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>.
| Version: | 0.1.2 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp, generics, matrixStats, pracma |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | keras, tensorflow, reticulate, luz, torch, cowplot, ggplot2, patchwork, testthat (≥ 3.0.0), vdiffr, knitr, rmarkdown |
| Published: | 2024-11-11 |
| DOI: | 10.32614/CRAN.package.nn2poly |
| Author: | Pablo Morala 
[aut, cre],
Iñaki Ucar [aut],
Jose Ignacio Diez [ctr] |
| Maintainer: | Pablo Morala <moralapablo at gmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://ibidat.github.io/nn2poly/ |
| NeedsCompilation: | yes |
| Citation: | nn2poly citation info |
| Materials: | README NEWS |
| CRAN checks: | nn2poly results |
Documentation:
| Reference manual: | nn2poly.pdf |
| Vignettes: |
Introduction to nn2poly (source) Supported DL frameworks (source) Classification example using tensorflow (source) |
Downloads:
| Package source: | nn2poly_0.1.2.tar.gz |
| Windows binaries: | r-devel: nn2poly_0.1.2.zip, r-release: nn2poly_0.1.2.zip, r-oldrel: nn2poly_0.1.2.zip |
| macOS binaries: | r-release (arm64): nn2poly_0.1.2.tgz, r-oldrel (arm64): nn2poly_0.1.2.tgz, r-release (x86_64): nn2poly_0.1.2.tgz, r-oldrel (x86_64): nn2poly_0.1.2.tgz |
| Old sources: | nn2poly archive |
Linking:
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