The stars have aligned in a strange way over Sweden and something quite out of the ordinary has happened: a former director of a government authority has started to talk openly about the de facto legislative process that goes on within the executive branch of the government. She seems to be arguing, in court, that her decision to suspend certain laws should have been respected by the judicial system, that it was an everyday occurrence, and that her employment at the highest levels of the executive branch should not have been terminated as a consequence of her actions.
All Post by Björn Smedman
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.
TLDR: Bayes rule is cool. Stable Wi‑Fi rules. The former can give us the latter.