BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Maxim Raginsky (University of Illinois Urbana-Champaign) DTSTART:20200519T160000Z DTEND:20200519T173000Z DTSTAMP:20260423T003235Z UID:IASML/1 DESCRIPTION:Title: Ne ural SDEs: deep generative models in the diffusion limit\nby Maxim Rag insky (University of Illinois Urbana-Champaign) as part of IAS Seminar Ser ies on Theoretical Machine Learning\n\n\nAbstract\nIn deep generative mode ls\, the latent variable is generated by a time-inhomogeneous Markov chain \, where at each time step we pass the current state through a parametric nonlinear map\, such as a feedforward neural net\, and add a small indepen dent Gaussian perturbation. In this talk\, based on joint work with Belind a Tzen\, I will discuss the diffusion limit of such models\, where we incr ease the number of layers while sending the step size and the noise varian ce to zero. I will first provide a unified viewpoint on both sampling and variational inference in such generative models through the lens of stocha stic control. Then I will show how we can quantify the expressiveness of d iffusion-based generative models. Specifically\, I will prove that one can efficiently sample from a wide class of terminal target distributions by choosing the drift of the latent diffusion from the class of multilayer fe edforward neural nets\, with the accuracy of sampling measured by the Kull back-Leibler divergence to the target distribution. Finally\, I will brief ly discuss a scheme for unbiased\, finite-variance simulation in such mode ls. This scheme can be implemented as a deep generative model with a rando m number of layers.\n LOCATION:https://researchseminars.org/talk/IASML/1/ END:VEVENT END:VCALENDAR