In machine learning, the two dominating approaches to learning generative models of data has mostly been based on either directed graphical models or undirected graphical models. In this talk, I'll discuss a third approach, which has become more popular only recently: autoregressive generative models. Thanks to neural networks, this family of models has been shown to be very competitive, both in terms of the realism of the data they can generate and the data representation they can learn.

I'll discuss a variety of such neural autoregressive models and dissect the advantages and disadvantages of this approach.