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  4. Structured Sequence Modeling with Graph Convolutional Recurrent Networks
 
conference paper not in proceedings

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

Seo, Youngjoo  
•
Defferrard, Michaël  
•
Vandergheynst, Pierre  
Show more
2017

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling. The proposed model combines convolutional neural networks (CNN) on graphs to identify spatial structures and RNN to find dynamic patterns. We study two possible architectures of GCRN, and apply the models to two practical problems: predicting moving MNIST data, and modeling natural language with the Penn Treebank dataset. Experiments show that exploiting simultaneously graph spatial and dynamic information about data can improve both precision and learning speed.

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Type
conference paper not in proceedings
ArXiv ID

1612.07659

Author(s)
Seo, Youngjoo  
Defferrard, Michaël  
Vandergheynst, Pierre  
Bresson, Xavier  
Date Issued

2017

Editorial or Peer reviewed

NON-REVIEWED

Written at

EPFL

EPFL units
LTS2  
Available on Infoscience
April 27, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/136618
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