9 citations · 48 across the 28 of their papers we have counts for
7 papers · 1 filter
Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI
Eric B. Gutierrez, Claire Delplancke, Matthias J. Ehrhardt
The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale optimization problems.…
Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
Matthias J. Ehrhardt, Lindon Roberts
Many machine learning solutions are framed as optimization problems which rely on good hyperparameters. Algorithms for tuning these hyperparameters usually assume access to exact s…
A temporal multiscale approach for MR Fingerprinting
Samuel Cortinhas, Mohammad Golbabaee, Matthias J. Ehrhardt
Quantitative MRI (qMRI) is becoming increasingly important for research and clinical applications, however, state-of-the-art reconstruction methods for qMRI are computationally pro…
Multi-modality imaging with structure-promoting regularisers
Matthias J. Ehrhardt
Imaging with multiple modalities or multiple channels is becoming increasingly important for our modern society. A key tool for understanding and early diagnosis of cancer and deme…
Structure preserving deep learning
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann +4
Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing…
Inexact Derivative-Free Optimization for Bilevel Learning
Matthias J. Ehrhardt, Lindon Roberts
Variational regularization techniques are dominant in the field of mathematical imaging. A drawback of these techniques is that they are dependent on a number of parameters which h…