9.3 Latest methods in BDL
In this book, we’ve introduced some of the core techniques used within BDL: Bayes by Backprop (BBB), Probabilistic Backpropagation (PBP), Monte-Carlo dropout (MC dropout), and deep ensembles. Many BNN approaches you’ll encounter in the literature will be based on one of these methods, and having these under your belt provides you with a versatile toolbox of approaches for developing your own BDL solutions. However, as with all aspects of machine learning, the field of BDL is progressing rapidly, and new techniques are being developed on a regular basis. In this section, we’ll explore a selection of recent developments from the field.
9.3.1 Combining MC dropout and deep ensembles
Why use just one Bayesian neural network technique when you could use two? This is exactly the approach taken by University of Edinburgh researchers Remus Pop and Patric Fulop in their paper, Deep Ensemble Bayesian Active Learning: Addressing the Mode Collapse...
