Model Comparisons: XNet Outperforms KAN

• URL: http://arxiv.org/abs/2410.02033v1
• Date: Wed, 2 Oct 2024 20:59:47 GMT
• Title: Model Comparisons: XNet Outperforms KAN
• Authors: Xin Li, Zhihong Jeff Xia, Xiaotao Zheng,
• Abstract summary: XNet is a novel algorithm that employs the complex-valued Cauchy integral formula. XNet significant improves speed and accuracy across various tasks in both low and high-dimensional spaces.
• Score: 3.9426000822656224
• License: http://creativecommons.org/licenses/by/4.0/
• 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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