We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (SMC) for performing inference in structured probabilistic models and the flexibility of deep neural networks to model complex conditional probability distributions. We develop additional theoretical insights and introduce a new training procedure which improves both model and proposal learning. We demonstrate that our approach provides a fast, easy-to-implement and scalable means for simultaneous model learning and proposal adaptation in deep generative models.
We build on auto-encoding sequential Monte Carlo, gain new theoretical insights and develop an improved training procedure based on those insights. The paper proposes a version of IWAE-style training that uses SMC instead of classical importance sampling. This work proposes auto-encoding sequential Monte Carlo (SMC), extending the VAE framework to a new Monte Carto objective based on SMC. 