106 citations · 229 across the 18 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
math.NA2023
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks
Xiaoyu Wang, Martin Benning
We propose a novel framework for the regularised inversion of deep neural networks. The framework is based on the authors' recent work on training feed-forward neural networks with…
math.NA2023
Trust your source: quantifying source condition elements for variational regularisation methods
Martin Benning, Tatiana A. Bubba, Luca Ratti +1
Source conditions are a key tool in regularisation theory that are needed to derive error estimates and convergence rates for ill-posed inverse problems. In this paper, we provide…