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- W29610521 abstract "This paper discusses higher order derivative computing for universal learning networks that form a super set of all kinds of neural networks. Two computing algorithms, backward and forward propagation, are proposed. Using a technique called local description expresses the proposed algorithms very simply. Numerical simulations demonstrate the usefulness of higher order derivatives in neural network training." @default.
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- W29610521 date "1998-04-20" @default.
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- W29610521 title "Computing Higher Order Derivatives in Universal Learning Networks" @default.
- W29610521 doi "https://doi.org/10.20965/jaciii.1998.p0047" @default.
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