BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Guilio Biroli (ENS Paris) DTSTART:20200729T140000Z DTEND:20200729T150000Z DTSTAMP:20260423T052450Z UID:MADPlus/13 DESCRIPTION:Title: On the benefit of over-parametrization and the origin of double descent c urves in artificial neural networks\nby Guilio Biroli (ENS Paris) as p art of MAD+\n\n\nAbstract\nDeep neural networks have triggered a revolutio n in machine learning\, and more generally in computer science. Understand ing their remarkable performance is a key scientific challenge with many o pen questions. For instance\, practitioners find that using massively over -parameterised networks is beneficial to learning and generalization abili ty. This fact goes against standard theories\, and defies intuition. In th is talk I will address this issue. I will first contrast standard expectat ions based on variance-bias trade-off to the results of numerical experime nts on deep neural networks\, which display a “double-descent” behavio r of the test error when increasing the number of parameters instead of th e traditional U-curve. I will then discuss a theory of this phenomenon bas ed on the solution of simplified models of deep neural networks by statist ical physics methods.\n LOCATION:https://researchseminars.org/talk/MADPlus/13/ END:VEVENT END:VCALENDAR