Summary of Kolmogorov-arnold Networks Are Radial Basis Function Networks, by Ziyao Li
Kolmogorov-Arnold Networks are Radial Basis Function Networks
by Ziyao Li
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This proof-of-concept demonstrates that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be efficiently approximated by Gaussian radial basis functions, leading to FastKAN. This faster implementation of KAN also serves as a radial basis function (RBF) network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that the complex components of KAN networks can be simplified using Gaussian RBFs, making them faster and more efficient. The resulting FastKAN model is a type of RBF network that maintains the accuracy of KAN while reducing computational complexity. |
