Summary of Gaussian Process Kolmogorov-arnold Networks, by Andrew Siyuan Chen
Gaussian Process Kolmogorov-Arnold Networks
by Andrew Siyuan Chen
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A probabilistic extension is introduced to Kolmogorov Arnold Networks (KANs) by incorporating Gaussian Process (GP) as non-linear neurons in this paper, referred to as GP-KAN. This approach allows for a fully analytical treatment of the output distribution of one GP as an input to another GP, enabling robust non-linear modelling capabilities while using few parameters. The GP neurons provide inherent uncertainty estimates to model predictions and can be trained directly on the log-likelihood objective function without requiring variational lower bounds or approximations. In MNIST classification, a GP-KAN model with 80 thousand parameters achieved 98.5% prediction accuracy, outperforming current state-of-the-art models with 1.5 million parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make artificial neural networks more powerful and accurate by combining two powerful machine learning tools: Kolmogorov Arnold Networks (KANs) and Gaussian Processes (GPs). This new approach, called GP-KAN, allows computers to learn from data in a smarter and more flexible way. The result is a model that can make very good predictions about handwritten digits, like the ones you might write on a piece of paper. |
Keywords
* Artificial intelligence * Classification * Log likelihood * Machine learning * Objective function
