Machine Learning

In addition to the projects highlighted below, we are part of the project Maths4DL - Mathematics for Deep Learning. It aims to put deep learning onto a firm mathematical grounding, and will combine theory, modelling, data and computation to help unlock the next generation of deep learning. This project is in collaboration with the University of Bath and University College London.

Continuum Limits in Graphical Based Machine Learning Problems

Researcher: Matthew Thorpe, Jeff Calder, Riccardo Cristoferi, Olly Crook, Matt Cunlop, Nicolás García Trillos, Tim Hurst, Ryan Murray, Carola-Bibiane Schönlieb, Dejan Slepcev, Andrew Stuart, Florian Theil, Kostas Zygalakis

Image Classification Under Minimal Supervision: Graph-Based Semi-Supervised Learning for Real-World Large-Scale Problems.

Reseacher: Angelica I. Aviles-Rivero, N. Papadakis (IMB, Université Bordeaux), R. Li (NUS), RT Tan (Yale-NUS), SM Alsaleh (GWU), P. Sellars (DAMTP, University of Cambridge), Q. Fan (Stanford University), C-B Schönlieb (DAMTP, University of Cambridge)

Learning Sampling Patterns for MRI

Researcher: Ferdia Sherry