A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size and structure of the networks' parameter space. Our work shows that successful SBI is possible by embracing the characteristic relationship between weight and function space, uncovering a systematic link between overparameterization and the difficulty of the sampling problem. Through extensive experiments, we establish practical guidelines for sampling and convergence diagnosis. As a result, we present a deep ensemble initialized approach as an effective solution with competitive performance and uncertainty quantification.

翻译：贝叶斯神经网络基于样本推断面临的主要挑战在于网络参数空间的规模与结构。本研究表明，通过把握权重空间与函数空间之间的特征关系，揭示过参数化与采样问题难度之间的系统性联系，成功的基于样本推断是可能实现的。通过大量实验，我们建立了采样与收敛诊断的实用准则。最终，我们提出一种深度集成初始化方法作为有效解决方案，该方法在性能与不确定性量化方面均具有竞争力。