[BOOK][B] An invitation to compressive sensing
This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …
standard compressive problem studied throughout the book and reveals its ubiquity in many …
Graph structure learning for robust graph neural networks
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs.
However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations …
However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations …
An overview of lead and accompaniment separation in music
Popular music is often composed of an accompaniment and a lead component, the latter
typically consisting of vocals. Filtering such mixtures to extract one or both components has …
typically consisting of vocals. Filtering such mixtures to extract one or both components has …
[BOOK][B] Large-scale convex optimization: algorithms & analyses via monotone operators
Starting from where a first course in convex optimization leaves off, this text presents a
unified analysis of first-order optimization methods–including parallel-distributed algorithms …
unified analysis of first-order optimization methods–including parallel-distributed algorithms …
An introduction to continuous optimization for imaging
A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …
typical structural properties. The aim of this paper is to describe the state of the art in …
An inertial forward-backward algorithm for monotone inclusions
In this paper, we propose an inertial forward-backward splitting algorithm to compute a zero
of the sum of two monotone operators, with one of the two operators being co-coercive. The …
of the sum of two monotone operators, with one of the two operators being co-coercive. The …
Sparse modeling for image and vision processing
In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …
models and their applications. In statistics and machine learning, the sparsity principle is …
A splitting algorithm for dual monotone inclusions involving cocoercive operators
BC Vũ - Advances in Computational Mathematics, 2013 - Springer
We consider the problem of solving dual monotone inclusions involving sums of composite
parallel-sum type operators. A feature of this work is to exploit explicitly the properties of the …
parallel-sum type operators. A feature of this work is to exploit explicitly the properties of the …
Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlap** networks
Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new
window onto the organizational principles of brain function. Using state-of-the-art signal …
window onto the organizational principles of brain function. Using state-of-the-art signal …
The cosparse analysis model and algorithms
After a decade of extensive study of the sparse representation synthesis model, we can
safely say that this is a mature and stable field, with clear theoretical foundations, and …
safely say that this is a mature and stable field, with clear theoretical foundations, and …