Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed-form optimization criteria for pruning the models for continuous as well as binary data which arise in a challenging real-world material physics problem. The pruned models are optimized to provide compact and interpretable versions of larger models based on informed assumptions about the data distribution. Visualizations of the pruned models provide insight into the atomic configurations that determine atom-level migration processes in solid matter; these results may inform future research on designing more suitable descriptors for use with machine learning algorithms.

许多应用程序，尤其是物理学和其他科学领域的应用程序，都要求使用易于解释和强大的机器学习技术。我们提出了一种完全基于梯度的技术，用于通过有效且可扩展的开源实现来训练径向基函数网络。我们推导了新颖的封闭形式优化准则，用于修剪连续数据和二进制数据的模型，这些模型是在现实世界中具有挑战性的材料物理问题中提出的。根据有关数据分布的明智假设，对经过修剪的模型进行了优化，以提供较大模型的紧凑且可解释的版本。修剪后的模型的可视化可深入了解确定固态物质中原子级迁移过程的原子构型；