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The reverse derivative is a fundamental operation in machine learning and automatic differentiation.
Taylor expansion of the accumulated rounding error
S. Linnainmaa · 1976
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Learning representations by backpropagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1988
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Categorical logic and type theory
B. Jacobs · 1999
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Restriction categories i: categories of partial maps
J.R.B. Cockett and Stephen Lack · 2002
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Differential categories
R.F. Blute, J.R.B. Cockett, and R.A.G. Seely · 2006
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Dagger compact closed categories and completely positive maps
Peter Selinger · 2006
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Handbook of categorical algebra II
F. Borceaux · 2008
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Cartesian Differential Categories
R. Blute, R. Cockett, and R. Seely · 2009
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Differential restriction categories
J.R.B. Cockett, G.S.H. Cruttwell, and J.D. Gallagher · 2011
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A Leibniz notation for automatic differentiation
Bruce Christianson · 2012
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Who invented the reverse mode of differentiation
Andreas Griewank · 2012
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Differential structure, tangent structure, and SDG
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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Cartesian Differential Storage Categories
R. Blute, R. Cockett, and R. Seely · 2015
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The dual fibration in elementary terms
Anders Kock · 2015
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The simple essence of automatic differentiation
Conal Elliott · 2018
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A tangent category alternative to the faa di bruno construction
J-S P. Lemay · 2018
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A simple differential programming language
G. Plotkin · 2018
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