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A number of problems can be formulated as prediction on graph-structured data.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
A. K. Debnath, R. L. Lopez de Compadre, G. Debnath, A. J. Shusterman, and C. Hansch · 1991
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Distinguishing enzyme structures from non-enzymes without alignments
P. D. Dobson and A. J. Doig · 2003
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Shortest-path kernels on graphs
K. M. Borgwardt and H. Kriegel · 2005
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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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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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3d is here: Point cloud library (pcl)
R. B. Rusu and S. Cousins · 2011
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Weisfeiler-lehman graph kernels
N. Shervashidze, P. Schweitzer, E. J. van Leeuwen, K. Mehlhorn, and K. M. Borgwardt · 2011
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Graph sparsification by effective resistances
D. A. Spielman and N. Srivastava · 2011
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
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Unsupervised feature learning for classification of outdoor 3d scans
M. De Deuge, A. Quadros, C. Hung, and B. Douillard · 2013
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Kron reduction of graphs with applications to electrical networks
F. Dörfler and F. Bullo · 2013
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Performance of global descriptors for velodyne-based urban object recognition
T. Chen, B. Dai, D. Liu, and J. Song · 2014
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B. Graham · 2014
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GSPBOX: A toolbox for signal processing on graphs
N. Perraudin, J. Paratte, D. I. Shuman, V. Kalofolias, P. Vandergheynst, and D. K. Hammond · 2014
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Advanced coarsening schemes for graph partitioning
I. Safro, P. Sanders, and C. Schulz · 2014
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
A. M. Saxe, J. L. McClelland, and S. Ganguli · 2014
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Convolutional neural networks at constrained time cost
K. He and J. Sun · 2015
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L. J. Ba, R. Kiros, and G. E. Hinton · 2016
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Dynamic filter networks
B. D. Brabandere, X. Jia, T. Tuytelaars, and L. V. Gool · 2016
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Discriminative embeddings of latent variable models for structured data
H. Dai, B. Dai, and L. Song · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Graph based convolutional neural network
M. Edwards and X. Xie · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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S. Ioffe and C. Szegedy · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Geodesic convolutional neural networks on riemannian manifolds
J. Masci, D. Boscaini, M. M. Bronstein, and P. Vandergheynst · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller · 2015
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3d shapenets for 2.5d object recognition and next-best-view prediction
Z. Wu, S. Song, A. Khosla, X. Tang, and J. Xiao · 2015
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P. Yanardag and S. V. N. Vishwanathan · 2015
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Point cloud labeling using 3d convolutional neural network
J. Huang and S. You · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. S. Zemel · 2016
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All you need is a good init
D. Mishkin and J. Matas · 2016
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Learning convolutional neural networks for graphs
M. Niepert, M. Ahmed, and K. Kutzkov · 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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A multiscale pyramid transform for graph signals
D. I. Shuman, M. J. Faraji, and P. Vandergheynst · 2016
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