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The Weber-Fechner Law observes that human perception scales as the logarithmof the stimulus. We argue that learning algorithms for human concepts couldbenefit from the Weber-Fechner Law. Specifically, we impose Weber-Fechner onsimple neural networks, with or without convolution, via the logarithmic powerseries of their sorted output. Our experiments show surprising performance andaccuracy on the MNIST data set within a few training iterations and limitedcomputational resources, suggesting that Weber-Fechner can accelerate machinelearning of human concepts.

Accelerating Machine Learning via the Weber-Fechner Law