Summary of Rkan: Rational Kolmogorov-arnold Networks, by Alireza Afzal Aghaei
rKAN: Rational Kolmogorov-Arnold Networks
by Alireza Afzal Aghaei
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The development of Kolmogorov-Arnold networks (KANs) revolutionizes traditional multi-layer perceptrons in deep learning. Initially, KANs utilized B-spline curves as primary basis functions, but their complexity hindered implementation. Researchers explored alternative basis functions like Wavelets, Polynomials, and Fractional functions. This study introduces rational functions as a novel basis function for KANs, proposing two approaches: Pade approximation and rational Jacobi functions. The resulting rational KAN (rKAN) is evaluated in various deep learning and physics-informed tasks to demonstrate its practicality and effectiveness in function approximation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists create a new type of neural network called Kolmogorov-Arnold networks (KANs). They use different “building blocks” or basis functions to make these networks. Initially, they used something called B-spline curves, but that was hard to work with. So, they looked for other options like Wavelets, Polynomials, and Fractional functions. Now, they’re trying a new idea: using rational functions (like fractions) as the building blocks. They show how this works in different tasks, like deep learning and solving physics problems. |
Keywords
* Artificial intelligence * Deep learning * Neural network
