[67] Chengcheng Guo, Bo Zhao, and Yanbing Bai. Deepcore: A comprehensive library for coreset selection in deep learning. In Database and Expert Systems Applications: 33rd International Conference, DEXA 2022, Vienna, Austria, August 22-24, 2022, Proceedings, Part I, pages 181-195. Springer, 2022. 21
[68] Talia B Kimber, Yonghui Chen, and Andrea Volkamer. Deep learning in virtual screening: recent applications and developments. International Journal of Molecular Sciences, 22(9):4435, 2021. 22
[69] Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller. Machine learning of accurate energy-conserving molecular force fields. Science advances, 3(5):e1603015, 2017. 23
[70] Johannes Gasteiger, Janek Groß, and Stephan Gunnemann. Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123, 2020. 23
[71] Johannes Gasteiger, Florian Becker, and Stephan Gunnemann. Gemnet: Universal directional graph neural networks for molecules. Advances in Neural Information Processing Systems, 34:6790-6802, 2021. 23
[72] Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. E (n) equivariant graph neural networks. In International conference on machine learning, pages 9323-9332. PMLR, 2021. 23
[73] Bowen Jing, Stephan Eismann, Pratham N Soni, and Ron O Dror. Equivariant graph neural networks for 3d macromolecular structure. arXiv preprint arXiv:2106.03843, 2021. 23
[74] Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, and Tie-Yan Liu. Se (3) equivariant graph neural networks with complete local frames. In International Conference on Machine Learning, pages 5583-5608. PMLR, 2022. 23
[75] 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. 23
[76] Mario Geiger and Tess Smidt. e3nn: Euclidean neural networks. arXiv preprint arXiv:2207.09453, 2022. 23
[77] Adrien Poulenard and Leonidas J Guibas. A functional approach to rotation equivariant non-linearities for tensor field networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13174-13183, 2021.
[78] Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. Se (3)-transformers: 3d roto-translation equivariant attention networks. Advances in neural information processing systems, 33:1970–1981, 2020.
[79] Yi-Lun Liao and Tess Smidt. Equformer: Equivariant graph attention transformer for 3d atomistic graphs. arXiv preprint arXiv:2206.11990, 2022.
[80] Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi. Mace: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems, 35:11423-11436, 2022. 23
[81] David Haussler. Quantifying inductive bias: AI learning algorithms and valiant's learning framework. Artificial intelligence, 36(2):177-221, 1988. 24
[82] Andrzej Ehrenfeucht, David Haussler, Michael Kearns, and Leslie Valiant. A general lower bound on the number of examples needed for learning. Information and Computation, 82(3):247-261, 1989.
[83] Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K Warmuth. Learnability and the vapnik-chervonenkis dimension. Journal of the ACM (JACM), 36(4):929-965, 1989.
[84] David Haussler, H Sebastian Seung, Michael Kearns, and Naftali Tishby. Rigorous learning curve bounds from statistical mechanics. In Proceedings of the seventh annual conference on Computational learning theory, pages 76-87, 1994. 24
[85] Michele Banko and Eric Brill. Scaling to very very large corpora for natural language disambiguation. In Proceedings of the 39th annual meeting of the Association for Computational Linguistics, pages 26-33, 2001. 24
[86] Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev. Reproducible scaling laws for contrastive language-image learning. arXiv preprint arXiv:2212.07143, 2022. 24