We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach by also considering the model size. Both methods are motivated by recent developments in the deep learning community, and they provide effective means to trade off between model size and prediction accuracy, which is demonstrated in extensive experiments. Furthermore, we contrast quantized BN classifiers with quantized deep neural networks (DNNs) for small-scale scenarios which have hardly been investigated in the literature. We show Pareto optimal models with respect to model size, number of operations, and test error and find that both model classes are viable options.

翻译：我们提出了两种方法来降低巴伊西亚网络(BN)分类的复杂性。 首先,我们采用直通梯度测算器来引入量化测算培训,将BN的参数量化到几个位子。 其次,我们通过考虑模型大小来推广最近提出的一种可区分的树放大天真海湾(TAN)结构学习方法。这两种方法都是由深层学习界的最新发展驱动的,它们提供了在模型大小和预测准确性之间进行交换的有效手段,这在广泛的实验中都得到了证明。 此外,我们将量化的BN分类与量化的深度神经网络(DNNN)对小型情景进行了对比,而文献中几乎没有对此进行过调查。我们展示了Pareto在模型大小、操作数量和测试错误方面的最佳模型,发现两个模型班都是可行的选择。