NeurIPS Tutorial: Deep Learning with Bayesian Principles

Why Bayesian DL?
Bayesian methods are an invaluable tool for model comparison and for measuring uncertainty in complex models like deep neural networks. The recent blooming of Bayesian approaches applied in the deep learning context already delivers many advantages that a frequentist deep learning approach cannot attain.

Agenda:

• Tutorial streaming & discussion (18:30 - 21:00)

Tutorial link : https://bit.ly/2QNitBT

Official description:
“Deep learning and Bayesian learning are considered two entirely different fields often used in complementary settings. It is clear that combining ideas from the two fields would be beneficial, but how can we achieve this given their fundamental differences?
This tutorial will introduce modern Bayesian principles to bridge this gap. Using these principles, we can derive a range of learning-algorithms as special cases, e.g., from classical algorithms, such as linear regression and forward-backward algorithms, to modern deep-learning algorithms, such as SGD, RMSprop and Adam. This view then enables new ways to improve aspects of deep learning, e.g., with uncertainty, robustness, and interpretation. It also enables the design of new methods to tackle challenging problems, such as those arising in active learning, continual learning, reinforcement learning, etc.
Overall, our goal is to bring Bayesians and deep-learners closer than ever before, and motivate them to work together to solve challenging real-world problems by combining their strengths.”