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Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations.
Berge, C., Minieka., E.: Graphs and Hypergraphs. North-Holland Publishing Company (1981)
1981
Earlier work this paper cites.
Hornik, K., Stinchcombe, M.B., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks (1989)
1989
Earlier work this paper cites.
Kalai, G.: Linear programming, the simplex algorithm and simple polytopes. Math. Program. (1997)
1997
Earlier work this paper cites.
Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)
1999
Earlier work this paper cites.
Belongie, S.J., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. (2002)
2002
Earlier work this paper cites.
Kofidis, E., Regalia, P.A.: On the best rank-1 approximation of higher-order supersymmetric tensors. Siam J. Matrix Anal. Appl (2002)
2002
Earlier work this paper cites.
Ray, L.A.: 2-d and 3-d image registration for medical, remote sensing, and industrial applications. J. Electronic Imaging (2005)
2005
Earlier work this paper cites.
Botsch, M., Pauly, M., Kobbelt, L., Alliez, P., Lévy, B.: Geometric modeling based on polygonal meshes. In: Eurographics Tutorials (2008)
2008
Earlier work this paper cites.
Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3d human pose annotations. In: ICCV (2009)
2009
Earlier work this paper cites.
Berend, D., Tassa, T.: Improved bounds on bell numbers and on moments of sums of random variables. Probability and Mathematical Statistics. (2010)
2010
Earlier work this paper cites.
Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., Zhang, L., He, X.: Music recommendation by unified hypergraph: Combining social media information and music content. In: Proceedings of the 18th International Conference on Multimedia 2010, Firenze, Italy, October 25-29, 2010 (2010)
2010
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision (2010)
2010
Earlier work this paper cites.
Tan, S., Bu, J., Chen, C., He, X.: Using rich social media information for music recommendation via hypergraph model. In: Social Media Modeling and Computing. Springer (2011)
2011
Earlier work this paper cites.
Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-d object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. (2012)
2012
Earlier work this paper cites.
Cho, M., Alahari, K., Ponce, J.: Learning graphs to match. In: ICCV (2013)
2013
Earlier work this paper cites.
Li, D., Xu, Z., Li, S., Sun, X.: Link prediction in social networks based on hypergraph. In: 22nd International World Wide Web Conference, WWW ’13, Rio de Janeiro, Brazil, May 13-17, 2013, Companion Volume (2013)
2013
Earlier work this paper cites.
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: ICML (2017)
2017
Earlier work this paper cites.
Gu, S., Yang, M., Medaglia, J.D., Gur, R.C., Gur, R.E., Satterthwaite, T.D., Bassett, D.S.: Functional hypergraph uncovers novel covariant structures over neurodevelopment. Human Brain Mapp. (2017)
2017
Earlier work this paper cites.
Ha, D., Dai, A.M., Le, Q.V.: Hypernetworks. In: ICLR (2017)
2017
Earlier work this paper cites.
Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Li, J., Cai, D., He, X.: Learning graph-level representation for drug discovery. arXiv (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: NeurIPS (2017)
2017
Cited alongside, same era.
Zaheer, M., Kottur, S., Ravanbakhsh, S., Póczos, B., Salakhutdinov, R., Smola, A.J.: Deep sets. In: NeurIPS (2017)
2017
Cited alongside, same era.
Li, Q., Han, Z., Wu, X.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI (2018)
2018
Cited alongside, same era.
Albooyeh, M., Bertolini, D., Ravanbakhsh, S.: Incidence networks for geometric deep learning. arXiv (2019)
2019
Cited alongside, same era.
Gu, F., Chang, H., Zhu, W., Sojoudi, S., Ghaoui, L.E.: Implicit graph neural networks. In: NeurIPS (2020)
2020
Later among the works it cites.
Kim, E., Kang, W., On, K., Heo, Y., Zhang, B.: Hypergraph attention networks for multimodal learning. In: CVPR (2020)
2020
Later among the works it cites.
Louis, S.M., Zhao, Y., Nasiri, A., Wong, X., Song, Y., Liu, F., Hu, J.: Global attention based graph convolutional neural networks for improved materials property prediction. arXiv (2020)
2020
Later among the works it cites.
Maron, H., Litany, O., Chechik, G., Fetaya, E.: On learning sets of symmetric elements. In: ICML (2020)
2020
Later among the works it cites.
