[35] Alexandre Milesi. Accelerating SE(3)-Transformers training using an NVIDIA open-source model implementation. https://developer.nvidia.com/blog/accelerating-se3-transformers-training-using-an-nvidia-open-source-model-implementation/, 2021. (Cited on pages 9 and 25)
[36] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems, 30, 2017. (Cited on pages 8 and 23)
[37] Markus N Rabe and Charles Staats. Self-attention does not need  $O(n^2)$  memory. arXiv:2112.05682, 2021. (Cited on pages 4 and 6)
[38] Martin Roelfs and Steven De Keninck. Graded symmetry groups: plane and simple. arXiv:2107.03771, 2021. (Cited on pages 2, 3, 10, and 15)
[39] David W Romero, Erik J Bekkers, Jakub M Tomczak, and Mark Hoogendoorn. Attentive group equivariant convolutional networks. In Proceedings of the 37th International Conference on Machine Learning, volume 119, pages 8188-8199. JMLR.org, July 2020. (Cited on page 10)
[40] David Ruhe, Johannes Brandstetter, and Patrick Forre. Clifford group equivariant neural networks. In Advances in Neural Information Processing Systems, volume 37, 2023. (Cited on pages 2 and 10)
[41] David Ruhe, Jayesh K Gupta, Steven de Keninck, Max Welling, and Johannes Brandstetter. Geometric clifford algebra networks. In International Conference on Machine Learning, 2023. (Cited on pages 2, 3, 7, 10, and 22)
[42] Víctor Garcia Satorras, Emiel Hoogeboom, and Max Welling. E(n) equivariant graph neural networks. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 9323–9332. PMLR, 2021. (Cited on page 10)
[43] Noam Shazeer. Fast transformer decoding: One write-head is all you need. arXiv:1911.02150, 2019. (Cited on page 21)
[44] Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann. Neural descriptor fields: SE(3)-Equivariant object representations for manipulation. In ICRA, 2022. (Cited on page 10)
[45] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015. (Cited on page 8)
[46] Matthew Spellings. Geometric algebra attention networks for small point clouds. arXiv:2110.02393, 2021. (Cited on pages 2 and 10)
[47] Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. arXiv:2104.09864, 2021. (Cited on page 7)
[48] Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, and Jelmer M Wolterink. Mesh neural networks for se (3)-equivariant hemodynamics estimation on the artery wall. arXiv:2212.05023, 2022. (Cited on pages 8, 22, and 23)
[49] 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. (Cited on page 10)
[50] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30, 2017. (Cited on pages 2, 4, 5, and 6)
[51] Dian Wang, Robin Walters, Xupeng Zhu, and Robert Platt. Equivariant Q learning in spatial action spaces, 2021. (Cited on page 10)