5 papers · 1 filter
1-Lipschitz Neural Networks on Hadamard Manifolds
Davide Murari, Marta Ghirardelli, Ben Adcock +4
Controlling the Lipschitz constant of a neural network is a standard way to promote robustness and stability. Most existing constraining strategies are designed for Euclidean space…
Graph Neural Regularizers for PDE Inverse Problems
William Lauga, James Rowbottom, Alexander Denker +3
We present a framework for solving a broad class of ill-posed inverse problems governed by partial differential equations (PDEs), where the target coefficients of the forward opera…
Data-driven approaches to inverse problems
Carola-Bibiane Schönlieb, Zakhar Shumaylov
Inverse problems are concerned with the reconstruction of unknown physical quantities using indirect measurements and are fundamental across diverse fields such as medical imaging,…
Nested Bregman Iterations for Decomposition Problems
Tobias Wolf, Derek Driggs, Kostas Papafitsoros +2
We consider the task of image reconstruction while simultaneously decomposing the reconstructed image into components with different features. A commonly used tool for this is a va…
Deep Block Proximal Linearised Minimisation Algorithm for Non-convex Inverse Problems
Chaoyan Huang, Zhongming Wu, Yanqi Cheng +3
Image restoration is typically addressed through non-convex inverse problems, which are often solved using first-order block-wise splitting methods. In this paper, we consider a ge…