论文标题
EXSPLINET：一个可解释的基于样条的神经网络
ExSpliNet: An interpretable and expressive spline-based neural network
论文作者
论文摘要
在本文中，我们提出了一种可解释和表现力的神经网络模型Exsplinet。该模型结合了Kolmogorov神经网络，概率树的合奏以及多元B-Spline表示形式的想法。我们给出了模型的概率解释，并显示其通用近似属性。我们还讨论了如何通过利用B-Spline属性来有效地编码它。最后，我们测试了提出模型对合成近似问题和经典机器学习基准数据集的有效性。
In this paper we present ExSpliNet, an interpretable and expressive neural network model. The model combines ideas of Kolmogorov neural networks, ensembles of probabilistic trees, and multivariate B-spline representations. We give a probabilistic interpretation of the model and show its universal approximation properties. We also discuss how it can be efficiently encoded by exploiting B-spline properties. Finally, we test the effectiveness of the proposed model on synthetic approximation problems and classical machine learning benchmark datasets.
