Summary of Fc-kan: Function Combinations in Kolmogorov-arnold Networks, by Hoang-thang Ta et al.
FC-KAN: Function Combinations in Kolmogorov-Arnold Networks
by Hoang-Thang Ta, Duy-Quy Thai, Abu Bakar Siddiqur Rahman, Grigori Sidorov, Alexander Gelbukh
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 proposed FC-KAN model leverages combinations of mathematical functions, including B-splines, wavelets, and radial basis functions, to process low-dimensional data. The authors explore various methods for combining function outputs, such as sum, product, concatenation, and linear transformations. Experimental results show that two variants of FC-KAN outperformed other models on the MNIST and Fashion-MNIST datasets. The model’s ability to combine functions could lead to the design of future Kolmogorov-Arnold Networks (KANs). The authors also compare their proposed FC-KAN with existing KANs, including MLP networks, BSRBF-KAN, EfficientKAN, FastKAN, and FasterKAN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FC-KAN is a new type of machine learning model that helps computers understand low-dimensional data better. It does this by combining different mathematical functions to process the data. The authors tested FC-KAN on two famous datasets: MNIST and Fashion-MNIST. They found that two special versions of FC-KAN were the best at recognizing images. This is exciting because it could lead to even better models in the future. |
Keywords
* Artificial intelligence * Machine learning
