[Spe21] Matthew Spellings, Geometric algebra attention networks for small point clouds, arXiv preprint arXiv:2110.02393, 2021.
[SSK+18] K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, Schnet – A deep learning architecture for molecules and materials, The Journal of Chemical Physics 148 (2018), no. 24, 241722.
[Kristof Schütt, Oliver T. Unke, and Michael Gastegger, Equivariant message passing for the prediction of tensorial properties and molecular spectra., International Conference on Machine Learning (ICML), 2021, pp. 9377-9388.
[Sy152] James Joseph Sylvester, XIX. A demonstration of the theorem that every homogeneous quadratic polynomial is reducible by real orthogonal substitutions to the form of a sum of positive and negative squares, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 4 (1852), no. 23, 138-142.
[TF22] Philipp Thölke and G. D. Fabritiis, Torch MD-NET: Equivariant Transformers for Neural Network based Molecular Potentials, ArXiv, vol. abs/2202.02541, 2022.
[TSK+18] Nathaniel Thomas, T. Smidt, S. Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick F. Riley, Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3d Point Clouds, arXiv, 2018.
[TVS+21] Raphael J. L. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon M. Anderson, Stephan Eismann, Risi Kondor, Russ B. Altman, and Ron O. Dror, Atom3d: Tasks on Molecules in Three Dimensions., Conference on Neural Information Processing Systems (NeurIPS), 2021.
[UPH+19] Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Duc Thanh Nguyen, and Sai-Kit Yeung, Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data., IEEE International Conference on Computer Vision (ICCV), 2019, pp. 1588-1597.
[VDWCV11] Stefan Van Der Walt, S Chris Colbert, and Gael Varoquaux, The numpy array: a structure for efficient numerical computation, Computing in science & engineering 13 (2011), no. 2, 22-30.
[VGO+20] Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al., Scipy 1.0: fundamental algorithms for scientific computing in python, Nature methods 17 (2020), no. 3, 261-272.
[Was21] Michael L Waskom, Seaborn: statistical data visualization, Journal of Open Source Software 6 (2021), no. 60, 3021.
[WC19] Maurice Weiler and Gabriele Cesa, General E(2)-Equivariant Steerable CNNs., Conference on Neural Information Processing Systems (NeurIPS), 2019, pp. 14334-14345.
[WCL05] Rich Wareham, Jonathan Cameron, and Joan Lasenby, Applications of conformal geometric algebra in computer vision and graphics, Computer Algebra and Geometric Algebra with Applications: 6th International Workshop, IWMM 2004, Shanghai, China, May 19-21, 2004 and International Workshop, GIAE 2004, Xian, China, May 24-28, 2004, Revised Selected Papers, Springer, 2005, pp. 329-349.
[WFVW21] 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.
[WGTB17] Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, and Gabriel J. Bros-tow, Harmonic Networks: Deep Translation and Rotation Equivariance., Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7168-7177.
[WGW+18] Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen, 3d Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data., Conference on Neural Information Processing Systems (NeurIPS), 2018, pp. 10402-10413.