Research Fellowship Seminar – Dr Junyu Xuan
FEIT Research Excellence Public Lecture: Functional Bayesian Deep Learning
Functional Bayesian Deep Learning
with Dr Junyu Xuan
Bayesian deep learning (BDL) is an emerging field that combines the strong function approximation power of deep learning with the uncertainty modelling capabilities of Bayesian inference. This synergy is poised to enhance model generalization and robustness, offering valuable uncertainty estimations for a range of safety-critical applications, including medical diagnostics, diabetes detection, autonomous driving, and civil aviation.
Despite these advantages, the fusion introduces complexities to classical posterior inference in parameter space, such as nonmeaningful priors, intricate posteriors, and possible pathologies. This talk will delve into the driving forces, concepts, and methodologies underpinning BDL in function space, segueing into pivotal technological breakthroughs and their applications in machine learning tasks. To conclude, we will explore the prevailing hurdle faced by BDL.
