pwc-archive/papers-with-abstracts huggingface dataset:
Weighted Mean Curvature
Authors: Yuanhao Gong, Orcun Goksel
Date: 2019-03-17 00:00:00
Tasks: Bayesian Inference, GPU

In image processing tasks, spatial priors are essential for robust computations, regularization, algorithmic design and Bayesian inference. In this paper, we introduce weighted mean curvature (WMC) as a novel image prior and present an efficient computation scheme for its discretization in practical image processing applications. We first demonstrate the favorable properties of WMC, such as sampling invariance, scale invariance, and contrast invariance with Gaussian noise model; and we show the relation of WMC to area regularization. We further propose an efficient computation scheme for discretized WMC, which is demonstrated herein to process over 33.2 giga-pixels/second on GPU. This scheme yields itself to a convolutional neural network representation. Finally, WMC is evaluated on synthetic and real images, showing its superiority quantitatively to total-variation and mean curvature.
Knowledge Graph Development for App Store Data Modeling
Authors: Mariia Rizun, Artur Strzelecki
Date: 2019-03-17 00:00:00
Tasks: Clustering

Usage of mobile applications has become a part of our lives today, since every day we use our smartphones for communication, entertainment, business and education. High demand on apps has led to significant growth of supply, yet large offer has caused complications in users search of the one suitable application. The authors have made an attempt to solve the problem of facilitating the search in app stores. With the help of a website crawling software a sample of data was retrieved from one of the well-known mobile app stores and divided into 11 groups by types. These groups of data were used to construct a Knowledge Schema - a graphic model of interconnections of data that characterize any mobile app in the selected store. Schema creation is the first step in the process of developing a Knowledge Graph that will perform applications clustering to facilitate users search in app stores.
On the Computation and Applications of Large Dense Partial Correlation Networks
Authors: Keith Dillon
Date: 2019-03-17 00:00:00
Tasks: Dimensionality Reduction, Model Selection, Uncertainty Quantification

While sparse inverse covariance matrices are very popular for modeling
network connectivity, the value of the dense solution is often overlooked. In
fact the L2-regularized solution has deep connections to a number of important
applications to spectral graph theory, dimensionality reduction, and
uncertainty quantification. We derive an approach to directly compute the
partial correlations based on concepts from inverse problem theory. This
approach also leads to new insights on open problems such as model selection
and data preprocessing, as well as new approaches which relate the above
application areas.
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Authors: Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen
Date: 2019-03-17 00:00:00
Tasks: Hippocampus, Imputation, Missing Values

Disease progression modeling (DPM) using longitudinal data is a challenging
machine learning task. Existing DPM algorithms neglect temporal dependencies
among measurements, make parametric assumptions about biomarker trajectories,
do not model multiple biomarkers jointly, and need an alignment of subjects'
trajectories. In this paper, recurrent neural networks (RNNs) are utilized to
address these issues. However, in many cases, longitudinal cohorts contain
incomplete data, which hinders the application of standard RNNs and requires a
pre-processing step such as imputation of the missing values. Instead, we
propose a generalized training rule for the most widely used RNN architecture,
long short-term memory (LSTM) networks, that can handle both missing predictor
and target values. The proposed LSTM algorithm is applied to model the
progression of Alzheimer's disease (AD) using six volumetric magnetic resonance
imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole
brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is
compared to standard LSTM networks with data imputation and a parametric,
regression-based DPM method. The results show that the proposed algorithm
achieves a significantly lower mean absolute error (MAE) than the alternatives
with p < 0.05 using Wilcoxon signed rank test in predicting values of almost
all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA)
classifier applied to the predicted biomarker values produces a significantly
larger AUC of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for
clinical diagnosis of AD. Inspection of MAE curves as a function of the amount
of missing data reveals that the proposed LSTM algorithm achieves the best
performance up until more than 74% missing values. Finally, it is illustrated
how the method can successfully be applied to data with varying time intervals.
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Authors: Rémi Giraud, Vinh-Thong Ta, Aurélie Bugeau, Pierrick Coupé, Nicolas Papadakis
Date: 2019-03-17 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Superpixels have become very popular in many computer vision applications.
Nevertheless, they remain underexploited since the superpixel decomposition may
produce irregular and non stable segmentation results due to the dependency to
the image content. In this paper, we first introduce a novel structure, a
superpixel-based patch, called SuperPatch. The proposed structure, based on
superpixel neighborhood, leads to a robust descriptor since spatial information
is naturally included. The generalization of the PatchMatch method to
SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a
framework to perform fast segmentation and labeling from an image database, and
demonstrate the potential of our approach since we outperform, in terms of
computational cost and accuracy, the results of state-of-the-art methods on
both face labeling and medical image segmentation.
Machine Learning: A Dark Side of Cancer Computing
Authors: Ripon Patgiri, Sabuzima Nayak, Tanya Akutota, Bishal Paul
Date: 2019-03-17 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Cancer analysis and prediction is the utmost important research field for
well-being of humankind. The Cancer data are analyzed and predicted using
machine learning algorithms. Most of the researcher claims the accuracy of the
predicted results within 99%. However, we show that machine learning algorithms
can easily predict with an accuracy of 100% on Wisconsin Diagnostic Breast
Cancer dataset. We show that the method of gaining accuracy is an unethical
approach that we can easily mislead the algorithms. In this paper, we exploit
the weakness of Machine Learning algorithms. We perform extensive experiments
for the correctness of our results to exploit the weakness of machine learning
algorithms. The methods are rigorously evaluated to validate our claim. In
addition, this paper focuses on correctness of accuracy. This paper report
three key outcomes of the experiments, namely, correctness of accuracies,
significance of minimum accuracy, and correctness of machine learning
algorithms.
An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
Authors: Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis, José V. Manjón, D. Louis Collins, Pierrick Coupé, Alzheimer's Disease Neuroimaging Initiative
Date: 2019-03-17 00:00:00
Tasks: Hippocampus, Segmentation

