Abstract: Bayesian models are rooted in Bayesian statistics and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. These two fields are perceived as fairly antipodal to each other in their respective communities. It is perhaps astonishing then that most modern deep learning models can be cast as performing approximate inference in a Bayesian setting. The implications of this are profound: we can use the rich Bayesian statistics literature with deep learning models, explain away many of the curiosities with this technique, combine results from deep learning into Bayesian modeling, and much more.
In this talk I will review links between Bayesian modeling and deep learning, and demonstrate the practical impact of the framework with a range of real-world applications in astronomy. I will also explore open problems for future research—problems that stand at the forefront of this new and exciting field.
12:00 noon in Lecture Room 7, Information Engineering Building