Kolmogorov-Arnold Networks are Radial Basis Function Networks
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911873181417472 |
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| author | Li, Ziyao |
| author_facet | Li, Ziyao |
| contents | This short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions. Doing so leads to FastKAN, a much faster implementation of KAN which is also a radial basis function (RBF) network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kolmogorov-Arnold Networks are Radial Basis Function Networks Li, Ziyao Machine Learning Artificial Intelligence This short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions. Doing so leads to FastKAN, a much faster implementation of KAN which is also a radial basis function (RBF) network. |
| title | Kolmogorov-Arnold Networks are Radial Basis Function Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.06721 |