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Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradient-based optimization.
A New Approach to Linear Filtering and Prediction Problems
R. E. Kalman · 1960
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Non-Uniform Random Variate Generation
Luc Devroye · 1986
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Smoothed (conditional) perturbation analysis of discrete event dynamical systems
Wei-Bo Gong and Yu-Chi Ho · 1987
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On automatic differentiation
Andreas Griewank · 1989
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Sampling derivatives of probabilities
G Ch Pflug · 1989
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Likelihood ratio gradient estimation for stochastic systems
Peter W Glynn · 1990
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Smoothed perturbation analysis for a class of discrete-event systems
Paul Glasserman and W-B Gong · 1990
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Gradient estimation via perturbation analysis
Paul Glasserman and Yu-Chi Ho · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Some guidelines and guarantees for common random numbers
Paul Glasserman and David D Yao · 1992
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A characterization of almost everywhere continuous functions
Fernando Mazzone · 1995
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Optimization and sensitivity analysis of computer simulation models by the score function method
Jack PC Kleijnen and Reuven Y Rubinstein · 1996
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Automatic differentiation of algorithms: from simulation to optimization
George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, and Uwe Naumann · 2002
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Some new perspectives on the method of control variates
Peter W Glynn and Roberto Szechtman · 2002
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Stochastic simulation: algorithms and analysis
Søren Asmussen and Peter W Glynn · 2007
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Measure-valued differentiation for Markov chains
Bernd Heidergott and FJ Vázquez-Abad · 2008
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Sensitivity estimation for Gaussian systems
Bernd Heidergott, Felisa J Vázquez-Abad, and Warren Volk-Makarewicz · 2008
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Rule-based modeling of biochemical systems with BioNetGen
James R Faeder, Michael L Blinov, and William S Hlavacek · 2009
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Direct loss minimization for structured prediction
Tamir Hazan, Joseph Keshet, and David McAllester · 2010
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A tutorial on particle filtering and smoothing: Fifteen years later
Arnaud Doucet and Adam M Johansen · 2011
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Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
George Poyiadjis, Arnaud Doucet, and Sumeetpal S. Singh · 2011
Cited alongside, same era.
An efficient finite difference method for parameter sensitivities of continuous time markov chains
David F Anderson · 2012
Cited alongside, same era.
Conditional Monte Carlo: Gradient estimation and optimization applications
Michael C Fu and Jian-Qiang Hu · 2012
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.
Differentiable particle filters: End-to-end learning with algorithmic priors
Rico Jonschkowski, Divyam Rastogi, and Oliver Brock · 2018
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Differentiable particle filters: End-to-end learning with algorithmic priors
Rico Jonschkowski, Divyam Rastogi, and Oliver Brock · 2018
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Don’t unroll adjoint: Differentiating SSA-form programs
Michael Innes · 2018
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GaussianDistributions.jl, 2018
Moritz Schauer and contributors · 2018
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JuliaStats/Distributions.jl: a Julia package for probability distributions and associated functions, July 2019
Dahua Lin, John Myles White, Simon Byrne, Douglas Bates, Andreas Noack, John Pearson, Alex Arslan, Kevin Squire, David Anthoff, Theodore Papamarkou, Mathieu Besançon, Jan Drugowitsch, Moritz Schauer, and contributors · 2019
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Diederik P Kingma and Max Welling · 2013
Cited alongside, same era.
Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems
Lily A Chylek, Leonard A Harris, Chang-Shung Tung, James R Faeder, Carlos F Lopez, and William S Hlavacek · 2014
Cited alongside, same era.
Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
Cited alongside, same era.
Forward-mode automatic differentiation in Julia
Jarrett Revels, Miles Lubin, and Theodore Papamarkou · 2016
Cited alongside, same era.
Forward-mode automatic differentiation in Julia
Jarrett Revels, Miles Lubin, and Theodore Papamarkou · 2016
Cited alongside, same era.
Robust benchmarking in noisy environments
Jiahao Chen and Jarrett Revels · 2016
Cited alongside, same era.
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
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Monte Carlo gradient estimation in machine learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, and Andriy Mnih · 2020
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Direct policy gradients: Direct optimization of policies in discrete action spaces
Guy Lorberbom, Chris J Maddison, Nicolas Heess, Tamir Hazan, and Daniel Tarlow · 2020
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An introduction to sequential Monte Carlo
Nicolas Chopin and Omiros Papaspiliopoulos · 2020
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Differentiable particle filtering without modifying the forward pass
Adam Ścibior and Frank Wood · 2021
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Storchastic: A framework for general stochastic automatic differentiation
Emile van Krieken, Jakub Mikolaj Tomczak, and Annette Ten Teije · 2021
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Situating agent-based modelling in population health research
Eric Silverman, Umberto Gostoli, Stefano Picascia, Jonatan Almagor, Mark McCann, Richard Shaw, and Claudio Angione · 2021
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Differentiable particle filtering via entropy-regularized optimal transport
Adrien Corenflos, James Thornton, George Deligiannidis, and Arnaud Doucet · 2021
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Distributions.jl: Definition and modeling of probability distributions in the juliastats ecosystem
Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, and John Pearson · 2021
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Differentiable agent-based epidemiology
Ayush Chopra, Alexander Rodr´guez, Jayakumar Subramanian, Balaji Krishnamurthy, B Aditya Prakash, and Ramesh Raskar · 2022
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Efficient learning of the parameters of non-linear models using differentiable resampling in particle filters
Conor Rosato, Lee Devlin, Vincent Beraud, Paul Horridge, Thomas B. Schön, and Simon Maskell · 2022
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Adev: Sound automatic differentiation of expected values of probabilistic programs
Alexander K Lew, Mathieu Huot, Sam Staton, and Vikash K Mansinghka · 2022
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JuliaDiff/ChainRules.jl: v1.35.0, May 2022
Frames Catherine White, Michael Abbott, Miha Zgubic, Jarrett Revels, Alex Arslan, Seth Axen, Simeon Schaub, Nick Robinson, Yingbo Ma, Gaurav Dhingra, Will Tebbutt, Niklas Heim, David Widmann, Andrew David Werner Rosemberg, Niklas Schmitz, Christopher Rackauckas, Rainer Heintzmann, Frank Schäfer, Carlo Lucibello, Keno Fischer, Alex Robson, Jerry Ling, Matt Brzezinski, Andrei Zhabinski, Daniel Wennberg, Mathieu Besançon, Pietro Vertechi, Shashi Gowda, and Andrew Fitzgibbon · 2022
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Plots.jl–a user extendable plotting API for the julia programming language
Simon Christ, Daniel Schwabeneder, and Christopher Rackauckas · 2022
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