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A Constructive Prediction of the Generalization Error Across Scales
[article]
2019
arXiv
pre-print
The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks. Nevertheless, the functional form of this dependency remains elusive. In this work, we present a functional form which approximates well the generalization error in practice. Capitalizing on the successful concept of model scaling (e.g., width, depth), we are able to simultaneously construct such a form and
arXiv:1909.12673v2
fatcat:kcrsitz5erg7vaibezdgbdwq2m