Abstract
This chapter begins with a review of variational inference (VI) as a fast approximation alternative to Markov Chain Monte Carlo (MCMC) methods, solving an optimization problem for approximating the posterior. VI is scaled to stochastic variational inference and generalized to black-box variational inference (BBVI). Amortized VI leads to the variational auto-encoder (VAE) framework which is introduced using deep neural networks and graphical models and used for learning representations and generative modeling. Finally, we explore generative flows, the latent space manifold, and Riemannian geometry of generative models.
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