  Variational inference (VI) and Markov chain Monte Carlo (MCMC) are two main
approximate approaches for learning deep generative models by maximizing
marginal likelihood. In this paper, we propose using annealed importance
sampling for learning deep generative models. Our proposed approach bridges VI
with MCMC. It generalizes VI methods such as variational auto-encoders and
importance weighted auto-encoders (IWAE) and the MCMC method proposed in
(Hoffman, 2017). It also provides insights into why running multiple short MCMC
chains can help learning deep generative models. Through experiments, we show
that our approach yields better density models than IWAE and can effectively
trade computation for model accuracy without increasing memory cost.
