Kolmogorov-Arnold Networks (KANs) have been recently proposed as a machine learning framework that is more interpretable and controllable than the multi-layer perceptron. Various network architectures have been proposed within the KAN framework targeting different tasks and application domains, including sequence processing. This paper proposes seqKAN, a new KAN architecture for sequence processing. Although multiple sequence processing KAN architectures have already been proposed, we argue that seqKAN is more faithful to the core concept of the KAN framework. Furthermore, we empirically demonstrate that it achieves better results. The empirical evaluation is performed on generated data from a complex physics problem on an interpolation and an extrapolation task. Using this dataset we compared seqKAN against a prior KAN network for timeseries prediction, recurrent deep networks, and symbolic regression. seqKAN substantially outperforms all architectures, particularly on the extrapolation dataset, while also being the most transparent.

本研究解决了现有序列处理网络在解释性和可控性方面的不足，提出了一种新的科尔莫哥罗夫-阿诺德网络架构——seqKAN。该架构更忠实于KAN框架的核心概念，并在复杂物理问题的插值和外推任务上显示出优越的性能，特别是在外推数据集上，显著超越了其他架构，具有更高的透明性。

seqKAN：使用科尔莫哥罗夫-阿诺德网络进行序列处理