V. Papyan, J. Sulam, and M. Elad. (2017)cite arxiv:1707.06066Comment: This is the journal version of arXiv:1607.02005 and arXiv:1607.02009, accepted to IEEE Transactions on Signal Processing.
A. Fisher, and E. Kennedy. (2018)cite arxiv:1810.03260Comment: This manuscript version includes 2 additional supplemental figures to further aid intuition. In total: 4 figures, 36 pages (double spaced).
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein. (2017)cite arxiv:1712.09913Comment: NIPS 2018 (extended version, 10.5 pages), code is available at https://github.com/tomgoldstein/loss-landscape.
J. Hron, A. Matthews, and Z. Ghahramani. Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, page 2019--2028. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)
J. Huggins, M. Kasprzak, T. Campbell, and T. Broderick. (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.
M. Vadera, A. Cobb, B. Jalaian, and B. Marlin. (2020)cite arxiv:2007.04466Comment: Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
D. Soudry, E. Hoffer, M. Nacson, S. Gunasekar, and N. Srebro. (2017)cite arxiv:1710.10345Comment: Final JMLR version, with improved discussions over v3. Main improvements in journal version over conference version (v2 appeared in ICLR): We proved the measure zero case for main theorem (with implications for the rates), and the multi-class case.
S. Mei, and A. Montanari. (2019)cite arxiv:1908.05355Comment: We added two sections in version 3. One section provides the precise asymptotics of the training error. The other section describes a Gaussian covariate model, which gives the same asymptotic test error as the random features model.
C. Chu, K. Minami, and K. Fukumizu. (2020)cite arxiv:2004.01822Comment: ICLR 2020, Workshop on Integration of Deep Neural Models and Differential Equations.
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