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SUMMARY:Lenka Zdeborova (CNRS)
DTSTART:20200527T140000Z
DTEND:20200527T150000Z
DTSTAMP:20260423T035417Z
UID:MADPlus/4
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MADPlus/4/">
 Understanding machine learning via exactly solvable statistical physics mo
 dels</a>\nby Lenka Zdeborova (CNRS) as part of MAD+\n\n\nAbstract\nThe aff
 inity between statistical physics and machine learning has a long history\
 , this is reflected even in the machine learning terminology that is in pa
 rt adopted from physics. I will describe the main lines of this long-lasti
 ng friendship in the context of current theoretical challenges and open qu
 estions about deep learning. Theoretical physics often proceeds in terms o
 f solvable synthetic models\, I will describe the related line of work on 
 solvable models of simple feed-forward neural networks. I will highlight a
  path forward to capture the subtle interplay between the structure of the
  data\, the architecture of the network\, and the learning algorithm.\n
LOCATION:https://researchseminars.org/talk/MADPlus/4/
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