Deep neural networks (DNNs) have shown exceptional performances in a wide range of tasks and have become the go-to method for problems requiring high-level predictive power. There has been extensive research on how DNNs arrive at their decisions, however, the inherently uninterpretable networks remain up to this day mostly unobservable "black boxes". In recent years, the field has seen a push towards interpretable neural networks, such as the visually interpretable Neural Additive Models (NAMs). We propose a further step into the direction of intelligibility beyond the mere visualization of feature effects and propose Structural Neural Additive Models (SNAMs). A modeling framework that combines classical and clearly interpretable statistical methods with the predictive power of neural applications. Our experiments validate the predictive performances of SNAMs. The proposed framework performs comparable to state-of-the-art fully connected DNNs and we show that SNAMs can even outperform NAMs while remaining inherently more interpretable.

翻译：深度神经网络（DNN）在广泛的任务中展现出卓越的性能，已成为需要高水平预测能力问题的首选方法。关于DNN如何做出决策已有大量研究，然而，这些本质上不可解释的网络至今仍大多是难以观测的“黑箱”。近年来，该领域出现了向可解释神经网络推进的趋势，例如视觉上可解释的神经加性模型（NAMs）。我们朝着超越单纯特征效应可视化的可理解方向迈出了进一步的一步，并提出了结构神经加性模型（SNAMs）。这是一个将经典且明确可解释的统计方法与神经应用的预测能力相结合的建模框架。我们的实验验证了SNAMs的预测性能。所提出的框架性能可与最先进的完全连接DNN相媲美，并且我们表明，SNAMs在保持本质上更可解释的同时，甚至可以优于NAMs。