Latent SDEs on Homogeneous Spaces

arXiv:2306.16248v3 Announce Type: replace
Abstract: We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE). Motivated by the challenges that arise when trying to learn an (almost arbitrary) latent neural SDE from data, such as efficient gradient computation, we take a step back and study a specific subclass instead. In our case, the SDE evolves on a homogeneous …

abstract arxiv bayesian bayesian inference challenges cs.lg data differential differential equation equation inference latent variable model learn process solution spaces stochastic stochastic process type