Summary of Ginn-kan: Interpretability Pipelining with Applications in Physics Informed Neural Networks, by Nisal Ranasinghe et al.
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
by Nisal Ranasinghe, Yu Xia, Sachith Seneviratne, Saman Halgamuge
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a novel approach to building interpretable neural networks, which can provide insights into the learned input-output relationships. The authors propose the concept of interpretability pipelineing, combining multiple techniques to outperform individual methods. They evaluate several architectures, focusing on two recent models: GINN (Growing Interpretable Neural Network) and KAN (Kolmogorov Arnold Networks). A novel model, GINN-KAN, is introduced, which synthesizes the advantages of both models. Experimental results show that GINN-KAN outperforms both GINN and KAN on the Feynman symbolic regression benchmark datasets. The authors also demonstrate the generalizability of this approach by applying it to Physics-Informed Neural Networks (PINNs) for solving partial differential equations. The experiments with GINN-KAN augmented PINNs show that they outperform traditional black-box networks in solving differential equations and surpass the capabilities of both GINN and KAN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes neural networks more transparent by combining different techniques to make them easier to understand. It’s like a puzzle, where you take different pieces (models) and put them together to get a better picture of how the network is working. They test two special models, GINN and KAN, and create a new one that combines the best parts of each. This new model, called GINN-KAN, does even better than the original models. The authors also show that this approach can be used to solve complex math problems, like equations, which is important in science. |
Keywords
» Artificial intelligence » Neural network » Regression
