Summary of Larctan-skan: Simple and Efficient Single-parameterized Kolmogorov-arnold Networks Using Learnable Trigonometric Function, by Zhijie Chen and Xinglin Zhang
LArctan-SKAN: Simple and Efficient Single-Parameterized Kolmogorov-Arnold Networks using Learnable Trigonometric Function
by Zhijie Chen, Xinglin Zhang
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper proposes a novel approach for designing Single-Parameterized Kolmogorov-Arnold Networks (SKAN) using trigonometric functions. The authors develop three new SKAN variants: LSin-SKAN, LCos-SKAN, and LArctan-SKAN. Experimental validation on the MNIST dataset demonstrates that LArctan-SKAN excels in both accuracy and computational efficiency. Specifically, LArctan-SKAN improves test set accuracy over existing models, outperforming pure KAN variants like FourierKAN and mixed MLP-based models such as MLP+rKAN and MLP+fKAN. Additionally, LArctan-SKAN exhibits remarkable computational efficiency with a training speed increase of 535.01% and 49.55% compared to MLP+rKAN and MLP+fKAN, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to design neural networks called Single-Parameterized Kolmogorov-Arnold Networks (SKAN). They use special functions like sine and cosine to make the network work better. The new SKAN versions they create do really well on a big dataset of pictures, beating other types of networks in both how accurate it is and how fast it trains. |
