Bayesian deep learning

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 a new theory linking Bayesian modeling and deep
learning and demonstrate the practical impact of the framework with a range
of real-world applications. I will also explore open problems for future
research—problems that stand at the forefront of this new and exciting
field.

• Speaker: Yarin Gal (Oxford)
• Tuesday 13 March 2018, 14:00–15:00
• Venue: Centre for Mathematical Sciences, MR4.
• Series: Mathematics and Machine Learning; organiser: Damon Wischik.