Reviews: Deep State Space Models for Unconditional Word Generation

This paper introduces a probabilistic model for unconditional word generation that uses state space models whose distributions are parameterized with deep neural networks. Normalizing flows are used to define flexible distributions both in the generative model and in the inference network. To improve inference the inference networks uses samples from the prior SSM transitions borrowing ideas from importance-weighted autoencoders. I enjoyed reading this paper, as it gives many useful insights on deep state space models and more in general on probabilistic models for sequential data. Also, it introduces novel ways of parameterizing the inference network by constructing a variational approximation over the noise term rather than the state.