On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors

arXiv:2403.08609v1 Announce Type: new
Abstract: Achieving robust uncertainty quantification for deep neural networks represents an important requirement in many real-world applications of deep learning such as medical imaging where it is necessary to assess the reliability of a neural network's prediction. Bayesian neural networks are a promising approach for modeling uncertainties in deep neural networks. Unfortunately, generating samples from the posterior distribution of neural networks is a major challenge. One significant advance in that direction would be the incorporation of …

abstract applications arxiv bayesian convergence cs.lg deep learning diffusion imaging medical medical imaging network networks neural network neural networks prediction quantification reliability robust sampling scalable stat.ml type uncertainty world