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Deep neural networks have achieved great success in the last decade.
J. K. Lenstra and A. R. Kan, “Some simple applications of the travelling salesman problem,” Journal of the Operational Research Society , 1975
1975
Earlier work this paper cites.
C. H. Papadimitriou, “The euclidean travelling salesman problem is np-complete,” Theoretical computer science , 1977
1977
Earlier work this paper cites.
K. A. Smith, “Neural networks for combinatorial optimization: a review of more than a decade of research,” Informs journal on Computing , vol. 11, no. 1, pp. 15–34, 1999
1999
Earlier work this paper cites.
S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” science , 2000
2000
Earlier work this paper cites.
J. B. Tenenbaum, V. De Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” science , 2000
2000
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. , 2005
2005
Earlier work this paper cites.
A. M. Bronstein, M. M. Bronstein, and R. Kimmel, “Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching,” Proceedings of the National Academy of Sciences , 2006
2006
Earlier work this paper cites.
S. Agarwal, J. Wills, L. Cayton, G. Lanckriet, D. Kriegman, and S. Belongie, “Generalized non-metric multidimensional scaling,” in Artificial Intelligence and Statistics , 2007
2007
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks , 2008
2008
Earlier work this paper cites.
M. A. Cox and T. F. Cox, “Multidimensional scaling,” in Handbook of data visualization , 2008
2008
Earlier work this paper cites.
A. Micheli, “Neural network for graphs: A contextual constructive approach,” IEEE Transactions on Neural Networks , 2009
2009
Earlier work this paper cites.
N. Shervashidze, P. Schweitzer, E. J. v. Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels,” Journal of Machine Learning Research , 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems , 2012
2012
Earlier work this paper cites.
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,” IEEE signal processing magazine , 2013
2013
Earlier work this paper cites.
J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, “Spectral networks and locally connected networks on graphs,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , 2015
2015
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver et al. , “Human-level control through deep reinforcement learning,” nature , 2015
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
2015
Earlier work this paper cites.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
T. Cohen and M. Welling, “Group equivariant convolutional networks,” in International conference on machine learning , 2016
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” Advances in neural information processing systems , 2016
2016
Earlier work this paper cites.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in International conference on machine learning , 2016
2016
Earlier work this paper cites.
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” in International Conference on Learning Representations , 2016
2016
Cited alongside, same era.
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: going beyond euclidean data,” IEEE Signal Processing Magazine , 2017
2017
Cited alongside, same era.
S. Ravanbakhsh, J. Schneider, and B. Poczos, “Equivariance through parameter-sharing,” in International Conference on Machine Learning , 2017
2017
Cited alongside, same era.
M. Zitnik and J. Leskovec, “Predicting multicellular function through multi-layer tissue networks,” Bioinformatics , 2017
2017
Cited alongside, same era.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proceedings of the 34th International Conference on Machine Learning , 2017
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” Acm Transactions On Graphics (tog) , 2019
2019
Later among the works it cites.
H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman, “Invariant and equivariant graph networks,” in International Conference on Learning Representations , 2019
2019
Later among the works it cites.
N. Keriven and G. Peyré, “Universal invariant and equivariant graph neural networks,” in Advances in Neural Information Processing Systems , 2019
2019
Later among the works it cites.
J. Klicpera, J. Groß, and S. Günnemann, “Directional message passing for molecular graphs,” in International Conference on Learning Representations , 2019
2019
Later among the works it cites.
C. Chen, G. Li, R. Xu, T. Chen, M. Wang, and L. Lin, “Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis,” in Proceedings of Conference on Computer Vision and Pattern Recognition , 2019
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2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017
2017
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in neural information processing systems , 2017
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,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017
2017
Cited alongside, same era.
K. Helsgaun, “An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems,” Roskilde: Roskilde University , vol. 12, 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
B. Xu, H. Shen, Q. Cao, Y. Qiu, and X. Cheng, “Graph wavelet neural network,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in International Conference on Learning Representations , 2019
2019
Later among the works it cites.
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe, “Weisfeiler and leman go neural: Higher-order graph neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2019
2019
Later among the works it cites.
H. Maron, H. Ben-Hamu, H. Serviansky, and Y. Lipman, “Provably powerful graph networks,” in Advances in Neural Information Processing Systems , 2019
2019
Later among the works it cites.
R. Murphy, B. Srinivasan, V. Rao, and B. Ribeiro, “Relational pooling for graph representations,” in International Conference on Machine Learning , 2019
2019
Later among the works it cites.
Z. Zhang, B.-S. Hua, D. W. Rosen, and S.-K. Yeung, “Rotation invariant convolutions for 3d point clouds deep learning,” in International Conference on 3D Vision (3DV) , 2019
2019
Later among the works it cites.
Y. Bengio, A. Lodi, and A. Prouvost, “Machine learning for combinatorial optimization: a methodological tour d’horizon,” European Journal of Operational Research , 2020
2020
Later among the works it cites.
F. Fuchs, D. Worrall, V. Fischer, and M. Welling, “Se (3)-transformers: 3d roto-translation equivariant attention networks,” Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
S. KIM, J. Park, and B. Han, “Rotation-invariant local-to-global representation learning for 3d point cloud,” Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
H. Tang, Z. Huang, J. Gu, B.-L. Lu, and H. Su, “Towards scale-invariant graph-related problem solving by iterative homogeneous graph neural networks,” in Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
P. Hermosilla, M. Schäfer, M. Lang, G. Fackelmann, P.-P. Vázquez, B. Kozlikova, M. Krone, T. Ritschel, and T. Ropinski, “Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
R. Yu, X. Wei, F. Tombari, and J. Sun, “Deep positional and relational feature learning for rotation-invariant point cloud analysis,” in European Conference on Computer Vision , 2020
2020
Later among the works it cites.
M. Horie, N. Morita, Y. Ihara, and N. Mitsume, “Isometric transformation invariant and equivariant graph convolutional networks,” in International Conference on Learning Representations , 2021
2021
Closest in time.
V. G. Satorras, E. Hoogeboom, and M. Welling, “E (n) equivariant graph neural networks,” in Proceedings of the 38th International Conference on Machine Learning, ICML , 2021
2021
Closest in time.
D. A. Spielman, “Testing isomorphism of graphs with distinct eigenvalues,” http://www.cs.yale.edu/homes/spielman/561/lect08-18.pdf, 2018, [Online; accessed 6-July-2021]
2021
Closest in time.
P. Toth and D. Vigo, Vehicle routing: problems, methods, and applications . SIAM, 2014
2021
Closest in time.
2022
Closest in time.
L. Koestler, D. Grittner, M. Moeller, D. Cremers, and Z. Lähner, “Intrinsic neural fields: Learning functions on manifolds,” in European Conference on Computer Vision , 2022, pp. 622–639
2022
Closest in time.