constraining the set of explorable polices. We utilize Gaussian Processes (GPs)
to model the system dynamics and its uncertainties.
  Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high
probability during the learning process, regardless of the RL algorithm used,
and demonstrates greater policy exploration efficiency. We test our algorithm
on (1) control of an inverted pendulum and (2) autonomous car-following with
wireless vehicle-to-vehicle communication, and show that our algorithm attains
much greater sample efficiency in learning than other state-of-the-art
algorithms and maintains safety during the entire learning process.