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Talks and Seminars

Liquid State Theory Meets Deep Learning and Molecular Informatics

A large class of problems in machine learning pertains to making sense of high dimensional and unlabelled data. The challenge lies in separating direct variable-variable interactions (e.g. cause and effect) and transitive correlations, as well as removing noise due to insufficient number of samples relative to the number of variables. In this talk, I will discuss an Ornstein-Zernike-like approach for data analysis that disentangles correlations in datasets using ideas from the theory of liquids. The Ornstein-Zernike closure is parameterised by deep learning, and a framework inspired by random matrix theory is used to remove finite sampling noise. I will illustrate this approach by applying it to problems such as ligand-based virtual screening and predicting protein function from sequence covariation.

Further information


Oct 25th 2017
14:15 to 15:15


Department of Chemistry, Cambridge, Unilever lecture theatre
(Getting to the Department)