We consider learning on multi-layer neural nets with piecewise polynomial

activation functions and a fixed number k of numerical inputs. We exhibit

arbitrarily large network architectures for which efficient and provably

successful learning algorithms exist in the rather realistic refinement of

Valiant's model for probably approximately correct learning ("PAC-learning")

where ...
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