We consider the problem of explaining a tractable deep probabilistic model, the Sum-Product Networks (SPNs).To this effect, we define the notion of a context-specific independence tree and present an iterative algorithm that converts an SPN to a CSI-tree. The resulting CSI-tree is both interpretable and explainable to the domain expert. To further compress the tree, we approximate the CSIs by fitting a supervised classifier. Our extensive empirical evaluations on synthetic, standard, and real-world clinical data sets demonstrate that the resulting models exhibit superior explainability without loss in performance.

翻译：我们考虑了解释一种可移植的深度概率模型,即总产值网络(SPNs)的问题。为此,我们界定了针对具体情况的独立树的概念,并提出了将SPN转换为CSI树的迭代算法。由此形成的CSI树既可以解释,也可以向域专家解释。为了进一步压缩树,我们用一个受监督的分类师来比喻CSI。我们对合成、标准和真实世界的临床数据集的广泛经验评估表明,所产生的模型在不损及性能的情况下表现出较好的可解释性。