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Implicit Generative Prior for Bayesian Neural Networks
April 30, 2024, 4:42 a.m. | Yijia Liu, Xiao Wang
cs.LG updates on arXiv.org arxiv.org
Abstract: Predictive uncertainty quantification is crucial for reliable decision-making in various applied domains. Bayesian neural networks offer a powerful framework for this task. However, defining meaningful priors and ensuring computational efficiency remain significant challenges, especially for complex real-world applications. This paper addresses these challenges by proposing a novel neural adaptive empirical Bayes (NA-EB) framework. NA-EB leverages a class of implicit generative priors derived from low-dimensional distributions. This allows for efficient handling of complex data structures and …
abstract applications arxiv bayesian challenges computational cs.lg decision domains efficiency framework generative however making networks neural networks novel paper predictive prior quantification stat.ap type uncertainty world
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