Summary of Fkan: Fractional Kolmogorov-arnold Networks with Trainable Jacobi Basis Functions, by Alireza Afzal Aghaei
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions
by Alireza Afzal Aghaei
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 neural network architecture called the Fractional Kolmogorov-Arnold Network (fKAN) is proposed, which incorporates the characteristics of Kolmogorov-Arnold Networks (KANs) with a trainable adaptive fractional-orthogonal Jacobi function as its basis function. The fKAN enhances learning efficiency and accuracy by leveraging the unique properties of fractional Jacobi functions. Evaluation on various tasks in deep learning and physics-informed deep learning demonstrates improved training speed and performance across diverse fields, including synthetic regression data, image classification, image denoising, sentiment analysis, ordinary differential equations, partial differential equations, and fractional delay differential equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new type of neural network called the Fractional Kolmogorov-Arnold Network (fKAN). It combines ideas from two other networks to make it better. This fKAN is special because it can learn quickly and accurately. The researchers tested this idea on lots of different problems, like predicting numbers, recognizing pictures, and cleaning noisy images. They even used it to solve math problems! Overall, the fKAN did really well on all these tasks. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Image denoising » Neural network » Regression
