Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks

Understand

Large neural networks trained on large datasets have become the dominant paradigm in machine learning.

• These systems rely on maximum likelihood point estimates of their parameters, precluding them from expressing model uncertainty.
• This may result in overconfident predictions and it prevents the use of deep learning models for sequential decision making.
• This thesis develops scalable methods to equip neural networks with model uncertainty.