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SUMMARY:Boltzmann Generators and Stochastic Normalizing Flows - Vincent St
 imper\, Michael Bromberg
DTSTART:20200401T100000Z
DTEND:20200401T113000Z
UID:TALK141382@talks.cam.ac.uk
CONTACT:Robert Pinsler
DESCRIPTION:In statistical physics\, systems are modeled by a probability 
 distribution\, i.e. the Boltzmann distribution. Being able to draw samples
  from it is of utmost importance for many applications such as analyzing p
 hysical processes as well as drug discovery. Current state of the art samp
 ling procedures such as molecular dynamics simulation and Markov Chain Mon
 te Carlo methods can only generate sequences of highly correlated samples\
 , leaving large parts of the potential landscape unexplored. In this talk\
 , we will present a paper introducing Boltzmann generators which is a meth
 od to approximate Boltzmann distributions with a normalizing flow in order
  to draw independent samples from it. Furthermore\, we will discuss a foll
 ow up work about a novel flow based model\, called Stochastic Normalizing 
 Flows\, which is supposedly\, among other merits\, even better suited to d
 raw independent samples from Boltzmann distributions.
LOCATION:Hangouts Meet (Link provided via e-mail)
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