SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[19] H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman. Invariant and equivariant graph networks. arXiv preprint arXiv:1812.09902, 2018.
[20] H. Maron, E. Fetaya, N. Segol, and Y. Lipman. On the universality of invariant networks. In International conference on machine learning, pages 4363-4371. PMLR, 2019.
[21] H. Maron, O. Litany, G. Chechik, and E. Fetaya. On learning sets of symmetric elements. In International conference on machine learning, pages 6734-6744. PMLR, 2020.
[22] C. R. Qi, H. Su, K. Mo, and L. J. Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.
[23] F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini. The graph neural network model. IEEE transactions on neural networks, 20(1):61-80, 2008.
[24] B. Schmitzer. A sparse multiscale algorithm for dense optimal transport. Journal of Mathematical Imaging and Vision, 56(2):238-259, 2016.
[25] N. Segol and Y. Lipman. On universal equivariant set networks. arXiv preprint arXiv:1910.02421, 2019.
[26] R. Shenoy and The CMS Collaboration Team. EMD Neural Network for HGCAL Data Compression Encoder ASIC. In APS April Meeting Abstracts, volume 2022 of APS Meeting Abstracts, page Q09.008, Jan. 2022.
[27] A. Taghvaei and A. Jalali. 2-wasserstein approximation via restricted convex potentials with application to improved training for gans. arXiv preprint arXiv:1902.07197, 2019.
[28] M. A. Uy, Q.-H. Pham, B.-S. Hua, T. Nguyen, and S.-K. Yeung. Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1588-1597, 2019.
[29] E. Wagstaff, F. Fuchs, M. Engelcke, I. Posner, and M. A. Osborne. On the limitations of representing functions on sets. In International Conference on Machine Learning, pages 6487-6494. PMLR, 2019.
[30] E. Wagstaff, F. B. Fuchs, M. Engelcke, M. A. Osborne, and I. Posner. Universal approximation of functions on sets. Journal of Machine Learning Research, 23(151):1-56, 2022.
[31] Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015.
[32] Z. Yao, C. T. van Velthoven, T. N. Nguyen, J. Goldy, A. E. Sedeno-Cortes, F. Baftizadeh, D. Bertagnolli, T. Casper, M. Chiang, K. Crichton, et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell, 184(12):3222-3241, 2021.
[33] D. Yarotsky. Universal approximations of invariant maps by neural networks. Constructive Approximation, 55(1):407-474, 2022.
[34] M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola. Deep sets. Advances in neural information processing systems, 30, 2017.