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UID:f223e4f9ae040d16168b639c5e048276
CATEGORIES:Mathematical Physics Seminar
CREATED:20221109T130250
SUMMARY:Renormalization Group Theory and Machine Learning 
LOCATION:Zoom
DESCRIPTION:Reconstructing, or generating, high dimensional distributions starting from
  data is a central problem in machine learning and data sciences. \nI will 
 present a method — The Wavelet Conditional Renormalization Group — that com
 bines ideas from physics (renormalization group theory) and computer scienc
 e (wavelets, stable representations of operators). The Wavelet Conditional 
 Renormalization Group allows to reconstruct in a very efficient way classes
  of high dimensional distributions hierarchically from large to small spati
 al scales. I will present the method and then show its applications to data
  from statistical physics and cosmology. The Wavelet Conditional Renormaliz
 ation Group Method also provides interesting insights on the interplay betw
 een structures of data and architectures of deep neural networks. \n
X-ALT-DESC;FMTTYPE=text/html:<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:20260827T005737
DTSTART;TZID=America/New_York:20221123T104500
DTEND;TZID=America/New_York:20221123T114500
SEQUENCE:0
TRANSP:OPAQUE
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