BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Joan Bruna (Courant Institute and Center for Data Science\, NYU) DTSTART:20201104T180000Z DTEND:20201104T190000Z DTSTAMP:20260423T003302Z UID:MPML/21 DESCRIPTION:Title: Ma thematical aspects of neural network learning through measure dynamics \nby Joan Bruna (Courant Institute and Center for Data Science\, NYU) as p art of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbst ract\nHigh-dimensional learning remains an outstanding phenomena where exp erimental evidence outpaces our current mathematical understanding\, mostl y due to the recent empirical successes of Deep Learning algorithms. Neura l Networks provide a rich yet intricate class of functions with statistica l abilities to break the curse of dimensionality\, and where physical prio rs can be tightly integrated into the architecture to improve sample effic iency. Despite these advantages\, an outstanding theoretical challenge in these models is computational\, ie providing an analysis that explains suc cessful optimization and generalization in the face of existing worst-case computational hardness results.\n\nIn this talk\, I will focus on the fra mework that lifts parameter optimization to an appropriate measure space. I will cover existing results that guarantee global convergence of the res ulting Wasserstein gradient flows\, as well as recent results that study t ypical fluctuations of the dynamics around their mean field evolution. We will also discuss extensions of this framework beyond vanilla supervised l earning\, to account for symmetries in the function\, as well as for compe titive optimization.\n LOCATION:https://researchseminars.org/talk/MPML/21/ END:VEVENT END:VCALENDAR