Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though introduce a model specific inhomogenous Poisson Process (IPPs) which is difficult to sample from. This work introduces a new generic and adaptive thinning scheme for sampling from these IPPs, and demonstrates how this approach can accelerate the application of PDMPs for inference in BNNs. Experimentation illustrates how inference with these methods is computationally feasible, can improve predictive accuracy, MCMC mixing performance, and provide informative uncertainty measurements when compared against other approximate inference schemes.

翻译：现代贝叶斯神经网络（BNN）的推断通常依赖于变分推断方法，这种方法施加了独立性假设及后验分布形式的假设，而这些假设往往与实际不符。传统MCMC方法避免了这些假设，但由于其与似然函数子采样不兼容，导致计算成本增加。新型逐段确定性马尔可夫过程（PDMP）采样器支持子采样，但引入了一个难以采样的模型特定非齐次泊松过程（IPP）。本文提出了一种新的通用自适应稀疏化方案，用于从这些IPP中采样，并展示了该方法如何加速PDMP在BNN推断中的应用。实验表明，与其它近似推断方案相比，采用这些方法进行推断在计算上可行，能够提升预测精度、改善MCMC混合性能，并提供具有信息量的不确定性度量。