Although I do enjoy digging my feet into newer tricks and techniques when competiting, I really like problems that test your understanding of an algorithm or data structure at a deeper level.

In the previous post I explained the basic idea behind using maximum likelihood estimation to fit parametric probability distributions over data.

One of the very fundamental problems that machine learning seeks to solve is trying to predict outcomes under certain degrees of uncertainty.

Following up on the PixelRNN and PixelCNN architectures, we look at a paper that uses the distrib

Although GANs are great at producing realistic images, the distributions they learn are implicit.