pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating CS in Parallel Imaging Reconstructions Using an Efficient and Effective Circulant Preconditioner
Authors: Kirsten Koolstra, Jeroen van Gemert, Peter Börnert, Andrew Webb, Rob Remis
Date: 2017-10-04 00:00:00
Tasks: compressed sensing

Purpose: Design of a preconditioner for fast and efficient parallel imaging
and compressed sensing reconstructions. Theory: Parallel imaging and compressed
sensing reconstructions become time consuming when the problem size or the
number of coils is large, due to the large linear system of equations that has
to be solved in l_1 and l_2-norm based reconstruction algorithms. Such linear
systems can be solved efficiently using effective preconditioning techniques.
Methods: In this paper we construct such a preconditioner by approximating the
system matrix of the linear system, which comprises the data fidelity and
includes total variation and wavelet regularization, by a matrix with the
assumption that is a block circulant matrix with circulant blocks. Due to its
circulant structure, the preconditioner can be constructed quickly and its
inverse can be evaluated fast using only two fast Fourier transformations. We
test the performance of the preconditioner for the conjugate gradient method as
the linear solver, integrated into the Split Bregman algorithm. Results: The
designed circulant preconditioner reduces the number of iterations required in
the conjugate gradient method by almost a factor of~5. The speed up results in
a total acceleration factor of approximately 2.5 for the entire reconstruction
algorithm when implemented in MATLAB, while the initialization time of the
preconditioner is negligible. Conclusion: The proposed preconditioner reduces
the reconstruction time for parallel imaging and compressed sensing in a Split
Bregman implementation and can easily handle large systems since it is
Fourier-based, allowing for efficient computations.
  Key words: preconditioning; compressed sensing; Split Bregman; parallel
imaging
Semantic 3D Reconstruction with Finite Element Bases
Authors: Audrey Richard, Christoph Vogel, Maros Blaha, Thomas Pock, Konrad Schindler
Date: 2017-10-04 00:00:00
Tasks: 3D Reconstruction, valid

We propose a novel framework for the discretisation of multi-label problems
on arbitrary, continuous domains. Our work bridges the gap between general FEM
discretisations, and labeling problems that arise in a variety of computer
vision tasks, including for instance those derived from the generalised Potts
model. Starting from the popular formulation of labeling as a convex relaxation
by functional lifting, we show that FEM discretisation is valid for the most
general case, where the regulariser is anisotropic and non-metric. While our
findings are generic and applicable to different vision problems, we
demonstrate their practical implementation in the context of semantic 3D
reconstruction, where such regularisers have proved particularly beneficial.
The proposed FEM approach leads to a smaller memory footprint as well as faster
computation, and it constitutes a very simple way to enable variable, adaptive
resolution within the same model.
Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power
Authors: Kostas Hatalis, Alberto J. Lamadrid, Katya Scheinberg, Shalinee Kishore
Date: 2017-10-04 00:00:00
Tasks: Decision Making, Form, Prediction Intervals, quantile regression, regression

Uncertainty analysis in the form of probabilistic forecasting can
significantly improve decision making processes in the smart power grid for
better integrating renewable energy sources such as wind. Whereas point
forecasting provides a single expected value, probabilistic forecasts provide
more information in the form of quantiles, prediction intervals, or full
predictive densities. This paper analyzes the effectiveness of a novel approach
for nonparametric probabilistic forecasting of wind power that combines a
smooth approximation of the pinball loss function with a neural network
architecture and a weighting initialization scheme to prevent the quantile
cross over problem. A numerical case study is conducted using publicly
available wind data from the Global Energy Forecasting Competition 2014.
Multiple quantiles are estimated to form 10%, to 90% prediction intervals which
are evaluated using a quantile score and reliability measures. Benchmark models
such as the persistence and climatology distributions, multiple quantile
regression, and support vector quantile regression are used for comparison
where results demonstrate the proposed approach leads to improved performance
while preventing the problem of overlapping quantile estimates.
Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians
Authors: Zhiyuan Liu, Soumya Kundu, Lijun Chen, Enoch Yeung
Date: 2017-10-04 00:00:00

