Sweden has chosen a nearly unique path through the COVID-19 pandemic, one that is raising eyebrows around the world. The official story is that our strategy is unique because we are a unique country, with high levels of trust in experts and the government. But unfortunately the truth seems to be that our strategy is based on an incorrect assumption. One that will cost us dearly.
Deep discriminative classifiers perform remarkably well on problems with a lot of labeled data. So-called deep generative models tend to excel when labeled training data is scarce. Can we do a hybrid, combining the best of both worlds? In this post I outline a hybrid generative-discriminative deep model loosely based on the importance weighted autoencoder (Burda et al., 2015). Don’t miss the pretty pictures.
Variational inference is all the rage these days, with new interesting papers coming out almost daily. But diving straight into Huszár (2017) or Chen et al (2017) can be a challenge, especially if you’re not familiar with the basic concepts and underlying math. Since it’s often easier to approach a new method by first applying it to a known problem I thought I’d walk you through variational inference applied to the classic “unfair coin” problem.
First of all, a big thanks for the great response to the previous post in this series. For a brief time it occupied the top spot in Reddit’s /r/Statistics and /r/MachineLearning.
TLDR: Bayes rule is cool. Stable Wi‑Fi rules. The former can give us the latter.
Every year about two hundred scholars are welcomed to the Institute for Advanced Study in Princeton, USA. Former faculty include Albert Einstein, John von Neumann and Kurt Gödel. Calling it an exclusive club would of course be an understatement.