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[FHL19] Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan. Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757, 2019.
[GJS+20] Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, and Matthieu Wyart. Scaling description of generalization with number of parameters in deep learning. Journal of Statistical Mechanics: Theory and Experiment, 2020(2):023401, 2020.
$\left[\mathrm{GLL}^{+}15\right]$  Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario March, and Jean-Francis Roy. Risk bounds for the majority vote: From a PAC-Bayesian analysis to a learning algorithm. Journal of Machine Learning Research, 16(26):787-860, 2015.
[HD19] Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. Proceedings of the International Conference on Learning Representations, 2019.
$\left[\mathrm{HVD}^{+}15\right]$  Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2(7), 2015.
[HZRS16] 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.
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[KH+09] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[LMRR17] François Laviolette, Emilie Morvant, Liva Ralaivola, and Jean-Francis Roy. Risk upper bounds for general ensemble methods with an application to multiclass classification. Neurocomputing, 219:15-25, 2017.
[LPB17] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017.
$\left[\mathrm{MDP}^{+}11\right]$  Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 142-150, June 2011.
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[MM17] C. H. Martin and M. W. Mahoney. Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior. Technical Report Preprint: arXiv:1710.09553, 2017.
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