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The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression. (arXiv:2201.05149v1 [cs.LG])
Jan. 14, 2022, 2:10 a.m. | Hamed Hassani, Adel Javanmard
cs.LG updates on arXiv.org arxiv.org
Successful deep learning models often involve training neural network
architectures that contain more parameters than the number of training samples.
Such overparametrized models have been extensively studied in recent years, and
the virtues of overparametrization have been established from both the
statistical perspective, via the double-descent phenomenon, and the
computational perspective via the structural properties of the optimization
landscape.
Despite the remarkable success of deep learning architectures in the
overparametrized regime, it is also well known that these models are …
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