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UID:68584a15a35c6a164e60621f68cfffd7
CATEGORIES:Mathematical Physics Seminar
CREATED:20221020T125308
SUMMARY:Learning to sample better
LOCATION:Zoom
DESCRIPTION:<p style="margin: 0in; text-align: left;">Abstract: Sampling high-dimension
 al probability distributions is a common task in computational chemistry, B
 ayesian inference, etc. Markov Chain Monte Carlo (MCMC) is the method of ch
 oice to perform these calculations, but it is often plagued by slow converg
 ence properties. I will discuss how methods from deep learning (DL) can hel
 p enhance the performance of MCMC via a feedback loop in which we simultane
 ously use DL to learn better samplers based e.g. on&nbsp; generative models
 , and MCMC to obtain the data for the training of these models. I will illu
 strate these techniques via several examples, including the sampling of rea
 ction paths in metastable systems and the calculation of free energies and 
 Bayes factors.</p>
CONTACT:Eric Vanden-Eijnden – New York University
DTSTAMP:20260829T151911
DTSTART;TZID=America/New_York:20221019T104500
DTEND;TZID=America/New_York:20221019T114500
SEQUENCE:0
TRANSP:OPAQUE
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