Summary of Kolmogorov-arnold Transformer, by Xingyi Yang et al.
Kolmogorov-Arnold Transformer
by Xingyi Yang, Xinchao Wang
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 Transformer (KAT) architecture replaces traditional multi-layer perceptron (MLP) layers with Kolmogorov-Arnold Network (KAN) layers, enhancing expressiveness and performance. To address challenges in integrating KANs into transformers, such as base function optimization, parameter and computation inefficiency, and weight initialization, the authors propose rational basis, group KAN, and variance-preserving initialization solutions. These designs enable KAT to scale effectively and outperform traditional MLP-based transformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Kolmogorov-Arnold Transformer (KAT) is a new way of doing something in deep learning. Traditional methods use something called multi-layer perceptron (MLP) layers, but this can be slow. The authors came up with a new idea that replaces these layers with something else called Kolmogorov-Arnold Network (KAN) layers. This makes the model better and faster. To make it work well, they had to fix some problems, like making sure it works on computers and making sure the weights are initialized correctly. They came up with solutions for these problems and showed that their new method is better than the old one. |
Keywords
» Artificial intelligence » Deep learning » Optimization » Transformer
