May 14, 2020

Bayesian inference with Stochastic Gradient Langevin Dynamics

Modern machine learning algorithms can scale to enormous datasets and reach superhuman accuracy on specific tasks. Yet, they are largely incapable of answering “I don’t know” when queried with new data. Taking a Bayesian approach to learning lets models be uncertain about their predictions, but classical Bayesian methods do not scale to modern settings. In this post we are going to use Julia to explore Stochastic Gradient Langevin Dynamics (SGLD), an algorithm which makes it possible to apply Bayesian learning to deep learning models and still train them on a GPU with mini-batched data. Read more

© Sebastian Callh 2020