In this paper we propose a new Koopman operator approach to the decomposition
of nonlinear dynamical systems using Koopman Gramians. We introduce the notion
of an input-Koopman operator, and show how input-Koopman operators can be used
to cast a nonlinear system into the classical state-space form, and identify
conditions under which input and state observable functions are well separated.
We then extend an existing method of dynamic mode decomposition for learning
Koopman operators from data known as deep dynamic mode decomposition to systems
with controls or disturbances. We illustrate the accuracy of the method in
learning an input-state separable Koopman operator for an example system, even
when the underlying system exhibits mixed state-input terms. We next introduce
a nonlinear decomposition algorithm, based on Koopman Gramians, that maximizes
internal subsystem observability and disturbance rejection from unwanted noise
from other subsystems. We derive a relaxation based on Koopman Gramians and
multi-way partitioning for the resulting NP-hard decomposition problem. We
lastly illustrate the proposed algorithm with the swing dynamics for an IEEE
39-bus system.
Multi-Oriented Text Detection and Verification in Video Frames and Scene Images
Authors: Aneeshan Sain, Ayan Kumar Bhunia, Partha Pratim Roy, Umapada Pal
Date: 2017-07-22 00:00:00
Tasks: Clustering, Curved Text Detection, text-classification, Text Classification, Text Detection

In this paper, we bring forth a novel approach of video text detection using
Fourier-Laplacian filtering in the frequency domain that includes a
verification technique using Hidden Markov Model (HMM). The proposed approach
deals with the text region appearing not only in horizontal or vertical
directions, but also in any other oblique or curved orientation in the image.
Until now only a few methods have been proposed that look into curved text
detection in video frames, wherein lies our novelty. In our approach, we first
apply Fourier-Laplacian transform on the image followed by an ideal
Laplacian-Gaussian filtering. Thereafter K-means clustering is employed to
obtain the asserted text areas depending on a maximum difference map. Next, the
obtained connected components (CC) are skeletonized to distinguish various text
strings. Complex components are disintegrated into simpler ones according to a
junction removal algorithm followed by a concatenation performed on possible
combination of the disjoint skeletons to obtain the corresponding text area.
Finally these text hypotheses are verified using HMM-based text/non-text
classification system. False positives are thus eliminated giving us a robust
text detection performance. We have tested our framework in multi-oriented text
lines in four scripts, namely, English, Chinese, Devanagari and Bengali, in
video frames and scene texts. The results obtained show that proposed approach
surpasses existing methods on text detection.
Intelligence Quotient and Intelligence Grade of Artificial Intelligence
Authors: Feng Liu, Yong Shi, Ying Liu
Date: 2017-09-29 00:00:00

Although artificial intelligence is currently one of the most interesting
areas in scientific research, the potential threats posed by emerging AI
systems remain a source of persistent controversy. To address the issue of AI
threat, this study proposes a standard intelligence model that unifies AI and
human characteristics in terms of four aspects of knowledge, i.e., input,
output, mastery, and creation. Using this model, we observe three challenges,
namely, expanding of the von Neumann architecture; testing and ranking the
intelligence quotient of naturally and artificially intelligent systems,
including humans, Google, Bing, Baidu, and Siri; and finally, the dividing of
artificially intelligent systems into seven grades from robots to Google Brain.
Based on this, we conclude that AlphaGo belongs to the third grade.
A signature-based machine learning model for bipolar disorder and borderline personality disorder
Authors: Imanol Perez Arribas, Kate Saunders, Guy Goodwin, Terry Lyons
Date: 2017-07-22 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Mobile technologies offer opportunities for higher resolution monitoring of
health conditions. This opportunity seems of particular promise in psychiatry
where diagnoses often rely on retrospective and subjective recall of mood
states. However, getting actionable information from these rather complex time
series is challenging, and at present the implications for clinical care are
largely hypothetical. This research demonstrates that, with well chosen cohorts
(of bipolar disorder, borderline personality disorder, and control) and modern
methods, it is possible to objectively learn to identify distinctive behaviour
over short periods (20 reports) that effectively separate the cohorts.
Participants with bipolar disorder or borderline personality disorder and
healthy volunteers completed daily mood ratings using a bespoke smartphone app
for up to a year. A signature-based machine learning model was used to classify
participants on the basis of the interrelationship between the different mood
items assessed and to predict subsequent mood. The signature methodology was
significantly superior to earlier statistical approaches applied to this data
in distinguishing the participant three groups, clearly placing 75% into their
original groups on the basis of their reports. Subsequent mood ratings were
correctly predicted with greater than 70% accuracy in all groups. Prediction of
mood was most accurate in healthy volunteers (89-98%) compared to bipolar
disorder (82-90%) and borderline personality disorder (70-78%).
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision
Authors: Dushyant Mehta, Helge Rhodin, Dan Casas, Pascal Fua, Oleksandr Sotnychenko, Weipeng Xu, Christian Theobalt
Date: 2016-11-29 00:00:00
Tasks: 3D Human Pose Estimation, Diversity, Monocular 3D Human Pose Estimation, Pose Estimation, Transfer Learning

