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Matching Convolutional Neural Networks without Priors about Data
Carlos Eduardo Rosar Kos Lassance, Jean-Charles Vialatte, Vincent Gripon, Nicolas Farrugia
Feb 12, 2018 (modified: Feb 12, 2018)ICLR 2018 Workshop Submissionreaders: everyone
Abstract:We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graph.
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.
Keywords:irregular domains, convolutional neural networks, graph based convolutional neural networks, graphs, deep learning
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