BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Francis Bach (Inria\, FR) DTSTART:20200518T130000Z DTEND:20200518T134500Z DTSTAMP:20260423T024538Z UID:OWMADS/9 DESCRIPTION:Title: O n the convergence of gradient descent for wide two-layer neural networks\nby Francis Bach (Inria\, FR) as part of One World seminar: Mathematica l Methods for Arbitrary Data Sources (MADS)\n\n\nAbstract\nMany supervised learning methods are naturally cast as optimization problems. For predict ion models which are linear in their parameters\, this often leads to conv ex problems for which many guarantees exist. Models which are non-linear i n their parameters such as neural networks lead to non-convex optimization problems for which guarantees are harder to obtain. In this talk\, I will consider two-layer neural networks with homogeneous activation functions where the number of hidden neurons tends to infinity\, and show how qualit ative convergence guarantees may be derived. I will also highlight open pr oblems related to the quantitative behavior of gradient descent for such m odels. (Based on joint work with Lénaïc Chizat\, https://arxiv.org/abs/1 805.09545\, https://arxiv.org/abs/2002.04486)\n\nPlease note that this is a joint talk with the One World Optimization Seminar.\n LOCATION:https://researchseminars.org/talk/OWMADS/9/ END:VEVENT END:VCALENDAR