Summary of Activation Space Selectable Kolmogorov-arnold Networks, by Zhuoqin Yang et al.
Activation Space Selectable Kolmogorov-Arnold Networks
by Zhuoqin Yang, Jiansong Zhang, Xiaoling Luo, Zheng Lu, Linlin Shen
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Selectable KAN (S-KAN) outperforms baseline methods in seven representative function fitting tasks and surpasses MLP methods with similar parameters. S-KAN employs an adaptive strategy to choose the possible activation mode for data at each feedforward node, addressing the reduced performance of previous KAN-based works across different tasks. The activation space selectable Convolutional KAN (S-ConvKAN) also achieves leading results on four general image classification datasets, demonstrating the potential of feedforward KANs with selectable activations to match or exceed MLP-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new type of artificial intelligence called Selectable KAN. It’s like a special kind of computer program that can learn and improve over time. The authors wanted to make this program better, so they came up with a way to choose the right “activation” (like a switch) for each step in the process. This makes it work even better than before! They tested it on lots of different tasks and it did really well. Then, they took it a step further and applied it to images, where it also performed very well. |
Keywords
» Artificial intelligence » Image classification
