[1] T. Amir, S. J. Gortler, I. Avni, R. Ravina, and N. Dym. Neural injective functions for multisets, measures and graphs via a finite witness theorem. arXiv preprint arXiv:2306.06529, 2023.
[2] A. Backurs, Y. Dong, P. Indyk, I. Razenshteyn, and T. Wagner. Scalable nearest neighbor search for optimal transport. In International Conference on machine learning, pages 497-506. PMLR, 2020.
[3] P. Bongini, M. Bianchini, and F. Scarselli. Molecular generative graph neural networks for drug discovery. Neurocomputing, 450:242-252, 2021.
[4] C. Bueno and A. Hylton. On the representation power of set pooling networks. Advances in Neural Information Processing Systems, 34:17170-17182, 2021.
[5] N. Courty, R. Flamary, and M. Ducoffe. Learning Wasserstein embeddings. arXiv preprint arXiv:1710.07457, 2017.
[6] M. Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26, 2013.
[7] G. Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4):303-314, 1989.
[8] P. Demetci, R. Santorella, B. Sandstede, W. S. Noble, and R. Singh. Gromov-wasserstein optimal transport to align single-cell multi-omics data. BioRxiv, pages 2020–04, 2020.
[9] 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 Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12200-12209, 2021.
[10] G. Di Guglielmo, F. Fahim, C. Herwig, M. B. Valentin, J. Duarte, C. Gingu, P. Harris, J. Hirschauer, M. Kwok, V. Loncar, et al. A reconfigurable neural network asic for detector front-end data compression at the hl-lhc. IEEE Transactions on Nuclear Science, 68(8):2179-2186, 2021.
[11] W. Du, H. Zhang, Y. Du, Q. Meng, W. Chen, N. Zheng, B. Shao, and T.-Y. Liu. Se (3) equivariant graph neural networks with complete local frames. In International Conference on Machine Learning, pages 5583-5608. PMLR, 2022.
[12] N. Dym and S. J. Gortler. Low dimensional invariant embeddings for universal geometric learning. arXiv preprint arXiv:2205.02956, 2022.
[13] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl. Neural message passing for quantum chemistry. In International conference on machine learning, pages 1263-1272. PMLR, 2017.
[14] P. Indyk and N. Thaper. Fast image retrieval via embeddings. In 3rd international workshop on statistical and computational theories of vision, volume 2, page 5, 2003.
[15] K. Kawano, S. Koide, and T. Kutsuna. Learning wasserstein isometric embedding for point clouds. In 2020 International Conference on 3D Vision (3DV), pages 473-482. IEEE, 2020.
[16] N. Keriven and G. Peyre. Universal invariant and equivariant graph neural networks. Advances in Neural Information Processing Systems, 32, 2019.
[17] D. Lim, J. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron, and S. Jegelka. Sign and basis invariant networks for spectral graph representation learning. arXiv preprint arXiv:2202.13013, 2022.
[18] A. Makkuva, A. Taghvaei, S. Oh, and J. Lee. Optimal transport mapping via input convex neural networks. In International Conference on Machine Learning, pages 6672-6681. PMLR, 2020.