research
∙
08/19/2023
Dynamic Bilevel Learning with Inexact Line Search
In various domains within imaging and data science, particularly when ad...
share
research
∙
06/30/2023
Proximal Langevin Sampling With Inexact Proximal Mapping
In order to solve tasks like uncertainty quantification or hypothesis te...
research
∙
06/29/2023
Designing Stable Neural Networks using Convex Analysis and ODEs
Motivated by classical work on the numerical integration of ordinary dif...
research
∙
01/11/2023
Analyzing Inexact Hypergradients for Bilevel Learning
Estimating hyperparameters has been a long-standing problem in machine l...
research
∙
01/06/2023
Uncertainty Quantification in CT pulmonary angiography
Computed tomography (CT) imaging of the thorax is widely used for the de...
research
∙
10/26/2022
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
Learned regularization for MRI reconstruction can provide complex data-d...
research
∙
09/05/2022
Imaging with Equivariant Deep Learning
From early image processing to modern computational imaging, successful ...
research
∙
07/25/2022
On the convergence and sampling of randomized primal-dual algorithms and their application to parallel MRI reconstruction
The Stochastic Primal-Dual Hybrid Gradient or SPDHG is an algorithm prop...
research
∙
07/22/2021
Regularising Inverse Problems with Generative Machine Learning Models
Deep neural network approaches to inverse imaging problems have produced...
research
∙
02/23/2021
Equivariant neural networks for inverse problems
In recent years the use of convolutional layers to encode an inductive b...
research
∙
01/06/2021
Synergistic Multi-spectral CT Reconstruction with Directional Total Variation
This work considers synergistic multi-spectral CT reconstruction where i...
research
∙
12/02/2020
On the Convergence of the Stochastic Primal-Dual Hybrid Gradient for Convex Optimization
Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle...
research
∙
11/06/2020
Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
Many machine learning solutions are framed as optimization problems whic...
research
∙
07/22/2020
Multi-modality imaging with structure-promoting regularisers
Imaging with multiple modalities or multiple channels is becoming increa...
research
∙
06/23/2020
Inexact Derivative-Free Optimization for Bilevel Learning
Variational regularization techniques are dominant in the field of mathe...
research
∙
06/05/2020
Structure preserving deep learning
Over the past few years, deep learning has risen to the foreground as a ...
research
∙
04/01/2020
Robust Image Reconstruction with Misaligned Structural Information
Multi-modality (or multi-channel) imaging is becoming increasingly impor...
research
∙
06/20/2019
Learning the Sampling Pattern for MRI
The discovery of the theory of compressed sensing brought the realisatio...
research
∙
04/11/2019
Deep learning as optimal control problems: models and numerical methods
We consider recent work of Haber and Ruthotto 2017 and Chang et al. 2018...
research
∙
08/21/2018
Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning
Uncompressed clinical data from modern positron emission tomography (PET...
research
∙
10/04/2017
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
Hyperspectral imaging is a cutting-edge type of remote sensing used for ...
research
∙
06/15/2017