# Autoregressive Autoencoders

You might think that I'd be bored with autoencoders by now but I still
find them extremely interesting! In this post, I'm going to be explaining
a cute little idea that I came across in the paper MADE: Masked Autoencoder
for Distribution Estimation.
Traditional autoencoders are great because they can perform unsupervised
learning by mapping an input to a latent representation. However, one
drawback is that they don't have a solid probabilistic basis
(of course there are other variants of autoencoders that do, see previous posts
here,
here, and
here).
By using what the authors define as the *autoregressive property*, we can
transform the traditional autoencoder approach into a fully probabilistic model
with very little modification! As usual, I'll provide some intuition, math and
an implementation.