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We decompose reverse-mode automatic differentiation into (forward-mode) linearization followed by transposition.
Evaluating derivatives: principles and techniques of algorithmic differentiation
Andreas Griewank and Andrea Walther · 2008
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Two tricks for the price of one: Linear filters and their transposes
Dan Piponi · 2009
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TensorFlow: A system for large-scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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DiffSharp: An AD library for .NET languages
Atılım Güneş Baydin, Barak A Pearlmutter, and Jeffrey Mark Siskind · 2016
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Automatic differentiation in machine learning: a survey
Atılım Günes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
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
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The simple essence of automatic differentiation
Conal Elliott · 2018
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Compiling machine learning programs via high-level tracing
Roy Frostig, Matthew James Johnson, and Chris Leary · 2018
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A differentiable programming system to bridge machine learning and scientific computing
Mike Innes, Alan Edelman, Keno Fischer, Chris Rackauckus, Elliot Saba, Viral B Shah, and Will Tebbutt · 2019
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Dex: array programming with typed indices
Dougal Maclaurin, Alexey Radul, Matthew J. Johnson, and Dimitrios Vytiniotis · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Getting to the point. index sets and parallelism-preserving autodiff for pointful array programming
Adam Paszke, Daniel Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew Johnson, Jonathan Ragan-Kelley, and Dougal Maclaurin · 2021
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