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Automatic differentiation (AD) is an essential primitive for machine learning programming systems.
Hygienic macro expansion
Eugene Kohlbecker, Daniel P. Friedman, Matthias Felleisen, and Bruce Duba · 1986
Earlier work this paper cites.
An efficient gradient-based algorithm for on-line training of recurrent network trajectories
Ronald J Williams and Jing Peng · 1990
Earlier work this paper cites.
Computing derivatives of computer programs
C. H. Bischof and H. M. Bücker · 2000
Earlier work this paper cites.
Reverse-mode ad in a functional framework: Lambda the ultimate backpropagator
Barak A. Pearlmutter and Jeffrey Mark Siskind · 2008
Cited alongside, same era.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Cited alongside, same era.
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Theano: A python framework for fast computation of mathematical expressions
Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, et al · 2016
Later among the works it cites.
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