Model Comparisons: XNet Outperforms KAN

Xin Li; Zhihong Jeff Xia; Xiaotao Zheng

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arXiv · 2410.02033

Model Comparisons: XNet Outperforms KAN

Xin Li·Zhihong Jeff Xia·Xiaotao Zheng

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Abstract

In the fields of computational mathematics and artificial intelligence, the need for precise data modeling is crucial, especially for predictive machine learning tasks. This paper explores further XNet, a novel algorithm that employs the complex-valued Cauchy integral formula, offering a superior network architecture that surpasses traditional Multi-Layer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs). XNet significant improves speed and accuracy across various tasks in both low and high-dimensional spaces, redefining the scope of data-driven model development and providing substantial improvements over established time series models like LSTMs.

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Xin Li, Zhihong Jeff Xia, Xiaotao Zheng. 2024-10-02. Model Comparisons: XNet Outperforms KAN. https://arxiv.org/abs/2410.02033

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