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Automatic differentiation (AD) is conventionally understood as a family of distinct algorithms, rooted in two "modes" -- forward and reverse -- which are typically presented (and implemented) separately.
Partial computation of programs. In RIMS Symposia on Software Science and Engineering , Eiichi Goto, Koichi Furukawa, Reiji Nakajima, Ikuo Nakata, and Akinori Yonezawa (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1–35
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Neil D. Jones, Carsten K. Gomard, and Peter Sestoft. 1993 · 1993
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Functional Differentiation of Computer Programs. In Proceedings of the Third ACM SIGPLAN International Conference on Functional Programming (Baltimore, Maryland, USA) (ICFP ’98) . Association for Computing Machinery, New York, NY, USA, 195–203
Jerzy Karczmarczuk. 1998 · 1998
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ADOL-C: Automatic Differentiation Using Operator Overloading in C++
Andrea Walther, Andreas Griewank, and Olaf Vogel. 2003 · 2003
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Automatic Differentiation, C++ Templates, and Photogrammetry
Dan Piponi. 2004 · 2004
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Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (second ed.)
Andreas Griewank and Andrea Walther. 2008 · 2008
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Reverse-mode AD in a functional framework: Lambda the ultimate backpropagator
Barak A Pearlmutter and Jeffrey Mark Siskind. 2008a · 2008
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Reverse-Mode AD in a Functional Framework: Lambda the Ultimate Backpropagator
Barak A. Pearlmutter and Jeffrey Mark Siskind. 2008b · 2008
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Beautiful Differentiation. In Proceedings of the 14th ACM SIGPLAN International Conference on Functional Programming (Edinburgh, Scotland) (ICFP ’09) . Association for Computing Machinery, New York, NY, USA, 191–202
Conal M. Elliott. 2009 · 2009
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Two Tricks for the Price of One: Linear Filters and Their Transposes
Dan Piponi. 2009 · 2009
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JAX: composable transformations of Python+NumPy programs
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. 2018 · 2018
Cited alongside, same era.
The Simple Essence of Automatic Differentiation
Conal Elliott. 2018 · 2018
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Backpropagation in the Simply Typed Lambda-Calculus with Linear Negation
Aloïs Brunel, Damiano Mazza, and Michele Pagani. 2019 · 2019
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Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator
Fei Wang, Daniel Zheng, James Decker, Xilun Wu, Grégory M. Essertel, and Tiark Rompf. 2019 · 2019
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Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing. In Foundations of Software Science and Computation Structures - 23rd International Conference, FOSSACS 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, 2020, Proceedings (Lecture Notes in Computer Science, Vol. 12077) , Jean Goubault-Larrecq and Barbara König (Eds.). Springer, 319–338
Mathieu Huot, Sam Staton, and Matthijs Vákár. 2020 · 2020
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Compiling machine learning programs via high-level tracing. In Machine Learning and Systems (MLSys)
Roy Frostig, Matthew Johnson, and Chris Leary. 2018 · 2018
Cited alongside, same era.
A Simple Differentiable Programming Language
Martín Abadi and Gordon D. Plotkin. 2019 · 2019
Cited alongside, same era.
Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, and Alexey Radul. 2021 · 2021
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Automatic Differentiation in PCF
Damiano Mazza and Michele Pagani. 2021 · 2021
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Getting to the Point: Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming
Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, and Dougal Maclaurin. 2021 · 2021
Later among the works it cites.
Provably Correct, Asymptotically Efficient, Higher-Order Reverse-Mode Automatic Differentiation
Faustyna Krawiec, Simon Peyton Jones, Neel Krishnaswami, Tom Ellis, Richard A. Eisenberg, and Andrew Fitzgibbon. 2022 · 2022
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