Math. of Deep Learning Seminar: Marylou Gabrie

Tuesday Dec 7, 2021, 11:00 AM → 12:00 PM America/New_York

Title: Assisting Sampling with Learning: Adaptive Monte Carlo with Normalizing Flows

Abstract: In many applications in computational sciences and statistical inference, one seeks to compute expectations on complex high-dimensional distributions. These problems are often plagued by multi-modality/metastability; slow relaxation between unconnected modes leads to slow convergence of estimators of such expectations. In this talk, I will present a strategy to enhance sampling with deep generative models called Normalizing Flows. We will see when simultaneously sampling and training can incur a drastic acceleration of MCMC convergence and discuss current limits in learning for sampling.

This is joint work with Grant Rotskoff (Stanford) and Eric Vanden-Eijnden (Courant Institute, NYU).

 

 

If you would like to attend, please email crampersad@flatironinstitute.org for the Zoom link.