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An Inside Look at Deep Neural Networks using Graph Signal Processing

V. Gripon, A. Ortega and B. Girault, "An Inside Look at Deep Neural Networks using Graph Signal Processing," in Proceedings of ITA, February 2018.

Deep Neural Networks (DNNs) are state-of-the-art in many machine learning benchmarks. Understanding how they perform is a major open question. In this paper, we are interested in using graph signal processing to monitor the intermediate representations obtained in a simple DNN architecture. We compare different metrics and measures and show that smoothness of label signals on k-nearest neighbor graphs are a good candidate to interpret individual layers role in achieving good performance.

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Bibtex
@inproceedings{GriOrtGir20182,
  author = {Vincent Gripon and Antonio Ortega and
Benjamin Girault},
  title = {An Inside Look at Deep Neural Networks
using Graph Signal Processing},
  booktitle = {Proceedings of ITA},
  year = {2018},
  month = {February},
}




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