As machine learning enthusiasts, we’re always curious about the latest advancements in the field. One area that’s garnered significant attention is Bayesian deep learning (BDL). The question is, have BDL methods achieved state-of-the-art performance in any area? Not just in terms of metrics like accuracy, but also in calibration and uncertainty quantification.
I’m interested in exploring this topic further because BDL has the potential to provide more robust and reliable results. By incorporating Bayesian principles, we can better quantify uncertainty and make more informed decisions.
Deep ensembles are a popular approach to uncertainty quantification, but have BDL methods surpassed them in any aspect? If so, I’d love to see the paper and results. Let’s dive into the world of BDL and uncover its potential.
What are your thoughts on BDL? Have you come across any groundbreaking research or applications?