Bayesian Model Selection in Deep Learning


The Bayesian Deep Learning community is widely known for its efforts in bringing uncertainty estimates to deep neural networks. However, Bayesian methods have another key advantage: the ability to adjust inductive biases through model selection. Interestingly, model selection and uncertainty estimation are dual problems in the Bayesian framework. In this talk, I will discuss the current state of model selection in Bayesian deep learning, together with some of my recent work towards this. I will discuss some theoretically grounded successes in Deep Gaussian Processes and in connecting ensembling to Bayesian inference, as well as recent empirical work on Neural Architecture Search. To finish, I would like to speculate on possible other benefits that the Bayesian framework can provide, in particular relating to asynchronous computation, and how this can potentially benefit from new hardware.

Feb 24, 2021 4:00 PM
Qualcomm Amsterdam Seminar Series
Qualcomm Amsterdam (Online)