Summary of Exploring the Potential Of Polynomial Basis Functions in Kolmogorov-arnold Networks: a Comparative Study Of Different Groups Of Polynomials, by Seyd Teymoor Seydi
Exploring the Potential of Polynomial Basis Functions in Kolmogorov-Arnold Networks: A Comparative Study of Different Groups of Polynomials
by Seyd Teymoor Seydi
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper presents a comprehensive survey of 18 distinct polynomials and their potential applications in Kolmogorov-Arnold Network (KAN) models, offering an alternative to traditional spline-based methods. The polynomials are classified into various groups based on their mathematical properties, such as orthogonal polynomials, hypergeometric polynomials, q-polynomials, Fibonacci-related polynomials, combinatorial polynomials, and number-theoretic polynomials. The study investigates the suitability of these polynomials as basis functions in KAN models for complex tasks like handwritten digit classification on the MNIST dataset. The performance metrics of the KAN models, including overall accuracy, Kappa, and F1 score, are evaluated and compared. The Gottlieb-KAN model achieves the highest performance across all metrics, suggesting its potential as a suitable choice for the given task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at different types of polynomials and how they can be used in computer models called Kolmogorov-Arnold Networks (KAN). These models are used to help machines learn from data. The study shows that some polynomials work better than others when it comes to classifying handwritten numbers. This is important because it could help us make better computers. |
Keywords
» Artificial intelligence » Classification » F1 score
