Summary of Kan Versus Mlp on Irregular or Noisy Functions, by Chen Zeng et al.
KAN versus MLP on Irregular or Noisy Functions
by Chen Zeng, Jiahui Wang, Haoran Shen, Qiao Wang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); 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 This paper compares the performance of Kolmogorov-Arnold Networks (KAN) and Multi-Layer Perceptron (MLP) networks on irregular or noisy functions. The study controls for parameters and training samples to ensure a fair comparison across six types of functions: regular, continuous with local non-differentiable points, jump discontinuities, singularities, coherent oscillations, and noisy. Results show that KAN doesn’t always outperform MLP, which excels on certain function types. Increasing training sample size improves performance, but noise can obscure features, hindering both models’ effectiveness. This study provides valuable insights for future neural network research, encouraging further investigations to overcome these challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares two kinds of artificial intelligence networks: KAN and MLP. They’re tested on tricky functions that are hard to predict. The researchers make sure the tests are fair by controlling some things. They find that sometimes one network is better than the other. When there’s extra noise in the data, it makes it harder for both networks to do well. This study helps us understand how these networks work and what we can do to make them better. |
Keywords
» Artificial intelligence » Neural network
