Summary of Lss-skan: Efficient Kolmogorov-arnold Networks Based on Single-parameterized Function, by Zhijie Chen and Xinglin Zhang
LSS-SKAN: Efficient Kolmogorov-Arnold Networks based on Single-Parameterized Function
by Zhijie Chen, Xinglin Zhang
First submitted to arxiv on: 19 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 The proposed Kolmogorov-Arnold Networks (KAN) networks have garnered attention due to their high visualizability compared to Multi-Layer Perceptron (MLP). This paper proposes the Efficient KAN Expansion Principle (EKE Principle), allocating parameters to expand network scale, rather than employing more complex basis functions, leading to more efficient performance improvements in KANs. A superior KAN, SKAN, is proposed, utilizing a single learnable parameter. Various single-parameterized functions for constructing SKANs are evaluated, with LShifted Softplus-based SKANs (LSS-SKANs) demonstrating superior accuracy. The paper then compares LSS-SKAN with other KAN variants on the MNIST dataset, exhibiting superior performance and execution speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at special kinds of computer networks called Kolmogorov-Arnold Networks (KAN). These networks are good because they’re easy to understand. The people who wrote this paper wanted to make them even better. They came up with a way to make the networks grow, but not too much. This helps them work faster and do their job better. They made a new kind of network called SKAN that’s really fast and accurate. They tested it on some images and it did well. |
Keywords
» Artificial intelligence » Attention
