7 citations · 28 across the 26 of their papers we have counts for
6 papers · 1 filter
Bregman Itoh--Abe methods for sparse optimisation
Martin Benning, Erlend S. Riis, Carola-Bibiane Schönlieb
In this paper we propose optimisation methods for variational regularisation problems based on discretising the inverse scale space flow with discrete gradient methods. Inverse sca…
On Biased Stochastic Gradient Estimation
Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb
We present a uniform analysis of biased stochastic gradient methods for minimizing convex, strongly convex, and non-convex composite objectives, and identify settings where bias is…
Deep learning as optimal control problems: models and numerical methods
Martin Benning, Elena Celledoni, Matthias J. Ehrhardt +2
We consider recent work of Haber and Ruthotto 2017 and Chang et al. 2018, where deep learning neural networks have been interpreted as discretisations of an optimal control problem…
Improving "Fast Iterative Shrinkage-Thresholding Algorithm": Faster, Smarter and Greedier
Jingwei Liang, Tao Luo, Carola-Bibiane Schönlieb
The "fast iterative shrinkage-thresholding algorithm", a.k.a. FISTA, is one of the most well-known first-order optimisation scheme in the literature, as it achieves the worst-case…
Faster FISTA
Jingwei Liang, Carola-Bibiane Schönlieb
The ``fast iterative shrinkage-thresholding algorithm'', a.k.a. FISTA, is one of the most widely used algorithms in the literature. However, despite its optimal theoretical $O(1/k^…
Directional Sinogram Inpainting for Limited Angle Tomography
Robert Tovey, Martin Benning, Christoph Brune +5
In this paper we propose a new joint model for the reconstruction of tomography data under limited angle sampling regimes. In many applications of Tomography, e.g. Electron Microsc…