Summary of Powermlp: An Efficient Version Of Kan, by Ruichen Qiu and Yibo Miao and Shiwen Wang and Lijia Yu and Yifan Zhu and Xiao-shan Gao
PowerMLP: An Efficient Version of KAN
by Ruichen Qiu, Yibo Miao, Shiwen Wang, Lijia Yu, Yifan Zhu, Xiao-Shan Gao
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 Kolmogorov-Arnold Network (KAN) is a neural network architecture known for its high accuracy in function fitting and PDE solving, thanks to the Kolmogorov-Arnold representation theorem and learnable spline functions. However, KAN’s computation of spline functions involves multiple iterations, making it slower than MLPs, increasing training and deployment costs. To address this issue, a novel PowerMLP network is proposed, using simpler non-iterative spline function representation, offering similar training time to MLP while demonstrating stronger expressive power theoretically. The authors compare the FLOPs of KAN and PowerMLP, quantifying the faster computation speed of PowerMLP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new neural network called PowerMLP that’s designed to be faster and more accurate than another model called KAN. KAN is good at certain tasks like fitting functions and solving partial differential equations, but it’s slow because it does lots of repeated calculations. The researchers created PowerMLP to fix this problem by using simpler calculations that don’t take as long. They tested both models on different tasks and found that PowerMLP was generally better and faster. |
Keywords
» Artificial intelligence » Neural network
