Recently, partial Bayesian neural networks (pBNNs), which only consider a subset of the parameters to be stochastic, were shown to perform competitively with full Bayesian neural networks. However, pBNNs are often multi-modal in the latent-variable space and thus challenging to approximate with parametric models. To address this problem, we propose an efficient sampling-based training strategy, wherein the training of a pBNN is formulated as simulating a Feynman--Kac model. We then describe variations of sequential Monte Carlo samplers that allow us to simultaneously estimate the parameters and the latent posterior distribution of this model at a tractable computational cost. We show on various synthetic and real-world datasets that our proposed training scheme outperforms the state of the art in terms of predictive performance.

翻译：近期研究表明，仅考虑部分参数为随机变量的部分贝叶斯神经网络（pBNN）在性能上与全贝叶斯神经网络相当。然而，pBNN在潜变量空间中常呈现多模态分布，导致难以用参数化模型进行近似。针对该问题，我们提出一种基于采样的高效训练策略：将pBNN训练过程建模为Feynman–Kac模型模拟。随后我们阐述了多种序贯蒙特卡洛采样器变体，这些变体能够以可控的计算代价同时估计该模型的参数与潜变量后验分布。在多个合成数据集与真实世界数据集上的实验表明，所提出的训练方案在预测性能上优于现有最先进方法。