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SUMMARY:Liquid State Theory Meets Deep Learning and Molecular Informatics 
 - Dr Alpha Lee\, University of Cambridge\, Department of Physics
DTSTART:20171025T131500Z
DTEND:20171025T141500Z
UID:TALK77331@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:A large class of problems in machine learning pertains to maki
 ng sense of high dimensional and unlabelled data. The challenge lies in se
 parating direct variable-variable interactions (e.g. cause and effect) and
  transitive correlations\, as well as removing noise due to insufficient n
 umber of samples relative to the number of variables. In this talk\, I wil
 l discuss an Ornstein-Zernike-like approach for data analysis that disenta
 ngles correlations in datasets using ideas from the theory of liquids. The
  Ornstein-Zernike closure is parameterised by deep learning\, and a framew
 ork inspired by random matrix theory is used to remove finite sampling noi
 se. I will illustrate this approach by applying it to problems such as lig
 and-based virtual screening and predicting protein function from sequence 
 covariation.  
LOCATION:Department of Chemistry\, Cambridge\, Unilever lecture theatre
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