Automatic segmentation methods are important tools for quantitative analysis
of Magnetic Resonance Images (MRI). Recently, patch-based label fusion
approaches have demonstrated state-of-the-art segmentation accuracy. In this
paper, we introduce a new patch-based label fusion framework to perform
segmentation of anatomical structures. The proposed approach uses an Optimized
PAtchMatch Label fusion (OPAL) strategy that drastically reduces the
computation time required for the search of similar patches. The reduced
computation time of OPAL opens the way for new strategies and facilitates
processing on large databases. In this paper, we investigate new perspectives
offered by OPAL, by introducing a new multi-scale and multi-feature framework.
During our validation on hippocampus segmentation we use two datasets: young
adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For
both, OPAL is compared to state-of-the-art methods. Results show that OPAL
obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for
EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy
similar to inter-expert variability. On the EADC-ADNI dataset, we compare the
hippocampal volumes obtained by manual and automatic segmentation. The volumes
appear to be highly correlated that enables to perform more accurate separation
of pathological populations.
Linearly Constrained Smoothing Group Sparsity Solvers in Off-grid Model
Authors: Cheng-Yu Hung, Mostafa Kaveh
Date: 2019-03-17 00:00:00
Tasks: compressed sensing

In compressed sensing, the sensing matrix is assumed perfectly known. However, there exists perturbation in the sensing matrix in reality due to sensor offsets or noise disturbance. Directions-of-arrival (DoA) estimation with off-grid effect satisfies this situation, and can be formulated into a (non)convex optimization problem with linear inequalities constraints, which can be solved by the interior point method (using the CVX tools), but at a large computational cost. In this work, in order to design efficient algorithms, we consider various alternative formulations, such as unconstrained formulation, primal-dual formulation, or conic formulation to develop group-sparsity promoted solvers. First, the consensus alternating direction method of multipliers (C-ADMM) is applied. Then, iterative algorithms for the BPDN formulation is proposed by combining the Nesterov smoothing technique with accelerated proximal gradient method, and the convergence analysis of the method is conducted as well. We also developed a variant of EGT (Excessive Gap Technique)-based primal-dual method to systematically reduce the smoothing parameter sequentially. Finally, we propose algorithms for quadratically constrained L2-L1 mixed norm minimization problem by using the smoothed dual conic optimization (SDCO) and continuation technique. The performance of accuracy and convergence for all the proposed methods are demonstrated in the numerical simulations.
Evaluation Framework of Superpixel Methods with a Global Regularity Measure
Authors: Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
Date: 2019-03-17 00:00:00

In the superpixel literature, the comparison of state-of-the-art methods can
be biased by the non-robustness of some metrics to decomposition aspects, such
as the superpixel scale. Moreover, most recent decomposition methods allow to
set a shape regularity parameter, which can have a substantial impact on the
measured performances. In this paper, we introduce an evaluation framework,
that aims to unify the comparison process of superpixel methods. We investigate
the limitations of existing metrics, and propose to evaluate each of the three
core decomposition aspects: color homogeneity, respect of image objects and
shape regularity. To measure the regularity aspect, we propose a new global
regularity measure (GR), which addresses the non-robustness of state-of-the-art
metrics. We evaluate recent superpixel methods with these criteria, at several
superpixel scales and regularity levels. The proposed framework reduces the
bias in the comparison process of state-of-the-art superpixel methods. Finally,
we demonstrate that the proposed GR measure is correlated with the performances
of various applications.
Technical notes: Syntax-aware Representation Learning With Pointer Networks
Authors: Matteo Grella
Date: 2019-03-17 00:00:00
Tasks: Dependency Parsing, regression, Representation Learning

