How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with  intractable posterior distributions, and large datasets? Auto-encoding is a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case.