Summary of Nn2poly: An R Package For Converting Neural Networks Into Interpretable Polynomials, by Pablo Morala (1 and 2) et al.
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials
by Pablo Morala, Jenny Alexandra Cifuentes, Rosa E. Lillo, Iñaki Ucar
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The nn2poly package is an R implementation of the NN2Poly method, which explains and interprets feed-forward neural networks using polynomial representations that mimic the original network’s behavior. This approach enables capturing interactions between variables and their effects on output, a crucial aspect often missing from Explainable Artificial Intelligence (XAI) methods. The package integrates with popular deep learning frameworks in R, such as TensorFlow and Torch, allowing users to apply the NN2Poly algorithm seamlessly. Additionally, nn2poly provides weight constraints for network training and supports other neural networks packages by accepting weights in list format. Polynomial representations can be used for predictions or visualizations using the package’s built-in plot method. The paper also includes simulations demonstrating the usage of nn2poly alongside a comparison with other R-based approaches for neural network interpretation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The nn2poly package helps us understand how artificial intelligence works by turning complex neural networks into simple math equations. This is called Explainable Artificial Intelligence (XAI). Most XAI methods don’t show how variables work together, which is important to know. The nn2poly package fixes this by creating polynomial equations that do the same job as the original network. It’s easy to use and works with popular tools like TensorFlow and Torch. You can even visualize the results or use them to make new predictions. |
Keywords
» Artificial intelligence » Deep learning » Neural network
