# Variational Autoencoders with Inverse Autoregressive Flows

In this post, I'm going to be describing a really cool idea about how to improve variational autoencoders using inverse autoregressive flows. The main idea is that we can generate more powerful posterior distributions compared to a more basic isotropic Gaussian by applying a series of invertible transformations. This, in theory, will allow your variational autoencoder to fit better by concentrating the stochastic samples around a closer approximation to the true posterior. The math works out so nicely while the results are kind of marginal [1]. As usual, I'll go through some intuition, some math, and have an implementation with few experiments I ran. Enjoy!