Variational models for signal processing with graph neural networks

DOI10.1007/978-3-030-75549-2_26zbMath1484.68203arXiv2103.16337OpenAlexW3164434125MaRDI QIDQ826200

Julien Rabin, Amitoz Azad, Abderrahim Elmoataz

Publication date: 20 December 2021

Full work available at URL: https://arxiv.org/abs/2103.16337

zbMATH Keywords

total variationneural networkvariational methodsunsupervised learninggraph processingmessage-passing network

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Variational inequalities (49J40) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

• Nonlinear multilayered representation of graph-signals
• Parseval proximal neural networks
• On the limited memory BFGS method for large scale optimization
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Deep neural network structures solving variational inequalities
• Non-local regularization of inverse problems
• Preconditioning of a Generalized Forward-Backward Splitting and Application to Optimization on Graphs
• Nonlocal Operators with Applications to Image Processing
• On the $p$-Laplacian and $\infty$-Laplacian on Graphs with Applications in Image and Data Processing
• Deep unfolding of a proximal interior point method for image restoration

This page was built for publication: Variational models for signal processing with graph neural networks