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17 papers
Greedy Learning to Optimize with Convergence Guarantees
Patrick Fahy, Mohammad Golbabaee, Matthias J. Ehrhardt
Learning to optimize (L2O) is an approach that leverages training data to accelerate the solution of optimization problems. Many approaches use unrolling to parametrize the update…
PET Rapid Image Reconstruction Challenge (PETRIC)
Casper da Costa-Luis, Matthias J. Ehrhardt, Christoph Kolbitsch +4
Introduction: We describe the foundation of PETRIC, an image reconstruction challenge to minimise the computational runtime of related algorithms for Positron Emission Tomography (…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
Bilevel Learning via Inexact Stochastic Gradient Descent
Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts +1
Bilevel optimization is a central tool in machine learning for high-dimensional hyperparameter tuning. Its applications are vast; for instance, in imaging it can be used for learni…
Stable neural networks and connections to continuous dynamical systems
Matthias J. Ehrhardt, Davide Murari, Ferdia Sherry
The existence of instabilities, for example in the form of adversarial examples, has given rise to a highly active area of research concerning itself with understanding and enhanci…
A primal-dual algorithm for image reconstruction with input-convex neural network regularizers
Matthias J. Ehrhardt, Subhadip Mukherjee, Hok Shing Wong
We address the optimization problem in a data-driven variational reconstruction framework, where the regularizer is parameterized by an input-convex neural network (ICNN). While gr…