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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=nn2poly to link to this page.
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