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20052026
most citedAutomatic Differentiation of Algorithms for Machine Learning

24 citations · 125 across the 25 of their papers we have counts for

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cs.SC2012★ 20 cited

Confusion of Tagged Perturbations in Forward Automatic Differentiation of Higher-Order Functions

Oleksandr Manzyuk, Barak A. Pearlmutter, Alexey Andreyevich Radul +2

Forward Automatic Differentiation (AD) is a technique for augmenting programs to compute derivatives. The essence of Forward AD is to attach perturbations to each number, and propa…