Corpus ID: 214693381

A copula-based visualization technique for a neural network

@article{Kubo2020ACV,
  title={A copula-based visualization technique for a neural network},
  author={Y. Kubo and Y. Komori and T. Okuyama and H. Tokieda},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.12317}
}
  • Y. Kubo, Y. Komori, +1 author H. Tokieda
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Interpretability of machine learning is defined as the extent to which humans can comprehend the reason of a decision. However, a neural network is not considered interpretable due to the ambiguity in its decision-making process. Therefore, in this study, we propose a new algorithm that reveals which feature values the trained neural network considers important and which paths are mainly traced in the process of decision-making. In the proposed algorithm, the score estimated by the correlation… CONTINUE READING

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