We propose a CNN-based approach for 3D human body pose estimation from single
RGB images that addresses the issue of limited generalizability of models
trained solely on the starkly limited publicly available 3D pose data. Using
only the existing 3D pose data and 2D pose data, we show state-of-the-art
performance on established benchmarks through transfer of learned features,
while also generalizing to in-the-wild scenes. We further introduce a new
training set for human body pose estimation from monocular images of real
humans that has the ground truth captured with a multi-camera marker-less
motion capture system. It complements existing corpora with greater diversity
in pose, human appearance, clothing, occlusion, and viewpoints, and enables an
increased scope of augmentation. We also contribute a new benchmark that covers
outdoor and indoor scenes, and demonstrate that our 3D pose dataset shows
better in-the-wild performance than existing annotated data, which is further
improved in conjunction with transfer learning from 2D pose data. All in all,
we argue that the use of transfer learning of representations in tandem with
algorithmic and data contributions is crucial for general 3D body pose
estimation.
Learning Functional Causal Models with Generative Neural Networks
Authors: Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
Date: 2017-09-15 00:00:00

We introduce a new approach to functional causal modeling from observational
data, called Causal Generative Neural Networks (CGNN). CGNN leverages the power
of neural networks to learn a generative model of the joint distribution of the
observed variables, by minimizing the Maximum Mean Discrepancy between
generated and observed data. An approximate learning criterion is proposed to
scale the computational cost of the approach to linear complexity in the number
of observations. The performance of CGNN is studied throughout three
experiments. Firstly, CGNN is applied to cause-effect inference, where the task
is to identify the best causal hypothesis out of $X\rightarrow Y$ and
$Y\rightarrow X$. Secondly, CGNN is applied to the problem of identifying
v-structures and conditional independences. Thirdly, CGNN is applied to
multivariate functional causal modeling: given a skeleton describing the direct
dependences in a set of random variables $\textbf{X} = [X_1, \ldots, X_d]$,
CGNN orients the edges in the skeleton to uncover the directed acyclic causal
graph describing the causal structure of the random variables. On all three
tasks, CGNN is extensively assessed on both artificial and real-world data,
comparing favorably to the state-of-the-art. Finally, CGNN is extended to
handle the case of confounders, where latent variables are involved in the
overall causal model.
GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion
Authors: Qiaodong Cui, Victor Fragoso, Chris Sweeney, Pradeep Sen
Date: 2017-10-04 00:00:00
Tasks: graph construction

We present GraphMatch, an approximate yet efficient method for building the
matching graph for large-scale structure-from-motion (SfM) pipelines. Unlike
modern SfM pipelines that use vocabulary (Voc.) trees to quickly build the
matching graph and avoid a costly brute-force search of matching image pairs,
GraphMatch does not require an expensive offline pre-processing phase to
construct a Voc. tree. Instead, GraphMatch leverages two priors that can
predict which image pairs are likely to match, thereby making the matching
process for SfM much more efficient. The first is a score computed from the
distance between the Fisher vectors of any two images. The second prior is
based on the graph distance between vertices in the underlying matching graph.
GraphMatch combines these two priors into an iterative "sample-and-propagate"
scheme similar to the PatchMatch algorithm. Its sampling stage uses Fisher
similarity priors to guide the search for matching image pairs, while its
propagation stage explores neighbors of matched pairs to find new ones with a
high image similarity score. Our experiments show that GraphMatch finds the
most image pairs as compared to competing, approximate methods while at the
same time being the most efficient.
Spectral estimation of the percolation transition in clustered networks
Authors: Pan Zhang
Date: 2017-10-04 00:00:00
Tasks: Clustering

