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We propose a promising alternative to MLPs, called Kolmogorov-Arnold Networks (KANs). Whereas MLPs are inspired by the universal approximation theorem, KANs are inspired by the Kolmogorov-Arnold representation theorem [ 6, 7]. Like MLPs, KANs have fully-connected structures.
A curated list of awesome libraries, projects, tutorials, papers, and other resources related to Kolmogorov-Arnold Network (KAN). This repository aims to be a comprehensive and organized collection that will help researchers and developers in the world of KAN!
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May 1, 2024 · Dubbed the Kolmogorov-Arnold Network (KAN), this innovative framework from MIT is poised to transform traditional models with its unique approach. The Traditional Foundation: Multi-Layer ...
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May 4, 2024 · Kolmogorov–Arnold Networks (KANs), a new type of neural network inspired by the Kolmogorov-Arnold representation theorem, as an alternative to Multi-Layer perceptron (MLPs). Here are the key...
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May 9, 2024 · Inspired by the Kolmogorov-Arnold Representation Theorem, Kolmogorov-Arnold Networks (KAN) bring a breath of fresh air to neural networks by addressing these limitations. Designed as an...
Apr 30, 2024 · 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").
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