seqKAN提升序列建模精度与可解释性，尤其在外推任务中表现优异。
seqKAN: Sequence processing with Kolmogorov-Arnold Networks
- 基于柯尔莫哥洛夫-阿诺德网络构建序列处理新架构，更贴近原框架核心思想
- 在物理模拟数据上外推任务误差显著低于对比模型，且优于循环神经网络和符号回归方法
- 兼具高透明度与强泛化能力，适合需要可解释性的科学计算场景
Kolmogorov-Arnold Networks（KANs）作为一种新型机器学习框架，相比多层感知机具有更强的可解释性与可控性。已有多种KAN架构被提出用于不同任务，包括序列处理。本文提出seqKAN，一种针对序列建模的新KAN架构。尽管已有多个序列处理KAN模型，我们主张seqKAN更忠实地遵循了KAN框架的核心理念。通过在复杂物理问题生成的数据集上进行插值与外推任务的实证评估，我们将seqKAN与先前的时序预测KAN、循环深度网络及符号回归方法进行了比较。结果表明，seqKAN在所有架构中表现最佳，尤其在外推任务中优势显著，同时具备最高的透明度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。