Liquid State Theory Meets Deep Learning and Molecular Informatics

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• Alpha Lee, Cavendish Laboratory
• Tuesday 06 March 2018, 13:00-14:00
• MR11, 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.

This talk is included in these lists:

• All CMS events
• DAMTP Statistical Physics and Soft Matter Seminar
• MR11, CMS
• Soft Matter
• bld31

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