Posts

Variational autoencoders are usually introduced as a probabilistic extension of autoencoders with regularization. An alternative view is that the encoder arises naturally as a tool for efficiently training the decoder. This is the perspective I take in this post, deriving VAEs without assuming an autoencoder architecture a priori.

“Structure” is a concept that keeps popping up when thinking about mathematics but it’s hard to pin down what it is exactly. I discuss several different perspectives for thinking about it.

The Karger-Stein algorithm is an improvement over Karger’s beautiful contraction algorithm for minimum graph cuts. In this post, I show how it finds the perfect tradeoff between finding a mincut with high probability and finding it quickly. In the course of doing so, we will also understand where the somewhat opaque factor of sqrt(2) comes from.

For people who want to discount the future, special relativity creates some challenges. There are different ways to handle those but none seem completely satisfactory which may be yet another argument against discounting pure utilities.