  Particle filtering is a standard Monte-Carlo approach for a wide range of
sequential inference tasks. The key component of a particle filter is a set of
particles with importance weights that serve as a proxy of the true posterior
distribution of some stochastic process. In this work, we propose continuous
latent particle filters, an approach that extends particle filtering to the
continuous-time domain. We demonstrate how continuous latent particle filters
can be used as a generic plug-in replacement for inference techniques relying
on a learned variational posterior. Our experiments with different model
families based on latent neural stochastic differential equations demonstrate
superior performance of continuous-time particle filtering in inference tasks
like likelihood estimation and sequential prediction for a variety of
stochastic processes.
