In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampling scheme. By partitioning the parameter space, sampling is possible irrespective of layer width. It is also possible to alleviate vanishing acceptance rates for increasing depth by reducing the proposal variance in deeper layers. Increasing the length of a non-convergent chain increases the predictive accuracy in classification tasks, so avoiding vanishing acceptance rates and consequently enabling longer chain runs have practical benefits. Moreover, non-convergent chain realizations aid in the quantification of predictive uncertainty. An open problem is how to perform minibatch MCMC sampling for feedforward neural networks in the presence of augmented data.

翻译：本文旨在提升前馈神经网络的批量马尔可夫链蒙特卡洛采样的可行性。为此，提出了一种通过分块吉布斯采样方案对参数子集进行采样的方法。通过对参数空间进行划分，无论层宽如何均可实现采样。同时，通过降低深层提议方差，可缓解因网络深度增加而导致的接受率递减问题。延长非收敛链的长度可提升分类任务的预测精度，因此避免接受率递减并实现更长的链运行具有实用价值。此外，非收敛链的样本有助于量化预测不确定性。目前一个悬而未决的问题是：如何在存在增强数据的情况下对前馈神经网络执行批量马尔可夫链蒙特卡洛采样。