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Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond

, , and . arXiv preprint arXiv:1611.07476, (2016)

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Empirical Analysis of the Hessian of Over-Parametrized Neural Networks, , , , and . (2017)cite arxiv:1706.04454Comment: Minor update for ICLR 2018 Workshop Track presentation.Triple descent and the two kinds of overfitting: where & why do they appear?, , and . NeurIPS, (2020)Confusing Large Models by Confusing Small Models., , , , , and . ICCV (Workshops), page 4306-4314. IEEE, (2023)Empirical Analysis of the Hessian of Over-Parametrized Neural Networks., , , , and . CoRR, (2017)On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks., , , , and . CoRR, (2019)On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks, , , , and . (2019)cite arxiv:1912.00018Comment: 32 pages. arXiv admin note: substantial text overlap with arXiv:1901.06053.Empirical Analysis of the Hessian of Over-Parametrized Neural Networks., , , , and . ICLR (Workshop), OpenReview.net, (2018)Early predictability of asylum court decisions., , , and . ICAIL, page 233-236. ACM, (2017)Measuring and signing fairness as performance under multiple stakeholder distributions., , , and . CoRR, (2022)Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond, , and . arXiv preprint arXiv:1611.07476, (2016)