Power-law Scaling to Assist with Key Challenges in Artificial Intelligence. (arXiv:2211.08430v1 [cs.LG])

Power-law scaling, a central concept in critical phenomena, is found to be
useful in deep learning, where optimized test errors on handwritten digit
examples converge as a power-law to zero with database size. For rapid decision
making with one training epoch, each example is presented only once to the
trained network, the power-law exponent increased with the number of hidden
layers. For the largest dataset, the obtained test error was estimated to be in
the proximity of state-of-the-art algorithms for …

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