[38] Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley. Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds. arXiv:1802.08219, 2018.
[39] Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. Se (3)-transformers: 3d roto-translation equivariant attention networks. Neural Information Processing Systems, 33, 2020.
[40] Mario Geiger and Tess Smidt. e3nn: Euclidean neural networks. arXiv preprint arXiv:2207.09453, 2022.
[41] Soledad Villar, David W Hogg, Kate Storey-Fisher, Weichi Yao, and Ben Blum-Smith. Scalars are universal: Equivariant machine learning, structured like classical physics. Advances in Neural Information Processing Systems, 34:28848-28863, 2021.
[42] Ben Finkelshtein, Chaim Baskin, Haggai Maron, and Nadav Dym. A simple and universal rotation equivariant point-cloud network. In Topological, Algebraic and Geometric Learning Workshops 2022, pages 107-115. PMLR, 2022.
[43] Risi Kondor, Zhen Lin, and Shubhendu Trivedi. Clebsch-gordan nets: a fully fourier space spherical convolutional neural network. Advances in Neural Information Processing Systems, 31:10117-10126, 2018.
[44] Maurice Weiler, Patrick Forre, Erik Verlinde, and Max Welling. Coordinate independent convolutional networks-isometry and gauge equivariant convolutions on riemannian manifolds. arXiv preprint arXiv:2106.06020, 2021.
[45] Rui Wang, Robin Walters, and Rose Yu. Approximately equivariant networks for imperfectly symmetric dynamics. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 23078-23091. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/wang22aa.html.
[46] Soledad Villar, Weichi Yao, David W Hogg, Ben Blum-Smith, and Bianca Dumitrascu. Dimensionless machine learning: Imposing exact units equivariance. Journal of Machine Learning Research, 24(109):1-32, 2023.
[47] Taco S. Cohen, Mario Geiger, Jonas Koehler, and Max Welling. Spherical cnns, 2018.
[48] Jiaqi Han, Wenbing Huang, Tingyang Xu, and Yu Rong. Equivariant graph hierarchy-based neural networks. Advances in Neural Information Processing Systems, 35:9176-9187, 2022.
[49] Pim de Haan, Taco S Cohen, and Max Welling. Natural graph networks. Advances in Neural Information Processing Systems, 33:3636-3646, 2020.
[50] Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola. Generalization and representational limits of graph neural networks. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 3419-3430. PMLR, 13-18 Jul 2020. URL https://proceedings.mlr.press/v119/garg20c.html.
[51] Pascal Esser, Leena Chennuru Vankadara, and Debarghya Ghoshdastidar. Learning theory can (sometimes) explain generalisation in graph neural networks. Advances in Neural Information Processing Systems, 34:27043-27056, 2021.
[52] Christopher Morris, Floris Geerts, Jan Tonshoff, and Martin Grohe. Wl meet vc, 2023.
[53] Sohir Maskey, Ron Levie, Yunseok Lee, and Gitta Kutyniok. Generalization analysis of message passing neural networks on large random graphs. In Advances in Neural Information Processing Systems, 2022.
[54] Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, and Gitta Kutyniok. Transferability of spectral graph convolutional neural networks. Journal of Machine Learning Research, 22 (272):1-59, 2021.
[55] Luana Ruiz, Luiz Chamon, and Alejandro Ribeiro. Graphon neural networks and the transferability of graph neural networks. Advances in Neural Information Processing Systems, 33: 1702-1712, 2020.