Perturbation confusion in forward automatic differentiation of higher-order functions
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[1] A. Church. The calculi of lambda-conversion , 1941 .
[2] R. E. Wengert,et al. A simple automatic derivative evaluation program , 1964, Commun. ACM.
[3] B. Speelpenning. Compiling Fast Partial Derivatives of Functions Given by Algorithms , 1980 .
[4] Louis B. Rall,et al. Automatic differentiation , 1981 .
[5] Griewank,et al. On automatic differentiation , 1988 .
[6] Andreas Griewank,et al. ADIFOR - Generating Derivative Codes form Fortran Programs , 1992, Sci. Program..
[7] René Lavendhomme,et al. Basic Concepts of Synthetic Differential Geometry , 1996 .
[8] C. Bendtsen. FADBAD, a flexible C++ package for automatic differentiation - using the forward and backward method , 1996 .
[9] Andreas Griewank,et al. Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.
[10] Jerzy Karczmarczuk. Functional Differentiation of Computer Programs , 2001, High. Order Symb. Comput..
[11] Gerald J. Sussman,et al. Structure and interpretation of classical mechanics , 2001 .
[12] Andrew M. Pitts. Nominal logic, a first order theory of names and binding , 2003, Inf. Comput..
[13] Laurent Regnier,et al. The differential lambda-calculus , 2003, Theor. Comput. Sci..
[14] Laurent Hascoët,et al. TAPENADE 2.1 user's guide , 2004 .
[15] Barak A. Pearlmutter,et al. Perturbation Confusion and Referential Transparency:Correct Functional Implementation of Forward-Mode AD , 2005 .
[16] Barak A. Pearlmutter,et al. Lazy multivariate higher-order forward-mode AD , 2007, POPL '07.
[17] Barak A. Pearlmutter,et al. First-class nonstandard interpretations by opening closures , 2007, POPL '07.
[18] Barak A. Pearlmutter,et al. Nesting forward-mode AD in a functional framework , 2008, High. Order Symb. Comput..
[19] Barak A. Pearlmutter,et al. Using Programming Language Theory to Make Automatic Differentiation Sound and Efficient , 2008 .
[20] Conal Elliott. Beautiful differentiation , 2009, ICFP.
[21] Isaac Sir Newton,et al. Opticks, or a Treatise of the Reflexions, Refractions, Inflexions and Colours of Light: Also Two Treatises of the Species and Magnitude of Curvilinear Figures , 2010 .
[22] James Cheney,et al. A dependent nominal type theory , 2012, Log. Methods Comput. Sci..
[23] Oleksandr Manzyuk. A Simply Typed λ-Calculus of Forward Automatic Differentiation , 2012, MFPS.
[24] Gerald Jay Sussman,et al. Functional Differential Geometry , 2013 .
[25] Ryan P. Adams,et al. Gradient-based Hyperparameter Optimization through Reversible Learning , 2015, ICML.
[26] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[27] Barak A. Pearlmutter,et al. Evolving the Incremental Lambda Calculus into a Model of Forward Automatic Differentiation (AD) , 2016 .
[28] Barak A. Pearlmutter,et al. DiffSharp: An AD Library for .NET Languages , 2016, ArXiv.
[29] Barak A. Pearlmutter,et al. P L ] 1 0 N ov 2 01 6 Evolving the Incremental λ Calculus into a Model of Forward AD ∗ , 2016 .
[30] Conal Elliott. Compiling to categories , 2017, Proc. ACM Program. Lang..
[31] Bart van Merriënboer,et al. Automatic Differentiation in Myia , 2017 .
[32] Nematollah Batmanghelich,et al. Deep Diffeomorphic Normalizing Flows , 2018, ArXiv.
[33] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[34] Tom Ronan,et al. OpenEnsembles: A Python Resource for Ensemble Clustering , 2018, J. Mach. Learn. Res..
[35] Dan Moldovan,et al. Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming , 2018, NeurIPS.
[36] Maziar Raissi,et al. Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations , 2018, J. Mach. Learn. Res..