University of Cambridge > Talks.cam > DAMTP Statistical Physics and Soft Matter Seminar > Liquid State Theory Meets Deep Learning and Molecular Informatics

Liquid State Theory Meets Deep Learning and Molecular Informatics

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  • UserAlpha Lee, Cavendish Laboratory
  • ClockTuesday 06 March 2018, 13:00-14:00
  • HouseMR11, CMS.

If you have a question about this talk, please contact Professor Mike Cates.

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.

This talk is part of the DAMTP Statistical Physics and Soft Matter Seminar series.

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