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的推理通常依赖变分推断方法，但该方法常需引入独立性假设及后验形式假设，这些假设往往与实际不符。传统MCMC方法虽能避免此类假设，却因无法利用似然子采样而导致计算成本显著增加。新型分段确定性马尔可夫过程采样器虽支持子采样，但需要从针对特定模型构建的非齐次泊松过程中进行采样，该过程难以直接实施。本文提出了一种通用且自适应的稀疏化采样方案，用于从上述非齐次泊松过程中采样，并展示了该方法如何加速分段确定性马尔可夫过程在贝叶斯神经网络推理中的应用。实验结果表明，与其他近似推理方案相比，基于本方法推理不仅在计算上可行，还能提升预测精度与MCMC混合性能，并提供更具信息量的不确定性度量。