Summary of On Training Of Kolmogorov-arnold Networks, by Shairoz Sohail
On Training of Kolmogorov-Arnold Networks
by Shairoz Sohail
First submitted to arxiv on: 8 Nov 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 Medium Difficulty Summary: This paper investigates the training dynamics of Kolmogorov-Arnold Networks (KANs) compared to multi-layer Perceptron (MLP) architectures. The authors train various KAN and MLP formulations using different initialization schemes, optimizers, and learning rates, as well as back propagation-free approaches like the HSIC Bottleneck. The results show that KANs are a viable alternative to MLPs on high-dimensional datasets, with better parameter efficiency but more unstable training dynamics. The authors provide recommendations for improving training stability in larger KAN models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research looks at how to train special kinds of artificial neural networks called Kolmogorov-Arnold Networks (KANs). Scientists compare these KANs to a different type of network called multi-layer Perceptrons (MLPs) to see which one works better. They try out different ways to start the training process, use different computers to help with the calculations, and even try a new way of learning that doesn’t need backpropagation. The results show that KANs can be useful for certain types of problems, but they can be tricky to train. The scientists give advice on how to make the training process more stable. |
Keywords
» Artificial intelligence » Backpropagation
