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Theorems from universal algebra such as that of Murski\u{i} from the 1970shave a striking similarity to universal approximation results for neural netsalong the lines of Cybenko's from the 1980s. We consider here a discreteanalogue of the classical notion of a neural net which places these results ina unified setting. We introduce a learning algorithm based on polymorphisms ofrelational structures and show how to use it for a classical learning task.

Discrete neural nets and polymorphic learning