1 citations · 2 across the 2 of their papers we have counts for
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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…
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…
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…
Efficient gradient-based methods for bilevel learning via recycling Krylov subspaces
Matthias J. Ehrhardt, Silvia Gazzola, Sebastian J. Scott
Many optimization problems require hyperparameters, i.e., parameters that must be pre-specified in advance, such as regularization parameters and parametric regularizers in variati…
An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation
Lea Bogensperger, Matthias J. Ehrhardt, Thomas Pock +2
We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minim…
Complex extension of optical flow and its practical evaluation for undersampled dynamic MRI
Matthias J. Ehrhardt, Marco Mauritz
Reconstructing high-quality images from undersampled dynamic MRI data is a challenging task and important for the success of this imaging modality. To remedy the naturally occurrin…