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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.02033 |
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| _version_ | 1866914963333840896 |
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| author | Li, Xin Xia, Zhihong Jeff Zheng, Xiaotao |
| author_facet | Li, Xin Xia, Zhihong Jeff Zheng, Xiaotao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02033 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model Comparisons: XNet Outperforms KAN Li, Xin Xia, Zhihong Jeff Zheng, Xiaotao Machine Learning Artificial Intelligence 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. |
| title | Model Comparisons: XNet Outperforms KAN |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.02033 |