Shallow Learning In Materio.

Celestine Preetham Lawrence

Abstract: We introduce Shallow Learning In Materio (SLIM) as a resource-efficient method to realize closed-loop higher-order perceptrons. Our SLIM method provides a rebuttal to the Minsky school's disputes with the Rosenblatt school about the efficacy of learning representations in shallow perceptrons. As a proof-of-concept, here we devise a physically-scalable realization of the parity function. Our findings are relevant to artificial intelligence engineers, as well as neuroscientists and biologists.

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