Summary of Reduced Effectiveness Of Kolmogorov-arnold Networks on Functions with Noise, by Haoran Shen and Chen Zeng and Jiahui Wang and Qiao Wang
Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise
by Haoran Shen, Chen Zeng, Jiahui Wang, Qiao Wang
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach is proposed to alleviate the degrading effect of noise on KAN performance. The authors employ kernel filtering based on diffusion maps to pre-filter noisy data for training KAN networks, in combination with oversampling techniques. Experimental results show that increasing the amount of training data leads to a test-loss trend similar to O(r^(-1/2)) as r approaches infinity. While both strategies can reduce noise’s detrimental effects, determining the optimal kernel filtering variance is challenging and expanding the training dataset increases associated costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KANs are powerful tools for many applications, but they struggle when noisy data is involved. This paper tries to fix this problem by cleaning up the noise before using it to train KANs. They use a special kind of filtering that’s based on how data points move around each other, and combine it with making extra copies of the training data. The results show that this combination can make KANs work better even when there’s a lot of noise. However, finding the right balance for the filtering is tricky, and making more data takes up more resources. |
Keywords
» Artificial intelligence » Diffusion
