Deep learning generalization error follows power-law scaling with training set size across multiple domains, with model size scaling sublinearly with data size.
Similar to word language models, we use normalized cross-entropy loss:− 1 N ∑ ilnpwi, wherepwi is the model’s predicted probability of seeing theith token
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Deep Learning Scaling is Predictable, Empirically
Deep learning generalization error follows power-law scaling with training set size across multiple domains, with model size scaling sublinearly with data size.