no code implementations • 1 Feb 2024 • Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb
Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced performance.
no code implementations • 9 Oct 2023 • Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb
An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data.
1 code implementation • 27 May 2023 • Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross Anderson
It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images.
1 code implementation • NeurIPS 2021 • Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross Anderson
Machine learning is vulnerable to a wide variety of attacks.
1 code implementation • 6 Aug 2020 • Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.