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CBL Alumni Talk: Latent Stochastic Differential Equations: An Unexplored Model Class.

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If you have a question about this talk, please contact Elre Oldewage.

We show how to do gradient-based stochastic variational inference in stochastic differential equations (SDEs), in a way that allows the use of adaptive SDE solvers. This allows us to scalably fit a new family of richly-parameterized distributions over irregularly-sampled time series. We apply latent SDEs to motion capture data, and to demonstrate infinitely-deep Bayesian neural networks. We also discuss the pros and cons of this barely-explored model class, comparing it to Gaussian processes and neural processes.

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

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 company.

This talk is part of the Machine Learning Reading Group @ CUED series.

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