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Found 5 papers, 3 papers with code
Learning Algebraic Multigrid Using Graph Neural Networks
Efficient numerical solvers for sparse linear systems are crucial in science and engineering.
Solving RED with Weighted Proximal Methods
REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors.
Learning to Optimize Multigrid PDE Solvers
Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for many scientific disciplines.
A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression
A multilevel framework is presented for solving such l1 regularized sparse optimization problems efficiently.
A Multiscale Variable-Grouping Framework for MRF Energy Minimization
We present a multiscale approach for minimizing the energy associated with Markov Random Fields (MRFs) with energy functions that include arbitrary pairwise potentials.
