Lessons from Sampling Bayesian Neural Networks | SLT Seminar скачать в хорошем качестве

Lessons from Sampling Bayesian Neural Networks | SLT Seminar

In the SLT seminar, Julius Kobialka, David Rügamer and Emanuel Sommer tell us about a series of papers on sampling from Bayesian Neural Networks. Abstract: Sampling-based inference is often regarded as the gold standard for posterior inference in Bayesian neural networks (BNNs), yet it continues to face skepticism regarding its practicality in large-scale or complex models. This perception has been challenged by recent methodological and computational advances that significantly broaden the scope of feasible applications. The presentation examines how sampling operates in BNNs, how performance can be improved through targeted adaptations, and why not all sampling procedures are equally effective. It further explores the role of implicit regularization induced by both the network architecture and the sampling dynamics. The discussion points toward future opportunities where sampling may redefine Bayesian deep learning, contingent on addressing current challenges in scalability, efficiency, and inference cost. Join the seminar: https://zachfurman.com/singular-learn... Email list: https://groups.google.com/g/singular-... Discord:   / discord   Recorded on 8 August 2025.