This is a work-in-progress report, which aims to share preliminary results of
a novel sequence-to-sequence schema for dependency parsing that relies on a
combination of a BiLSTM and two Pointer Networks (Vinyals et al., 2015), in
which the final softmax function has been replaced with the logistic
regression. The two pointer networks co-operate to develop a latent syntactic
knowledge, by learning the lexical properties of "selection" and the lexical
properties of "selectability", respectively. At the moment and without
fine-tuning, the parser implementation gets a UAS of 93.14% on the English
Penn-treebank (Marcus et al., 1993) annotated with Stanford Dependencies: 2-3%
under the SOTA but yet attractive as a baseline of the approach.
Joint Block Low Rank and Sparse Matrix Recovery in Array Self-Calibration Off-Grid DoA Estimation
Authors: Cheng-Yu Hung, Mostafa Kaveh
Date: 2019-03-17 00:00:00

This letter addresses the estimation of directions-of-arrival (DoA) by a sensor array using a sparse model in the presence of array calibration errors and off-grid directions. The received signal utilizes previously used models for unknown errors in calibration and structured linear representation of the off-grid effect. A convex optimization problem is formulated with an objective function to promote two-layer joint block-sparsity with its second-order cone programming (SOCP) representation. The performance of the proposed method is demonstrated by numerical simulations and compared with the Cramer-Rao Bound (CRB), and several previously proposed methods.
Modeling and Optimization of Human-machine Interaction Processes via the Maximum Entropy Principle
Authors: Jiaxiao Zheng, Gustavo de Veciana
Date: 2019-03-17 00:00:00
Tasks: Decision Making

We propose a data-driven framework to enable the modeling and optimization of
human-machine interaction processes, e.g., systems aimed at assisting humans in
decision-making or learning, work-load allocation, and interactive advertising.
This is a challenging problem for several reasons. First, humans' behavior is
hard to model or infer, as it may reflect biases, long term memory, and
sensitivity to sequencing, i.e., transience and exponential complexity in the
length of the interaction. Second, due to the interactive nature of such
processes, the machine policy used to engage with a human may bias possible
data-driven inferences. Finally, in choosing machine policies that optimize
interaction rewards, one must, on the one hand, avoid being overly sensitive to
error/variability in the estimated human model, and on the other, being overly
deterministic/predictable which may result in poor human 'engagement' in the
interaction. To meet these challenges, we propose a robust approach, based on
the maximum entropy principle, which iteratively estimates human behavior and
optimizes the machine policy--Alternating Entropy-Reward Ascent (AREA)
algorithm. We characterize AREA, in terms of its space and time complexity and
convergence. We also provide an initial validation based on synthetic data
generated by an established noisy nonlinear model for human decision-making.
Proximal Splitting Networks for Image Restoration
Authors: Raied Aljadaany, Dipan K. Pal, Marios Savvides
Date: 2019-03-17 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

Image restoration problems are typically ill-posed requiring the design of
suitable priors. These priors are typically hand-designed and are fully
instantiated throughout the process. In this paper, we introduce a novel
framework for handling inverse problems related to image restoration based on
elements from the half quadratic splitting method and proximal operators.
Modeling the proximal operator as a convolutional network, we defined an
implicit prior on the image space as a function class during training. This is
in contrast to the common practice in literature of having the prior to be
fixed and fully instantiated even during training stages. Further, we allow
this proximal operator to be tuned differently for each iteration which greatly
increases modeling capacity and allows us to reduce the number of iterations by
an order of magnitude as compared to other approaches. Our final network is an
end-to-end one whose run time matches the previous fastest algorithms while
outperforming them in recovery fidelity on two image restoration tasks. Indeed,
we find our approach achieves state-of-the-art results on benchmarks in image
denoising and image super resolution while recovering more complex and finer
details.
SCALP: Superpixels with Contour Adherence using Linear Path
Authors: Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
Date: 2019-03-17 00:00:00
Tasks: Clustering, Contour Detection, Superpixels

