Title: Latent Stochastic Differential Equations: An Unexplored Model Class.
Abstract: 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