  In this paper, I present VQ-DRAW, an algorithm for learning compact discrete
representations of data. VQ-DRAW leverages a vector quantization effect to
adapt the sequential generation scheme of DRAW to discrete latent variables. I
show that VQ-DRAW can effectively learn to compress images from a variety of
common datasets, as well as generate realistic samples from these datasets with
no help from an autoregressive prior.
