Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Matching Convolutional Neural Networks without Priors about Data
Carlos Eduardo Rosar Kos Lassance, Jean-Charles Vialatte, Vincent Gripon, Nicolas Farrugia
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
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
Enter your feedback below and we'll get back to you as soon as possible.