[1] R. Abboud, I. I. Ceylan, M. Grohe, and T. Lukasiewicz. The surprising power of graph neural networks with random node initialization. In *IJCAI*, 2021. (Cited on 6, 23)
[2] M. Balcilar, P. Héroux, B. Gaüzère, P. Vasseur, S. Adam, and P. Honeine. Breaking the limits of message passing graph neural networks. In ICML, 2021. (Cited on 6, 7, 23)
[3] S. Basu, P. Katdare, P. Sattigeri, V. Chenthamarakshan, K. Driggs-Campbell, P. Das, and L. R. Varshney. Equivariant few-shot learning from pretrained models. arXiv, 2023. (Cited on 22)
[4] S. Basu, P. Sattigeri, K. N. Ramamurthy, V. Chenthamarakshan, K. R. Varshney, L. R. Varshney, and P. Das. Equi-tuning: Group equivariant fine-tuning of pretrained models. arXiv, 2022. (Cited on 2, 6, 22)
[5] P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. F. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulçehre, H. F. Song, A. J. Ballard, J. Gilmer, G. E. Dahl, A. Vaswani, K. R. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. M. Botvinick, O. Vinyals, Y. Li, and R. Pascanu. Relational inductive biases, deep learning, and graph networks. arXiv, 2018. (Cited on 1, 22)
[6] Y. Bengio, N. Léonard, and A. C. Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv, abs/1308.3432, 2013. (Cited on 4, 24)
[7] G. W. Benton, M. Finzi, P. Izmailov, and A. G. Wilson. Learning invariances in neural networks from training data. In NeurIPS, 2020. (Cited on 22, 23)
[8] B. Bevilacqua, F. Frasca, D. Lim, B. Srinivasan, C. Cai, G. Balamurugan, M. M. Bronstein, and H. Maron. Equivariant subgraph aggregation networks. In ICLR, 2022. (Cited on 22)
[9] B. Bloem-Reddy and Y. W. Teh. Probabilistic symmetries and invariant neural networks. J. Mach. Learn. Res., 2020. (Cited on 2)
[10] A. Bogatskiy, S. Ganguly, T. Kipf, R. Kondor, D. W. Miller, D. Murnane, J. T. Offermann, M. Pettee, P. Shanahan, C. Shimmin, and S. Thais. Symmetry group equivariant architectures for physics. arXiv, 2022. (Cited on 1, 22)
[11] X. Bresson and T. Laurent. Residual gated graph convnets. arXiv, 2019. (Cited on 9, 10)
[12] M. M. Bronstein, J. Bruna, T. Cohen, and P. Velickovic. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv, 2021. (Cited on 1, 22)
[13] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei. Language models are few-shot learners. In NeurIPS, 2020. (Cited on 22)
[14] C. Cai and Y. Wang. A note on over-smoothing for graph neural networks. arXiv, 2020. (Cited on 4, 22)
[15] M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li. Simple and deep graph convolutional networks. In ICML, 2020. (Cited on 10)
[16] Z. Chen, S. Villar, L. Chen, and J. Bruna. On the equivalence between graph isomorphism testing and function approximation with gnns. In NeurIPS, 2019. (Cited on 9)
[17] E. Chien, C. Pan, J. Peng, and O. Milenkovic. You are allset: A multiset function framework for hypergraph neural networks. In ICLR, 2022. (Cited on 22)
[18] T. Cohen and M. Welling. Group equivariant convolutional networks. In ICML, 2016. (Cited on 22)
[19] T. S. Cohen and M. Welling. Steerable cnns. In ICLR, 2017. (Cited on 22)
[20] G. Cybenko. Approximation by superpositions of a sigmoidal function. Math. Control. Signals Syst., 1989. (Cited on 3, 22)
[21] G. Dasoulas, L. D. Santos, K. Scaman, and A. Virmaux. Coloring graph neural networks for node disambiguation. In IJCAI, 2020. (Cited on 22)
[22] M. Defferrard, X. Bresson, and P. Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In NeurIPS, 2016. (Cited on 7)
[23] C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. J. Guibas. Vector neurons: A general framework for so(3)-equivariant networks. In ICCV, 2021. (Cited on 1, 5, 8, 22, 24)
[24] J. Devlin, M. Chang, K. Lee, and K. Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, 2019. (Cited on 5, 22)
[25] T. Dinh, Y. Zeng, R. Zhang, Z. Lin, M. Gira, S. Rajput, J. Sohn, D. S. Papailiopoulos, and K. Lee. LIFT: language-interfaced fine-tuning for non-language machine learning tasks. In NeurIPS, 2022. (Cited on 22)