[31] Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images, 2009.
[32] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[33] Shuxiao Chen, Edgar Dobriban, and Jane Lee. A group-theoretic framework for data augmentation. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 21321-21333. Curran Associates, Inc., 2020.
[34] Maurice Weiler and Gabriele Cesa. General e(2)-equivariant steerable cnns. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[35] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations, 2017.
[36] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013.
[37] Thomas D Downs. Orientation statistics. Biometrika, 59(3):665-676, 1972.
[38] Mohamed A Khamsi and William A Kirk. An introduction to metric spaces and fixed point theory. John Wiley & Sons, 2011.
[39] Taeyoung Lee. Bayesian attitude estimation with the matrix fisher distribution on so(3). IEEE Transactions on Automatic Control, 63(10):3377-3392, 2018.
[40] David Mohlin, Josephine Sullivan, and Gérald Bianchi. Probabilistic orientation estimation with matrix fisher distributions. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 4884-4893. Curran Associates, Inc., 2020.
[41] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG), 2019.
[42] Adam Coates, Andrew Ng, and Honglak Lee. An analysis of single-layer networks in unsupervised feature learning. In Geoffrey Gordon, David Dunson, and Miroslav Dudík, editors, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, volume 15 of Proceedings of Machine Learning Research, pages 215–223. PMLR, 2011.
[43] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[44] Nate Gruver, Marc Finzi, Micah Goldblum, and Andrew Gordon Wilson. The lie derivative for measuring learned equivariance. arXiv preprint arXiv:2210.02984, 2022.
[45] Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017.
[46] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. pages 1912-1920, 2015.
[47] Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. ShapeNet: An Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR], 2015.
[48] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38(5):1-12, 2019.