doi:10.1016/j.neunet.2021.04.036>, and 2023 <doi:10.1109/TNNLS.2023.3330328>.">

nn2poly: Neural Network Weights Transformation into Polynomial Coefficients (original) (raw)

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 ORCID iD [aut, cre], Iñaki Ucar ORCID iD [aut], Jose Ignacio Diez [ctr]
Maintainer: Pablo Morala
License: MIT + file
URL: https://ibidat.github.io/nn2poly/
NeedsCompilation: yes
Citation: nn2poly citation info
Materials: README NEWS
CRAN checks: nn2poly results

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