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In this episode, University of Edinburgh Phd student George Papamakarios and I discuss his paper "Masked Autoregressive Flow for Density Estimation."
George walks us through the idea of Masked Autoregressive Flow, which uses neural networks to produce estimates of probability densities from a set of input examples. We discuss some of the related work that's laid the groundwork for his research, including Inverse Autoregressive Flow, Real NVP and Masked Auto-encoders. We also look at the properties of probability density networks and discuss some of the challenges associated with this effort.
