Björn Smedman

All Post by Björn Smedman

Hybrid Generative-Discriminative Deep Models

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.

Björn Smedman May 28, 2017

Variational Coin Toss

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.

Björn Smedman May 7, 2017

Bayesian Wi‑Fi

TLDR: Bayes rule is cool. Stable Wi‑Fi rules. The former can give us the latter.

Björn Smedman Oct 22, 2016