[19] Ralph B. D'Agostino. An omnibus test of normality for moderate and large size samples. Biometrika, 58(2):341-348, 1971. ISSN 00063444. URL http://www.jstor.org/stable/2334522.
[20] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[21] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[22] Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, and Chris J Maddison. Lossy compression for lossless prediction. Advances in Neural Information Processing Systems, 34, 2021.
[23] Marco Federici, Anjan Dutta, Patrick Forre, Nate Kushman, and Zeynep Akata. Learning robust representations via multi-view information bottleneck. In International Conference on Learning Representations, 2019.
[24] Marco Federici, Anjan Dutta, Patrick Forre, Nate Kushman, and Zeynep Akata. Learning robust representations via multi-view information bottleneck. arXiv preprint arXiv:2002.07017, 2020.
[25] Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan. Testing the manifold hypothesis. Journal of the American Mathematical Society, 29(4):983-1049, 2016.
[26] Ziv Goldfeld, Ewout van den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, and Yury Polyanskiy. Estimating information flow in deep neural networks. arXiv preprint arXiv:1810.05728, 2018.
[27] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning, volume 1. MIT Press, 2016.
[28] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020.
[29] Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nati Srebro. Implicit regularization in matrix factorization. In Advances in Neural Information Processing Systems, pages 6151-6159, 2017.
[30] Suriya Gunasekar, Jason D Lee, Daniel Soudry, and Nati Srebro. Implicit bias of gradient descent on linear convolutional networks. In Advances in Neural Information Processing Systems, pages 9461-9471, 2018.
[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015.
[32] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016.
[33] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020.
[34] R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670, 2018.
[35] Marco Huber, Tim Bailey, Hugh Durrant-Whyte, and Uwe Hanebeck. On entropy approximation for gaussian mixture random vectors. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pages 181 - 188, 09 2008. doi: 10.1109/MFI.2008.4648062.