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cs.PL2024
Composing Automatic Differentiation with Custom Derivatives of Higher-Order Functions
Sam Estep
Recent theoretical work on automatic differentiation (autodiff) has focused on characteristics such as correctness and efficiency while assuming that all derivatives are automatica…
cs.PL2024
Rose: Composable Autodiff for the Interactive Web
Sam Estep, Wode Ni, Raven Rothkopf +1
Reverse-mode automatic differentiation (autodiff) has been popularized by deep learning, but its ability to compute gradients is also valuable for interactive use cases such as bid…