DeepGCNs: Making GCNs Go as Deep as CNNs

J. Bastings, I. Titov, W. Aziz, D. Marcheggiani, and K. Simaan, “Graph convolutional encoders for syntax-aware neural machine translation,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 1957–1967

1967

Earlier work this paper cites.

M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian, “A real-time algorithm for signal analysis with the help of the wavelet transform,” in Wavelets . Springer, 1990, pp. 286–297

1990

Earlier work this paper cites.

M. J. Shensa, “The discrete wavelet transform: wedding the a trous and mallat algorithms,” IEEE Transactions on signal processing , vol. 40, no. 10, pp. 2464–2482, 1992

1992

Earlier work this paper cites.

N. Wale, I. A. Watson, and G. Karypis, “Comparison of descriptor spaces for chemical compound retrieval and classification,” Knowledge and Information Systems , vol. 14, no. 3, pp. 347–375, 2008

2008

Earlier work this paper cites.

L. Tang and H. Liu, “Relational learning via latent social dimensions,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2009, pp. 817–826

2009

Earlier work this paper cites.

F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122 , 2015

Original

2015

Earlier work this paper cites.

H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 945–953

2015

Earlier work this paper cites.

D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in neural information processing systems , 2015, pp. 2224–2232

2015

Earlier work this paper cites.

Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” arXiv preprint arXiv:1511.05493 , 2015

Original

2015

Earlier work this paper cites.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 , 2015

Original

2015

Earlier work this paper cites.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241

2015

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Earlier work this paper cites.

A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural-rnn: Deep learning on spatio-temporal graphs,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 5308–5317

2016

Earlier work this paper cites.

Z. Li, Y. Gan, X. Liang, Y. Yu, H. Cheng, and L. Lin, “Lstm-cf: Unifying context modeling and fusion with lstms for rgb-d scene labeling,” in European Conference on Computer Vision . Springer, 2016, pp. 541–557

2016

Earlier work this paper cites.

C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas, “Volumetric and multi-view cnns for object classification on 3d data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 5648–5656

2016

Earlier work this paper cites.

T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907 , 2016

Original

2016

Earlier work this paper cites.

M. Zitnik and J. Leskovec, “Predicting multicellular function through multi-layer tissue networks,” Bioinformatics , vol. 33, no. 14, pp. i190–i198, 2017

2017

Earlier work this paper cites.

F. Monti, M. Bronstein, and X. Bresson, “Geometric matrix completion with recurrent multi-graph neural networks,” in Advances in Neural Information Processing Systems , 2017, pp. 3697–3707

2017

Earlier work this paper cites.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708

2017

Earlier work this paper cites.

I. Armeni, S. Sax, A. R. Zamir, and S. Savarese, “Joint 2D-3D-Semantic Data for Indoor Scene Understanding,” ArXiv e-prints , Feb. 2017

2017

Earlier work this paper cites.

D. Marcheggiani and I. Titov, “Encoding sentences with graph convolutional networks for semantic role labeling,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 1506–1515

2017

Earlier work this paper cites.

X. Qi, R. Liao, J. Jia, S. Fidler, and R. Urtasun, “3d graph neural networks for rgbd semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 5199–5208

2017

Earlier work this paper cites.

D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei, “Scene graph generation by iterative message passing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5410–5419

2017

Earlier work this paper cites.

J. Guerry, A. Boulch, B. Le Saux, J. Moras, A. Plyer, and D. Filliat, “Snapnet-r: Consistent 3d multi-view semantic labeling for robotics,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) , 2017, pp. 669–678

2017

Earlier work this paper cites.

A. Boulch, B. Le Saux, and N. Audebert, “Unstructured point cloud semantic labeling using deep segmentation networks.” in 3DOR , 2017

2017

Earlier work this paper cites.

A. Dai, A. X. Chang, M. Savva, M. Halber, T. A. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes.” in CVPR , vol. 2, 2017, p. 10

2017

Earlier work this paper cites.

G. Riegler, A. O. Ulusoy, and A. Geiger, “Octnet: Learning deep 3d representations at high resolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , vol. 3, 2017

2017

Earlier work this paper cites.

L. Tchapmi, C. Choy, I. Armeni, J. Gwak, and S. Savarese, “Segcloud: Semantic segmentation of 3d point clouds,” in 3D Vision (3DV), 2017 International Conference on . IEEE, 2017, pp. 537–547

2017

Cited alongside, same era.

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” Proc. Computer Vision and Pattern Recognition (CVPR), IEEE , vol. 1, no. 2, p. 4, 2017

2017

Cited alongside, same era.

C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in Neural Information Processing Systems , 2017, pp. 5099–5108

2017

Cited alongside, same era.

F. Engelmann, T. Kontogianni, A. Hermans, and B. Leibe, “Exploring spatial context for 3d semantic segmentation of point clouds,” in IEEE International Conference on Computer Vision, 3DRMS Workshop, ICCV , 2017

2017

Cited alongside, same era.

K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” arXiv preprint arXiv:1810.00826 , 2018

Original

2018

Later among the works it cites.

Y. Xu, T. Fan, M. Xu, L. Zeng, and Y. Qiao, “Spidercnn: Deep learning on point sets with parameterized convolutional filters,” arXiv preprint arXiv:1803.11527 , 2018

Original

2018

Later among the works it cites.

Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on x-transformed points,” in NeurIPS , 2018

2018

Later among the works it cites.

Y. Ben-Shabat, M. Lindenbaum, and A. Fischer, “3dmfv: Three-dimensional point cloud classification in real-time using convolutional neural networks,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3145–3152, 2018

2018

Later among the works it cites.