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Matching Convolutional Neural Networks without Priors about Data

We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graphs. Our method matches the accuracy of state-of-the-art CNNs when applied on images, without any prior about their 2D regular structure. On fMRI data, we obtain a significant gain in accuracy compared with existing graph-based alternatives.

@inproceedings{LasViaGri2018,
  author = {Carlos Eduardo Rosar Kos Lassance and
Jean-Charles Vialatte and Vincent Gripon},
  title = {Matching Convolutional Neural Networks
without Priors about Data},
  booktitle = {Proceedings of Data Science Workshop},
  year = {2018},
  pages = {234--238},
}