000227513 001__ 227513
000227513 005__ 20180317092109.0
000227513 037__ $$aCONF
000227513 245__ $$aStructured Sequence Modeling with Graph Convolutional Recurrent Networks
000227513 269__ $$a2017
000227513 260__ $$c2017
000227513 336__ $$aConference Papers
000227513 520__ $$aThis 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.
000227513 700__ $$0249843$$aSeo, Youngjoo$$g247595
000227513 700__ $$0249515$$aDefferrard, Michaël$$g226056
000227513 700__ $$0240428$$aVandergheynst, Pierre$$g120906
000227513 700__ $$0241065$$aBresson, Xavier$$g140163
000227513 8564_ $$uhttps://arxiv.org/abs/1612.07659$$zURL
000227513 909CO $$ooai:infoscience.tind.io:227513$$pSTI$$pconf
000227513 909C0 $$0252392$$pLTS2$$xU10380
000227513 917Z8 $$x226056
000227513 937__ $$aEPFL-CONF-227513
000227513 973__ $$aEPFL$$rNON-REVIEWED$$sSUBMITTED
000227513 980__ $$aCONF