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03/01/2023
A Lifted Bregman Formulation for the Inversion of Deep Neural Networks
We propose a novel framework for the regularised inversion of deep neura...
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03/01/2023
Trust your source: quantifying source condition elements for variational regularisation methods
Source conditions are a key tool in variational regularisation to derive...
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12/14/2022
Convergent Data-driven Regularizations for CT Reconstruction
The reconstruction of images from their corresponding noisy Radon transf...
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08/18/2022
Lifted Bregman Training of Neural Networks
We introduce a novel mathematical formulation for the training of feed-f...
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09/05/2021
Timbre Transfer with Variational Auto Encoding and Cycle-Consistent Adversarial Networks
This research project investigates the application of deep learning to t...
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12/07/2020
Generalised Perceptron Learning
We present a generalisation of Rosenblatt's traditional perceptron learn...
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08/03/2020
Scanning electron diffraction tomography of strain
Strain engineering is used to obtain desirable materials properties in a...
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08/14/2019
Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI data
Velocity-encoded MRI is an imaging technique used in different areas to ...
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06/24/2019
An entropic Landweber method for linear ill-posed problems
The aim of this paper is to investigate the use of an entropic projectio...
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06/24/2019
An entropic projection method for linear ill-posed problems
The aim of this paper is to investigate the use of an entropic projectio...
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06/20/2019
Learning the Sampling Pattern for MRI
The discovery of the theory of compressed sensing brought the realisatio...
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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...
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03/23/2017
Nonlinear Spectral Image Fusion
In this paper we demonstrate that the framework of nonlinear spectral de...
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08/01/2014
