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SUMMARY:Marylou Gabrié (Center for Data Science\, NYU and Flatiron Instit
 ute\, CCM)
DTSTART:20200723T163000Z
DTEND:20200723T173000Z
DTSTAMP:20260423T003240Z
UID:MPML/12
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/12/">Pr
 ogress and hurdles in the statistical mechanics of deep learning</a>\nby M
 arylou Gabrié (Center for Data Science\, NYU and Flatiron Institute\, CCM
 ) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n
 \nAbstract\nUnderstanding the great performances of deep neural networks i
 s a very active direction of research with contributions coming from a wid
 e variety of fields. The statistical mechanics of learning is a theoretica
 l framework dating back to the 80s studying learning problems from a physi
 cist viewpoint and using tools from the physics of disordered systems. In 
 this talk\, I will first go over this traditional framework\, which relies
  on the teacher-student scenario\, bayesian analysis and mean-field approx
 imations. Then I will discuss some recent advances in the corresponding an
 alysis of modern deep neural network\, and highlight remaining challenges.
 \n
LOCATION:https://researchseminars.org/talk/MPML/12/
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