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Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields.
A real-time algorithm for signal analysis with the help of the wavelet transform
M. Holschneider, R. Kronland-Martinet, J. Morlet, and P. Tchamitchian · 1990
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The discrete wavelet transform: wedding the a trous and mallat algorithms
M. J. Shensa · 1992
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Comparison of descriptor spaces for chemical compound retrieval and classification
N. Wale, I. A. Watson, and G. Karypis · 2008
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Relational learning via latent social dimensions
L. Tang and H. Liu · 2009
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2015
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Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 2015
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Structural-rnn: Deep learning on spatio-temporal graphs
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Lstm-cf: Unifying context modeling and fusion with lstms for rgb-d scene labeling
Z. Li, Y. Gan, X. Liang, Y. Yu, H. Cheng, and L. Lin · 2016
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Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas · 2016
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Joint 2D-3D-Semantic Data for Indoor Scene Understanding
I. Armeni, S. Sax, A. R. Zamir, and S. Savarese · 2017
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Graph convolutional encoders for syntax-aware neural machine translation
J. Bastings, I. Titov, W. Aziz, D. Marcheggiani, and K. Simaan · 2017
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Unstructured point cloud semantic labeling using deep segmentation networks
A. Boulch, B. Le Saux, and N. Audebert · 2017
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Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. A. Funkhouser, and M. Nießner · 2017
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Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
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Exploring spatial context for 3d semantic segmentation of point clouds
F. Engelmann, T. Kontogianni, A. Hermans, and B. Leibe · 2017
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Snapnet-r: Consistent 3d multi-view semantic labeling for robotics
J. Guerry, A. Boulch, B. Le Saux, J. Moras, A. Plyer, and D. Filliat · 2017
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Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Predicting multicellular function through multi-layer tissue networks
M. Zitnik and J. Leskovec · 2017
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Recurrent slice networks for 3d segmentation of point clouds
Q. Huang, W. Wang, and U. Neumann · 2018
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Image generation from scene graphs
J. Johnson, A. Gupta, and L. Fei-Fei · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X.-M. Wu · 2018
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Factorizable net: an efficient subgraph-based framework for scene graph generation
Y. Li, W. Ouyang, B. Zhou, J. Shi, C. Zhang, and X. Wang · 2018
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Encoding sentences with graph convolutional networks for semantic role labeling
D. Marcheggiani and I. Titov · 2017
Cited alongside, same era.
Geometric matrix completion with recurrent multi-graph neural networks
F. Monti, M. Bronstein, and X. Bresson · 2017
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Cross-sentence n-ary relation extraction with graph lstms
N. Peng, H. Poon, C. Quirk, K. Toutanova, and W.-t. Yih · 2017
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Column networks for collective classification
T. Pham, T. Tran, D. Phung, and S. Venkatesh · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
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Semi-supervised user geolocation via graph convolutional networks
A. Rahimi, T. Cohn, and T. Baldwin · 2018
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Learning localized generative models for 3d point clouds via graph convolution
D. Valsesia, G. Fracastoro, and E. Magli · 2018
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Understanding convolution for semantic segmentation
P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell · 2018
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Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2018
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
S. Yan, Y. Xiong, and D. Lin · 2018
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Graph r-cnn for scene graph generation
J. Yang, J. Lu, S. Lee, D. Batra, and D. Parikh · 2018
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3d recurrent neural networks with context fusion for point cloud semantic segmentation
X. Ye, J. Li, H. Huang, L. Du, and X. Zhang · 2018
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Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
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Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, and M. Sun · 2018
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2019
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