Learning the optimal Tikhonov regularizer for inverse problems

Learning the optimal Tikhonov regularizer for inverse problems

11 June 2021

Giovanni S. Alberti

Matteo Santacesaria

Papers citing "Learning the optimal Tikhonov regularizer for inverse problems"

18 / 18 papers shown

Title
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems Matthias Chung B. Peters Michael Solomon 34 0 0 10 May 2025
A Generalization Bound for a Family of Implicit Networks Samy Wu Fung Benjamin Berkels 43 0 0 28 Jan 2025
The Star Geometry of Critic-Based Regularizer Learning Oscar Leong Eliza O'Reilly Yong Sheng Soh AAML 42 0 0 29 Aug 2024
sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures Andac Demir E. Solovyeva James Boylan Mei Xiao Fabrizio Serluca S. Hoersch Jeremy L Jenkins Murthy S. Devarakonda B. Kiziltan 37 0 0 06 May 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation Hongwei Tan Ziruo Cai Marcelo Pereyra Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb SSL 67 1 0 08 Apr 2024
Training Implicit Networks for Image Deblurring using Jacobian-Free Backpropagation Linghai Liu Shuaicheng Tong Lisa Zhao 21 1 0 03 Feb 2024
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation Zakhar Shumaylov Jeremy Budd Subhadip Mukherjee Carola-Bibiane Schönlieb 26 6 0 01 Feb 2024
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems Giovanni S. Alberti Luca Ratti Matteo Santacesaria Silvia Sciutto 13 1 0 29 Jan 2024
Statistical inverse learning problems with random observations Abhishake T. Helin Nicole Mucke 11 1 0 23 Dec 2023
Learned reconstruction methods for inverse problems: sample error estimates Luca Ratti 26 0 0 21 Dec 2023
Learned Regularization for Inverse Problems: Insights from a Spectral Model Martin Burger Samira Kabri 27 0 0 15 Dec 2023
On Learning the Optimal Regularization Parameter in Inverse Problems Jonathan Chirinos-Rodriguez E. De Vito C. Molinari Lorenzo Rosasco S. Villa 17 3 0 27 Nov 2023
Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting E. Agliari Francesco Alemanno Miriam Aquaro A. Fachechi 19 7 0 01 Aug 2023
Convergent Data-driven Regularizations for CT Reconstruction Samira Kabri Alexander Auras D. Riccio Hartmut Bauermeister Martin Benning Michael Moeller Martin Burger 38 12 0 14 Dec 2022
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis Luca Galimberti Anastasis Kratsios Giulia Livieri OOD 28 14 0 24 Oct 2022
Learning Variational Models with Unrolling and Bilevel Optimization Christoph Brauer Niklas Breustedt T. Wolff D. Lorenz SSL 31 3 0 26 Sep 2022
Learning via nonlinear conjugate gradients and depth-varying neural ODEs George Baravdish Gabriel Eilertsen Rym Jaroudi B. Johansson Lukávs Malý Jonas Unger 16 3 0 11 Feb 2022
Convergence Rates for Learning Linear Operators from Noisy Data Maarten V. de Hoop Nikola B. Kovachki Nicholas H. Nelsen Andrew M. Stuart 19 54 0 27 Aug 2021