Summary of Kolmogorov-arnold Networks in Low-data Regimes: a Comparative Study with Multilayer Perceptrons, by Farhad Pourkamali-anaraki
Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons
by Farhad Pourkamali-Anaraki
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO); Machine Learning (stat.ML)
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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 presents a comprehensive study comparing Multilayer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs) from algorithmic and experimental perspectives, focusing on low-data regimes. The authors introduce a technique for designing MLPs with parameterized activation functions for each neuron, allowing for a balanced comparison with KANs. Empirical evaluations on simulated data and real-world datasets from medicine and engineering demonstrate the trade-offs between model complexity and accuracy, highlighting the role of network depth. Results show that MLPs with individualized activation functions achieve higher predictive accuracy with only a modest increase in parameters, particularly when sample size is limited to around one hundred. For example, in a three-class classification problem within additive manufacturing, MLPs reach a median accuracy of 0.91, significantly outperforming KANs (0.53) with default hyperparameters. This study offers valuable insights into the impact of activation function selection on neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research compares two types of artificial intelligence models called Multilayer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs). The authors want to know which type works better when we have limited data. They found that MLPs with special activation functions perform better than KANs in many cases, especially when we don’t have a lot of information. For instance, they tested these models on some real-world data from medicine and engineering, and the MLPs did much better at predicting what would happen next. This study can help us make better artificial intelligence models that work well even with limited data. |
Keywords
» Artificial intelligence » Classification
