  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.
