Model Comparisons: XNet Outperforms KAN

Xiaotao Zheng; Xin Li; Zhihong Jeff Xia

Model Comparisons: XNet Outperforms KAN

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arxiv 2410.02033 v1 pith:746EGUJO submitted 2024-10-02 cs.LG cs.AI

Xin Li , Zhihong Jeff Xia , Xiaotao Zheng This is my paper · ORCID

classification cs.LGcs.AI

keywords xnetmodeltasksaccuracyacrossalgorithmarchitectureartificial

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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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