nn2poly
Neural Network Weights Transformation into Polynomial Coefficients
Implements a method that builds the coefficients of a polynomial model that performs almost equivalently as a given neural network (densely connected). This is achieved using Taylor expansion at the activation functions. The obtained polynomial coefficients can be used to explain features (and their interactions) importance in the neural network, therefore working as a tool for interpretability or eXplainable Artificial Intelligence (XAI). See Morala et al. 2021 <doi:10.1016/j.neunet.2021.04.036>, and 2023 <doi:10.1109/TNNLS.2023.3330328>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.3 |
rolling linux/jammy R-4.5 | nn2poly_0.1.3.tar.gz |
514.7 KiB |
0.1.3 |
rolling linux/noble R-4.5 | nn2poly_0.1.3.tar.gz |
516.4 KiB |
0.1.3 |
rolling source/ R- | nn2poly_0.1.3.tar.gz |
557.6 KiB |
0.1.3 |
latest linux/jammy R-4.5 | nn2poly_0.1.3.tar.gz |
514.7 KiB |
0.1.3 |
latest linux/noble R-4.5 | nn2poly_0.1.3.tar.gz |
516.4 KiB |
0.1.3 |
latest source/ R- | nn2poly_0.1.3.tar.gz |
557.6 KiB |
0.1.3 |
2026-04-26 source/ R- | nn2poly_0.1.3.tar.gz |
557.6 KiB |
0.1.3 |
2026-04-23 source/ R- | nn2poly_0.1.3.tar.gz |
557.6 KiB |
0.1.3 |
2026-04-09 windows/windows R-4.5 | nn2poly_0.1.3.zip |
834.3 KiB |
0.1.2 |
2025-04-20 source/ R- | nn2poly_0.1.2.tar.gz |
421.7 KiB |