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KAN: Kolmogorov-Arnold Networks
May 1, 2024, 4:42 a.m. | Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Solja\v{c}i\'c, Thomas Y. Hou, Max Tegmark
cs.LG updates on arXiv.org arxiv.org
Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, …
abstract arxiv cond-mat.dis-nn cs.ai cs.lg every function functions layer linear networks neurons nodes representation stat.ml theorem type while
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