Interpreting Deep Neural Networks with the Package innsight

• URL: http://arxiv.org/abs/2306.10822v2
• Date: Thu, 18 Jan 2024 20:13:41 GMT
• Title: Interpreting Deep Neural Networks with the Package innsight
• Authors: Niklas Koenen, Marvin N. Wright
• Abstract summary: innsight is generally the first R package implementing feature attribution methods for neural networks. It operates independently of the deep learning library allowing the interpretation of models from any R package. Innsight benefits internally from the torch package's fast and efficient array calculations.
• Score: 0.951828574518325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks' predictions with so-called feature attribution methods. Aside from the unified and user-friendly framework, the package stands out in three ways: It is generally the first R package implementing feature attribution methods for neural networks. Secondly, it operates independently of the deep learning library allowing the interpretation of models from any R package, including keras, torch, neuralnet, and even custom models. Despite its flexibility, innsight benefits internally from the torch package's fast and efficient array calculations, which builds on LibTorch $-$ PyTorch's C++ backend $-$ without a Python dependency. Finally, it offers a variety of visualization tools for tabular, signal, image data or a combination of these. Additionally, the plots can be rendered interactively using the plotly package.

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