CVKAN: Complex-Valued Kolmogorov-Arnold Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914172849094656 |
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| author | Wolff, Matthias Eilers, Florian Jiang, Xiaoyi |
| author_facet | Wolff, Matthias Eilers, Florian Jiang, Xiaoyi |
| contents | In this work we propose CVKAN, a complex-valued Kolmogorov-Arnold Network (KAN), to join the intrinsic interpretability of KANs and the advantages of Complex-Valued Neural Networks (CVNNs). We show how to transfer a KAN and the necessary associated mechanisms into the complex domain. To confirm that CVKAN meets expectations we conduct experiments on symbolic complex-valued function fitting and physically meaningful formulae as well as on a more realistic dataset from knot theory. Our proposed CVKAN is more stable and performs on par or better than real-valued KANs while requiring less parameters and a shallower network architecture, making it more explainable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02417 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CVKAN: Complex-Valued Kolmogorov-Arnold Networks Wolff, Matthias Eilers, Florian Jiang, Xiaoyi Machine Learning In this work we propose CVKAN, a complex-valued Kolmogorov-Arnold Network (KAN), to join the intrinsic interpretability of KANs and the advantages of Complex-Valued Neural Networks (CVNNs). We show how to transfer a KAN and the necessary associated mechanisms into the complex domain. To confirm that CVKAN meets expectations we conduct experiments on symbolic complex-valued function fitting and physically meaningful formulae as well as on a more realistic dataset from knot theory. Our proposed CVKAN is more stable and performs on par or better than real-valued KANs while requiring less parameters and a shallower network architecture, making it more explainable. |
| title | CVKAN: Complex-Valued Kolmogorov-Arnold Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.02417 |