Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data.

翻译：贝叶斯流网络（Bayesian Flow Networks, BFNs）是近期提出的最具前景的通用生成建模方向之一，能够学习任意数据类型。其优势源于神经网络的表达力与贝叶斯推理的结合，这使其适用于持续学习场景。本文深入探究了BFNs的内在机制，并通过实验实证验证了其对非平稳数据的生成能力。