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Found 9 papers, 0 papers with code
On the Interplay of Subset Selection and Informed Graph Neural Networks
However, learning on large datasets is strongly limited by the availability of computational resources and can be infeasible in some scenarios.
Learning Variational Models with Unrolling and Bilevel Optimization
In this paper we consider the problem of learning variational models in the context of supervised learning via risk minimization.
Non-stationary Douglas-Rachford and alternating direction method of multipliers: adaptive stepsizes and convergence
This, in turn, proves the convergence of the method with the new adaptive stepsize rule.
Denoising of image gradients and total generalized variation denoising
We show that this increases the image reconstruction quality and derive that the resulting model resembles the total generalized variation denoising method, thus providing a new motivation for this model.
An extended Perona-Malik model based on probabilistic models
Moreover, we show how mean field approximations to these Gaussian scale mixtures lead to a modification of the lagged-diffusivity algorithm that better captures the uncertainties in the restoration.
Imaging with Kantorovich-Rubinstein discrepancy
We propose the use of the Kantorovich-Rubinstein norm from optimal transport in imaging problems.
A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing
An algorithmic framework to compute sparse or minimal-TV solutions of linear systems is proposed.
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 Linearized Bregman Method via Split Feasibility Problems: Analysis and Generalizations
The linearized Bregman method is a method to calculate sparse solutions to systems of linear equations.
