Scaling description of generalization with number of parameters in deep learning

Supervised deep learning involves the training of neural networks with a large number \(N\) of parameters. For large enough \(N\), in the so-called over-parametrized regime, one can essentially fit the training data points. Sparsity-based arguments would suggest that the generalization error increas...

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