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UID:f223e4f9ae040d16168b639c5e048276
CATEGORIES:Mathematical Physics Seminar
CREATED:20221109T130250
SUMMARY:Renormalization Group Theory and Machine Learning 
LOCATION:Zoom
DESCRIPTION:<p>Reconstructing, or generating, high dimensional distributions starting f
 rom data is a central problem in machine learning and data sciences.&nbsp;<
 /p><p>I will present a method — The Wavelet Conditional Renormalization Gro
 up — that combines ideas from physics (renormalization group theory) and co
 mputer science (wavelets, stable representations of operators). The Wavelet
  Conditional Renormalization Group allows to reconstruct in a very efficien
 t way classes of high dimensional distributions hierarchically from large t
 o small spatial scales. I will present the method and then show its applica
 tions to data from statistical physics and cosmology. The Wavelet Condition
 al Renormalization Group Method also provides interesting insights on the i
 nterplay between structures of data and architectures of deep neural networ
 ks.&nbsp;</p>
CONTACT:Giulio Biroli - École normale supérieure (Paris)
DTSTAMP:20260827T020846
DTSTART;TZID=America/New_York:20221123T104500
DTEND;TZID=America/New_York:20221123T114500
SEQUENCE:0
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