3 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
Gradient Descent on Point Clouds and Applications in Learned Operator Correction
Andreas Hauptmann, Yury Korolev, Matthew Thorpe
We consider the problem of minimising an energy over an unknown manifold that is given implicitly by a point cloud. For a known manifold one can define a gradient descent scheme an…
QVaR: a Quantum Variational Regularization method for Linear Inverse Problems
Siiri Rautio, Hjørdis Schlüter, Andreas Hauptmann +1
We present a tailored framework for solving regularized linear inverse problems using quantum optimization methods. By discretizing the solution space and encoding data fidelity an…
Transformer Causality Regularization for Dynamic Inverse Problems
Gesa Sarnighausen, Anne Wald, Andreas Hauptmann
We study the concept of including the causality principle as regularizer into the solution of linear time-dependent inverse problems. This is achieved by combining transformer-base…
Regularization for time-dependent inverse problems: Geometry of Lebesgue-Bochner spaces and algorithms
Gesa Sarnighausen, Thorsten Hohage, Martin Burger +2
We consider time-dependent inverse problems in a mathematical setting using Lebesgue-Bochner spaces. Such problems arise when one aims to recover a function from given observations…
Inverse Problems with Learned Forward Operators
Simon Arridge, Andreas Hauptmann, Yury Korolev
Solving inverse problems requires the knowledge of the forward operator, but accurate models can be computationally expensive and hence cheaper variants that do not compromise the…