David Duvenaud’s Talk

Hi, everyone! We have a guest speaker for our Seminar, and you are all cordially invited to the AMLab Seminar on Tuesday 24th November at 16:00 CET on Zoom, where‪ David Duvenaud will give a talk titled “Latent Stochastic Differential Equations for Irregularly-Sampled Time Series”.

Title: Latent Stochastic Differential Equations for Irregularly-Sampled Time Series

Abstract: Much real-world data is sampled at irregular intervals, but most time series models require regularly-sampled data. Continuous-time models address this problem, but until now only deterministic (ODE) models or linear-Gaussian models were efficiently trainable with millions of parameters. We construct a scalable algorithm for computing gradients of samples from stochastic differential equations (SDEs), and for gradient-based stochastic variational inference in function space, all with the use of adaptive black-box SDE solvers. This allows us to fit a new family of richly-parameterized distributions over time series. We apply latent SDEs to motion capture data, and to construct infinitely-deep Bayesian neural networks.

The technical details are in this paper: https://arxiv.org/abs/2001.01328 And the code is available at: https://github.com/google-research/torchsde

To gain more deep insights into neural stochastic differential equations, feel free to join and discuss it! See you there!

Bio: David Duvenaud is an assistant professor in computer science at the University of Toronto. His research focuses on continuous-time models, latent-variable models, and deep learning. His postdoc was done at Harvard University, and his Ph.D. at the University of Cambridge. David also co-founded Invenia, an energy forecasting and trading company.