Milano, F., Loquercio, A., Rosinol, A., Scaramuzza, D., Carlone, L.: Primal-dual mesh convolutional neural networks. In: NeurIPS (2020)
2020
Later among the works it cites.
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Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI (2019)
2019
Cited alongside, same era.
Ishiguro, K., ichi Maeda, S., Koyama, M.: Graph warp module: an auxiliary module for boosting the power of graph neural networks in molecular graph analysis. arXiv (2019)
2019
Cited alongside, same era.
Keriven, N., Peyré, G.: Universal invariant and equivariant graph neural networks. In: NeurIPS (2019)
2019
Cited alongside, same era.
Knyazev, B., Taylor, G.W., Amer, M.R.: Understanding attention and generalization in graph neural networks. In: NeurIPS (2019)
2019
Cited alongside, same era.
Lee, J., Lee, Y., Kim, J., Kosiorek, A.R., Choi, S., Teh, Y.W.: Set transformer: A framework for attention-based permutation-invariant neural networks. In: ICML (2019)
2019
Cited alongside, same era.
Maron, H., Ben-Hamu, H., Serviansky, H., Lipman, Y.: Provably powerful graph networks. In: NeurIPS (2019)
2019
Cited alongside, same era.
Maron, H., Ben-Hamu, H., Shamir, N., Lipman, Y.: Invariant and equivariant graph networks. In: ICLR (2019)
2019
Cited alongside, same era.
Oono, K., Suzuki, T.: Graph neural networks exponentially lose expressive power for node classification. In: ICLR (2020)
2020
Later among the works it cites.
Puny, O., Ben-Hamu, H., Lipman, Y.: From graph low-rank global attention to 2-fwl approximation. In: ICML (2020)
2020
Later among the works it cites.
Rolinek, M., Swoboda, P., Zietlow, D., Paulus, A., Musil, V., Martius, G.: Deep graph matching via blackbox differentiation of combinatorial solvers. In: ECCV (2020)
2020
Later among the works it cites.
Serviansky, H., Segol, N., Shlomi, J., Cranmer, K., Gross, E., Maron, H., Lipman, Y.: Set2graph: Learning graphs from sets. In: NeurIPS (2020)
2020
Later among the works it cites.
Bai, S., Zhang, F., Torr, P.H.S.: Hypergraph convolution and hypergraph attention. Pattern Recognit. (2021)
2021
Later among the works it cites.
Huang, J., Yang, J.: Unignn: a unified framework for graph and hypergraph neural networks. In: IJCAI (2021)
2021
Later among the works it cites.
Kim, J., Oh, S., Hong, S.: Transformers generalize deepsets and can be extended to graphs and hypergraphs. In: NeurIPS (2021)
2021
Later among the works it cites.
Klimm, F., Deane, C.M., Reinert, G., Estrada, E.: Hypergraphs for predicting essential genes using multiprotein complex data. Journal of Complex Networks (2021)
2021
Later among the works it cites.
Wang, R., Yan, J., Yang, X.: Neural graph matching network: Learning lawler’s quadratic assignment problem with extension to hypergraph and multiple-graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
2021
Later among the works it cites.
Wu, Z., Jain, P., Wright, M.A., Mirhoseini, A., Gonzalez, J.E., Stoica, I.: Representing long-range context for graph neural networks with global attention. arXiv (2021)
2021
Later among the works it cites.
Yavartanoo, M., Hung, S., Neshatavar, R., Zhang, Y., Lee, K.M.: Polynet: Polynomial neural network for 3d shape recognition with polyshape representation. In: 3DV (2021)
2021
Later among the works it cites.
Bevilacqua, B., Frasca, F., Lim, D., Srinivasan, B., Cai, C., Balamurugan, G., Bronstein, M.M., Maron, H.: Equivariant subgraph aggregation networks. In: ICLR (2022)
2022
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Chien, E., Pan, C., Peng, J., Milenkovic, O.: You are allset: A multiset function framework for hypergraph neural networks. In: ICLR (2022)
2022
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Kim, J., Nguyen, T.D., Min, S., Cho, S., Lee, M., Lee, H., Hong, S.: Pure transformers are powerful graph learners. arXiv (2022)
2022
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Velikovic, P.: Message passing all the way up. arXiv (2022)
2022
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