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arXiv:2311.00049cs.NE

On the Kolmogorov neural networks

Aysu Ismayilova, Vugar Ismailov

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Abstract

In this paper, we show that the Kolmogorov two hidden layer neural network model with a continuous, discontinuous bounded or unbounded activation function in the second hidden layer can precisely represent continuous, discontinuous bounded and all unbounded multivariate functions, respectively.

arXiv comments
14 pages, 1 figure; this article uses material from arXiv:2012.03016
Journal reference
DOI
Subjects
cs.NENeural and Evolutionary Computing cs.LGMachine Learning math.FAFunctional Analysis stat.MLMachine Learning (Statistics)

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from May 2024 to May 2024. Found in 1 HN submission from Nov 2023 to Nov 2023. Open submission discussions.

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  1. Kolmogorov-Arnold Networks 1 comment

    • Very interesting! Kolmogorov neutral networks can represent discontinuous functions [1], but I've wondered about how practically applicable they are. This repo seems to show that they have some use after all. [1]: https://arxiv.org/abs/2311.00049

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