P.-A. Absil, R. Mahony, and R. Sepulchre. Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton, NJ, 2008. ISBN 978-0-691-13298-3. URL https://press.princeton.edu/absil.
Taleb Alashkar, Boulbaba Ben Amor, Mohamed Daoudi, and Stefano Berretti. A grassmann framework for 4d facial shape analysis. Pattern Recognition, 57:21-30, 2016. ISSN 0031-3203. URL https://doi.org/10.1016/j.patcog.2016.03.013.
E. Begelfor and M. Werman. Affine invariance revisited. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pp. 2087-2094. IEEE Computer Society, 2006. URL https://doi.org/10.1109/CVPR.2006.50.
Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen. Generative models for molecular discovery: Recent advances and challenges. WIREs Computational Molecular Science, pp. e1608, 2022. URL https://doi.org/10.1002/wcms.1608.
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, and Phiala E. Shanahan. Sampling using  $su(n)$  gauge equivariant flows. Phys. Rev. D, 103(7):074504, 2021a. URL https://doi.org/10.1103/PhysRevD.103.074504.
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, and Phiala E. Shanahan. Sampling using  $\mathrm{su}(\mathfrak{n})$  gauge equivariant flows. Physical Review D, 103(7), 2021b. URL https://link.aps.org/doi/10.1103/PhysRevD.103.074504.
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. CoRR, abs/2104.13478, 2021. URL https://arxiv.org/abs/2104.13478.
Elena Celledoni and Brynjulf Owren. A class of intrinsic schemes for orthogonal integration. SIAM Journal on Numerical Analysis, 40(6):2069-2084, 2002. URL http://www.jstor.org/stable/4100984.
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary differential equations. In Advances in Neural Information Processing Systems, volume 31, pp. 6571-6583. Curran Associates, Inc., 2018. URL https://proceedings.neurips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf.
Yasuko Chikuse. Statistics on Special Manifolds. Lecture Notes in Statistics, volume 174. Springer, 01 2003. URL https://doi.org/10.1007/978-0-387-21540-2.
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling. Gauge equivariant convolutional networks and the icosahedral CNN. In Proceedings of the 36th International Conference on Machine Learning, volume 97, pp. 1321-1330. PMLR, 2019a. URL https://proceedings.mlr.press/v97/cohen19d.html.
Taco S. Cohen and Max Welling. Group equivariant convolutional networks. In Proceedings of The 33rd International Conference on Machine Learning, volume 48, pp. 2990-2999. PMLR, 2016. URL https://proceedings.mlr.press/v48/cohenc16.html.
Taco S. Cohen and Max Welling. Steerable cnns. In International Conference on Learning Representations, 2017. URL https://openreview.net/forum?id=rJQKYt511.
Taco S. Cohen, Mario Geiger, Jonas Kohler, and Max Welling. Spherical cnns. In International Conference on Learning Representations, 2018. URL https://openreview.net/forum?id=Hkbd5xZRb.
Taco S. Cohen, Mario Geiger, and Maurice Weiler. A general theory of equivariant cnns on homogeneous spaces. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019b. URL https://proceedings.neurips.cc/paper/2019/file/b9cfe8b6042cf759dc4c0cccb27a6737-Paper.pdf.