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. Using various synthetic and real-world datasets we show 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）在全贝叶斯神经网络中展现出竞争性表现。然而，pBNN在潜变量空间中常呈现多峰分布，因此难以通过参数化模型进行近似。针对该问题，我们提出一种基于高效采样的训练策略，将pBNN的训练过程构建为模拟费曼-卡克模型。接着，我们描述了序贯蒙特卡罗采样器的多种变体，这些方法能够以可承受的计算代价同时估计该模型的参数与潜变量后验分布。通过多个合成数据集与真实数据集实验表明，我们提出的训练方案在预测性能上优于当前最先进方法。