There have been several spectral bounds for the percolation transition in
networks, using spectrum of matrices associated with the network such as the
adjacency matrix and the non-backtracking matrix. However they are far from
being tight when the network is sparse and displays clustering or transitivity,
which is represented by existence of short loops e.g. triangles. In this work,
for the bond percolation, we first propose a message passing algorithm for
calculating size of percolating clusters considering effects of triangles, then
relate the percolation transition to the leading eigenvalue of a matrix that we
name the triangle-non-backtracking matrix, by analyzing stability of the
message passing equations. We establish that our method gives a tighter
lower-bound to the bond percolation transition than previous spectral bounds,
and it becomes exact for an infinite network with no loops longer than 3. We
evaluate numerically our methods on synthetic and real-world networks, and
discuss further generalizations of our approach to include higher-order
sub-structures.
Deep Mean-Shift Priors for Image Restoration
Authors: Siavash Arjomand Bigdeli, Meiguang Jin, Paolo Favaro, Matthias Zwicker
Date: 2017-09-12 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Deblurring, Demosaicking, Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our prior corresponds to the mean-shift vector on the natural image
distribution. In addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes risk
minimization. We demonstrate competitive results for noise-blind deblurring,
super-resolution, and demosaicing.
LSTM: A Search Space Odyssey
Authors: Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber
Date: 2015-03-13 00:00:00
Tasks: CPU, Handwriting Recognition, Music Modeling, Speech Recognition

Several variants of the Long Short-Term Memory (LSTM) architecture for
recurrent neural networks have been proposed since its inception in 1995. In
recent years, these networks have become the state-of-the-art models for a
variety of machine learning problems. This has led to a renewed interest in
understanding the role and utility of various computational components of
typical LSTM variants. In this paper, we present the first large-scale analysis
of eight LSTM variants on three representative tasks: speech recognition,
handwriting recognition, and polyphonic music modeling. The hyperparameters of
all LSTM variants for each task were optimized separately using random search,
and their importance was assessed using the powerful fANOVA framework. In
total, we summarize the results of 5400 experimental runs ($\approx 15$ years
of CPU time), which makes our study the largest of its kind on LSTM networks.
Our results show that none of the variants can improve upon the standard LSTM
architecture significantly, and demonstrate the forget gate and the output
activation function to be its most critical components. We further observe that
the studied hyperparameters are virtually independent and derive guidelines for
their efficient adjustment.
A Compact Kernel Approximation for 3D Action Recognition
Authors: Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Date: 2017-09-06 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Temporal Action Localization

3D action recognition was shown to benefit from a covariance representation
of the input data (joint 3D positions). A kernel machine feed with such feature
is an effective paradigm for 3D action recognition, yielding state-of-the-art
results. Yet, the whole framework is affected by the well-known scalability
issue. In fact, in general, the kernel function has to be evaluated for all
pairs of instances inducing a Gram matrix whose complexity is quadratic in the
number of samples. In this work we reduce such complexity to be linear by
proposing a novel and explicit feature map to approximate the kernel function.
This allows to train a linear classifier with an explicit feature encoding,
which implicitly implements a Log-Euclidean machine in a scalable fashion. Not
only we prove that the proposed approximation is unbiased, but also we work out
an explicit strong bound for its variance, attesting a theoretical superiority
of our approach with respect to existing ones. Experimentally, we verify that
our representation provides a compact encoding and outperforms other
approximation schemes on a number of publicly available benchmark datasets for
3D action recognition.
Discourse Structure in Machine Translation Evaluation
Authors: Shafiq Joty, Francisco Guzmán, Lluís Màrquez, Preslav Nakov
Date: 2017-10-04 00:00:00
Proceeding: CL 2017 12
Tasks: Machine Translation, Sentence, Translation

In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.
Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations
Authors: Klemen Grm, Vitomir Štruc, Anais Artiges, Matthieu Caron, Hazim Kemal Ekenel
Date: 2017-10-04 00:00:00
Tasks: Face Recognition, Face Verification

Deep convolutional neural networks (CNNs) based approaches are the
state-of-the-art in various computer vision tasks, including face recognition.
Considerable research effort is currently being directed towards further
improving deep CNNs by focusing on more powerful model architectures and better
learning techniques. However, studies systematically exploring the strengths
and weaknesses of existing deep models for face recognition are still
relatively scarce in the literature. In this paper, we try to fill this gap and
study the effects of different covariates on the verification performance of
four recent deep CNN models using the Labeled Faces in the Wild (LFW) dataset.
Specifically, we investigate the influence of covariates related to: image
quality -- blur, JPEG compression, occlusion, noise, image brightness,
contrast, missing pixels; and model characteristics -- CNN architecture, color
information, descriptor computation; and analyze their impact on the face
verification performance of AlexNet, VGG-Face, GoogLeNet, and SqueezeNet. Based
on comprehensive and rigorous experimentation, we identify the strengths and
weaknesses of the deep learning models, and present key areas for potential
future research. Our results indicate that high levels of noise, blur, missing
pixels, and brightness have a detrimental effect on the verification
performance of all models, whereas the impact of contrast changes and
compression artifacts is limited. It has been found that the descriptor
computation strategy and color information does not have a significant
influence on performance.