xRAI: Explainable Representations through AI

• URL: http://arxiv.org/abs/2012.06006v1
• Date: Thu, 10 Dec 2020 22:49:29 GMT
• Title: xRAI: Explainable Representations through AI
• Authors: Christiann Bartelt and Sascha Marton and Heiner Stuckenschmidt
• Abstract summary: We present an approach for extracting symbolic representations of the mathematical function a neural network was supposed to learn from the trained network. The approach is based on the idea of training a so-called interpretation network that receives the weights and biases of the trained network as input and outputs the numerical representation of the function the network was supposed to learn that can be directly translated into a symbolic representation.
• Score: 10.345196226375455
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present xRAI an approach for extracting symbolic representations of the mathematical function a neural network was supposed to learn from the trained network. The approach is based on the idea of training a so-called interpretation network that receives the weights and biases of the trained network as input and outputs the numerical representation of the function the network was supposed to learn that can be directly translated into a symbolic representation. We show that interpretation nets for different classes of functions can be trained on synthetic data offline using Boolean functions and low-order polynomials as examples. We show that the training is rather efficient and the quality of the results are promising. Our work aims to provide a contribution to the problem of better understanding neural decision making by making the target function explicit

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