no code implementations • 10 Dec 2020 • Christiann Bartelt, Sascha Marton, Heiner Stuckenschmidt
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.