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No single Automatic Differentiation (AD) system is the optimal choice for all problems.
Zygote: A differentiable programming system to bridge machine learning and scientific computing
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A. Wachter · 2002
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Perturbation confusion and referential transparency: Correct functional implementation of forward-mode AD
J. M. Siskind and B. A. Pearlmutter · 2005
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M. Udell, K. Mohan, D. Zeng, J. Hong, S. Diamond, and S. Boyd · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
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Theano: A Python framework for fast computation of mathematical expressions
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Forward-mode automatic differentiation in julia
J. Revels, M. Lubin, and T. Papamarkou · 2016
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Probabilistic programming in python using PyMC3
J. Salvatier, T. V. Wiecki, and C. Fonnesbeck · 2016
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Jump: A modeling language for mathematical optimization
I. Dunning, J. Huchette, and M. Lubin · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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End-to-end differentiable learning of protein structure
M. AlQuraishi · 2018
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Effective extensible programming: Unleashing Julia on GPUs
T. Besard, C. Foket, and B. De Sutter · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang · 2018
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ReverseDiff.jl, 2018
J. Revels · 2018
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Spnets: Differentiable fluid dynamics for deep neural networks
C. Schenck and D. Fox · 2018
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A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning
F. Srajer, Z. Kukelova, and A. Fitzgibbon · 2018
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Perturbation confusion in forward automatic differentiation of higher-order functions
O. Manzyuk, B. A. Pearlmutter, A. A. Radul, D. R. Rush, and J. M. Siskind · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2019
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Non-local compiler transformations in the presence of dynamic dispatch
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Instead of rewriting foreign code for machine learning, automatically synthesize fast gradients
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Juliadiff/chainrules.jl: v1.11.5, Sept. 2021
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