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 Bayesian deep ensemble approach as an effective solution with competitive performance and uncertainty quantification.

翻译：样本推断在贝叶斯神经网络中的主要挑战在于网络的参数空间规模与结构。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关联，即可实现有效的样本推断。基于大量实验，我们建立了采样与收敛诊断的实用准则。最终提出一种贝叶斯深度集成方法，该方案在竞争性性能与不确定性量化方面表现优异。