Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <arXiv:1704.02685> and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) <arXiv:1706.03825>, 'Gradient x Input' described by Baehrens et al. (2009) <arXiv:0912.1128> or 'Vanilla Gradient'.
Package details |
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Author | Niklas Koenen [aut, cre] (<https://orcid.org/0000-0002-4623-8271>), Raphael Baudeu [ctb] |
Maintainer | Niklas Koenen <niklas.koenen@gmail.com> |
License | MIT + file LICENSE |
Version | 0.3.0 |
URL | https://bips-hb.github.io/innsight/ https://github.com/bips-hb/innsight/ |
Package repository | View on CRAN |
Installation |
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