Superpixel decomposition methods are generally used as a pre-processing step
to speed up image processing tasks. They group the pixels of an image into
homogeneous regions while trying to respect existing contours. For all
state-of-the-art superpixel decomposition methods, a trade-off is made between
1) computational time, 2) adherence to image contours and 3) regularity and
compactness of the decomposition. In this paper, we propose a fast method to
compute Superpixels with Contour Adherence using Linear Path (SCALP) in an
iterative clustering framework. The distance computed when trying to associate
a pixel to a superpixel during the clustering is enhanced by considering the
linear path to the superpixel barycenter. The proposed framework produces
regular and compact superpixels that adhere to the image contours. We provide a
detailed evaluation of SCALP on the standard Berkeley Segmentation Dataset. The
obtained results outperform state-of-the-art methods in terms of standard
superpixel and contour detection metrics.
Robust Shape Regularity Criteria for Superpixel Evaluation
Authors: Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
Date: 2019-03-17 00:00:00
Tasks: Object Recognition

Regular decompositions are necessary for most superpixel-based object
recognition or tracking applications. So far in the literature, the regularity
or compactness of a superpixel shape is mainly measured by its circularity. In
this work, we first demonstrate that such measure is not adapted for superpixel
evaluation, since it does not directly express regularity but circular
appearance. Then, we propose a new metric that considers several shape
regularity aspects: convexity, balanced repartition, and contour smoothness.
Finally, we demonstrate that our measure is robust to scale and noise and
enables to more relevantly compare superpixel methods.
A Brain-inspired Algorithm for Training Highly Sparse Neural Networks
Authors: Zahra Atashgahi, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy
Date: 2019-03-17 00:00:00
Tasks: Learning Theory

Sparse neural networks attract increasing interest as they exhibit comparable performance to their dense counterparts while being computationally efficient. Pruning the dense neural networks is among the most widely used methods to obtain a sparse neural network. Driven by the high training cost of such methods that can be unaffordable for a low-resource device, training sparse neural networks sparsely from scratch has recently gained attention. However, existing sparse training algorithms suffer from various issues, including poor performance in high sparsity scenarios, computing dense gradient information during training, or pure random topology search. In this paper, inspired by the evolution of the biological brain and the Hebbian learning theory, we present a new sparse training approach that evolves sparse neural networks according to the behavior of neurons in the network. Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, Cosine similarity-based and Random Topology Exploration (CTRE), evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward. We carried out different experiments on eight datasets, including tabular, image, and text datasets, and demonstrate that our proposed method outperforms several state-of-the-art sparse training algorithms in extremely sparse neural networks by a large gap. The implementation code is available on https://github.com/zahraatashgahi/CTRE
Topic-Guided Variational Autoencoders for Text Generation
Authors: Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin
Date: 2019-03-17 00:00:00
Tasks: Conditional Text Generation, Text Generation

We propose a topic-guided variational autoencoder (TGVAE) model for text
generation. Distinct from existing variational autoencoder (VAE) based
approaches, which assume a simple Gaussian prior for the latent code, our model
specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural
topic module. Each mixture component corresponds to a latent topic, which
provides guidance to generate sentences under the topic. The neural topic
module and the VAE-based neural sequence module in our model are learned
jointly. In particular, a sequence of invertible Householder transformations is
applied to endow the approximate posterior of the latent code with high
flexibility during model inference. Experimental results show that our TGVAE
outperforms alternative approaches on both unconditional and conditional text
generation, which can generate semantically-meaningful sentences with various
topics.
Stabilize Deep ResNet with A Sharp Scaling Factor $τ$
Authors: Huishuai Zhang, Da Yu, Mingyang Yi, Wei Chen, Tie-Yan Liu
Date: 2019-03-17 00:00:00

We study the stability and convergence of training deep ResNets with gradient descent. Specifically, we show that the parametric branch in the residual block should be scaled down by a factor $\tau =O(1/\sqrt{L})$ to guarantee stable forward/backward process, where $L$ is the number of residual blocks. Moreover, we establish a converse result that the forward process is unbounded when $\tau>L^{-\frac{1}{2}+c}$, for any positive constant $c$. The above two results together establish a sharp value of the scaling factor in determining the stability of deep ResNet. Based on the stability result, we further show that gradient descent finds the global minima if the ResNet is properly over-parameterized, which significantly improves over the previous work with a much larger range of $\tau$ that admits global convergence. Moreover, we show that the convergence rate is independent of the depth, theoretically justifying the advantage of ResNet over vanilla feedforward network. Empirically, with such a factor $\tau$, one can train deep ResNet without normalization layer. Moreover, for ResNets with normalization layer, adding such a factor $\tau$ also stabilizes the training and obtains significant performance gain for deep ResNet.