I am trying to reproduce the paper Synthetic Petri Dish: A novel surrogate model for Rapid Architecture Search. In the paper, the authors try to reduce the architecture of an MLP model trained on MNIST (2 layers - 100 neurons) by initializing a motif network from it, that is, 2 layers, 1 neuron each, and extracting the sigmoid function. I have been searching a lot, but I have not found the answer of how can someone extract an 'architectural motif' from a trained neural network.

  • $\begingroup$ Hi. Welcome to AI SE. Have you already looked into github.com/uber-research/Synthetic-Petri-Dish? If you find the answer in this Github repo or somewhere else, feel free to write a formal answer below to your own question ;) $\endgroup$ – nbro Jan 13 at 17:04
  • $\begingroup$ @nbro Hello, thanks for welcoming me. Indeed I did but unfortunately they did not include the motif extraction part in their code. What they do only is display the motif training points - which they define in their paper as tuple of slope values and validation accuracy. $\endgroup$ – Perl Jan 13 at 17:10
  • $\begingroup$ Maybe the best thing to do is open an issue in the issue tracker of that repo, if nobody provides an answer meanwhile. $\endgroup$ – nbro Jan 13 at 17:12

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