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ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases.

, , , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 2286-2296. PMLR, (2021)

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