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