Model Comparisons: XNet Outperforms KAN
<|reference_start|>Model Comparisons: XNet Outperforms KAN: 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.<|reference_end|>
@article{li2024model,
    title={Model Comparisons: XNet Outperforms KAN},
    author={Xin Li, Zhihong Jeff Xia, Xiaotao Zheng},
    journal={arXiv preprint arXiv:2410.02033},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02033},
    primaryClass={cs.LG cs.AI}
}