Machine Learning Landscapes

Lead Research Organisation: University of Cambridge
Department Name: Chemistry

Abstract

This project will involve the characterisation of machine learning landscapes for neural networks. The theory and numerical methods developed for exploring molecular energy landscapes will be applied to the non-convex landscapes defined by the loss function of an artificial neural network. In particular, we will investigate analogues of the density of states to define thermodynamic quantities and rate constants. The resulting insight will be used to define combinations of solutions that provide superior predictive power for applications ranging from molecular geometry optimisation to clinician diagnostic support.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509620/1 30/09/2016 29/09/2022
2275899 Studentship EP/N509620/1 30/09/2019 30/03/2023 Conor Thomas Cafolla