“Exhaustive Learning”, D. B. Schwartz, V. K. Samalam, Sara A. Solla, J. S. Denker1990-09-01 (neural net, AI scaling; similar)⁠:

Exhaustive exploration of an ensemble of networks is used to model learning and generalization in layered neural networks. A simple Boolean learning problem involving networks with binary weights is numerically solved to obtain the entropy S_m and the average generalization ability G_m as a function of the size m of the training set.

Learning curves G_m vs m are shown to depend solely on the distribution of generalization abilities over the ensemble of networks.

Such distribution is determined prior to learning, and provides a novel theoretical tool for the prediction of network performance on a specific task.

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