[62] Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, and Zhi-Ming Ma. A new perspective on building efficient and expressive 3d equivariant graph neural networks. arXiv preprint arXiv:2304.04757, 2023.
[63] Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Yunpeng Liu, Justice Sefas, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, and Frank Wood. Conditional permutation invariant flows. arXiv preprint arXiv:2206.09021, 2022.
[64] Marin Biloš and Stephan Gunnemann. Equivariant normalizing flows for point processes and sets, 2021.
[65] Yang Li, Haidong Yi, Christopher Bender, Siyuan Shan, and Junier B Oliva. Exchangeable neural ode for set modeling. Advances in Neural Information Processing Systems, 33:6936-6946, 2020.
[66] Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser Nam Lim, and Christopher M De Sa. Equivariant manifold flows. Advances in Neural Information Processing Systems, 34:10600-10612, 2021.
[67] Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M Solomon, and Mikhail Yurochkin. Alleviating label switching with optimal transport. Advances in Neural Information Processing Systems, 32, 2019.
[68] Yann Brenier. Polar factorization and monotone rearrangement of vector-valued functions. Communications on pure and applied mathematics, 44(4):375-417, 1991.
[69] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019.
[70] Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, and Stefano Ermon. Torchdyn: Implicit models and neural numerical methods in pytorch. In Neural Information Processing Systems, Workshop on Physical Reasoning and Inductive Biases for the Real World, volume 2, 2021.
[71] Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer. Pot: Python optimal transport. Journal of Machine Learning Research, 22(78):1-8, 2021.
[72] Peter Eastman, Jason Swails, John D Chodera, Robert T McGibbon, Yutong Zhao, Kyle A Beauchamp, Lee-Ping Wang, Andrew C Simonett, Matthew P Harrigan, Chaya D Stern, et al. Openmm 7: Rapid development of high performance algorithms for molecular dynamics. PLoS computational biology, 13(7):e1005659, 2017.
[73] Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, Ivano E Castelli, Rune Christensen, Marcin Dułak, Jesper Friis, Michael N Groves, Björk Hammer, Cory Hargus, Eric D Hermes, Paul C Jennings, Peter Bjerre Jensen, James Kermode, John R Kitchin, Esben Leonhard Kolsbjerg, Joseph Kubal, Kristen Kaasbjerg, Steen Lysgaard, Jón Bergmann Maronsson, Tristan Maxson, Thomas Olsen, Lars Pastewka, Andrew Peterson, Carsten Rostgaard, Jakob Schötz, Ole Schütt, Mikkel Strange, Kristian S Thygesen, Tejs Vegge, Lasse Vilhelmsen, Michael Walter, Zhenhua Zeng, and Karsten W Jacobsen. The atomic simulation environment—a python library for working with atoms. Journal of Physics: Condensed Matter, 29(27):273002, 2017.
[74] H Wiegand. Kish, l.: Survey sampling. John wiley & sons, inc., new york, london 1965, ix+ 643 s., 31 abb., 56 tab., preis 83 s., 1968.