[29] Y. Jiang, D. Krishnan, H. Mobahi, and S. Bengio. Predicting the generalization gap in deep networks with margin distributions. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=HJlQfnCqKX.
[30] Y. Jiang, P. Natekar, M. Sharma, S. K. Aithal, D. Kashyap, N. Subramanyam, C. Lassance, D. M. Roy, G. K. Dziugaite, S. Gunasekar, et al. Methods and analysis of the first competition in predicting generalization of deep learning. In NeurIPS 2020 Competition and Demonstration Track, pages 170–190. PMLR, 2021.
[31] M. G. Kendall. A new measure of rank correlation. Biometrika, 30(1/2):81-93, 1938.
[32] D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
[33] B. Knyazev, M. Drozdal, G. W. Taylor, and A. Romero Soriano. Parameter prediction for unseen deep architectures. Advances in Neural Information Processing Systems, 34:29433-29448, 2021.
[34] R. Kondor and S. Trivedi. On the generalization of equivariance and convolution in neural networks to the action of compact groups. In International Conference on Machine Learning, pages 2747-2755. PMLR, 2018.
[35] A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[36] D. Krueger, C.-W. Huang, R. Islam, R. Turner, A. Lacoste, and A. Courville. Bayesian hypernetworks. arXiv preprint arXiv:1710.04759, 2017.
[37] Y. LeCun, Y. Bengio, et al. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995, 1995.
[38] Y. LeCun, C. Cortes, and C. Burges. Mnist handwritten digit database. ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist, 2, 2010.
[39] K. Li and J. Malik. Learning to optimize. arXiv preprint arXiv:1606.01885, 2016.
[40] H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman. Invariant and equivariant graph networks. arXiv preprint arXiv:1812.09902, 2018.
[41] 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.
[42] C. H. Martin and M. W. Mahoney. Implicit self-regularization in deep neural networks: Evidence from random matrix theory and implications for learning. The Journal of Machine Learning Research, 22(1):7479–7551, 2021.
[43] L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019.
[44] L. Metz, J. Harrison, C. D. Freeman, A. Merchant, L. Beyer, J. Bradbury, N. Agrawal, B. Poole, I. Mordatch, A. Roberts, et al. Velo: Training versatile learned optimizers by scaling up. arXiv preprint arXiv:2211.09760, 2022.
[45] B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng. Nerf: representing scenes as neural radiance fields for view synthesis (2020). arXiv preprint arXiv:2003.08934, 2020.
[46] E. Mitchell, C. Lin, A. Bosselut, C. Finn, and C. D. Manning. Fast model editing at scale. arXiv preprint arXiv:2110.11309, 2021.
[47] A. Navon, A. Shamsian, I. Achituve, E. Fetaya, G. Chechik, and H. Maron. Equivariant architectures for learning in deep weight spaces. arXiv preprint arXiv:2301.12780, 2023.