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Autograd

swMATH ID: 22077
Software Authors: Maclaurin, Dougal; Duvenaud, David; Johnson, Matt
Description: Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python’s features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization. For more information, check out the tutorial and the examples directory.
Homepage: https://github.com/HIPS/autograd
Source Code:  https://github.com/HIPS/autograd
Related Software: TensorFlow; PyTorch; Python; Theano; NumPy; SciPy; JAX; DiffSharp; Adam; GitHub; Scikit; Keras; Julia; Tangent; CVXPY; Pymanopt; Stan; Matlab; UCI-ml; OpenFermion
Cited in: 24 Documents
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Cited by 75 Authors

2 Doshi-Velez, Finale
2 Schoenholz, Samuel S.
2 Sun, WaiChing
2 Vlassis, Nikolaos N.
1 Ali, Alnur
1 Amos, Brandon D.
1 Bahri, Yasaman
1 Basak, Suryoday
1 Blanchard, Antoine B.
1 Blei, David M.
1 Boyd, Stephen Poythress
1 Briol, François-Xavier
1 Broderick, Tamara
1 Chouly, Franz
1 Cockayne, Jon
1 Cruttwell, Geoffrey S. H.
1 Cubuk, Ekin D.
1 Curtin, Ryan R.
1 Daulbaev, Talgat
1 Durbin, Richard
1 Edel, Marcus
1 Fitzgibbon, Andrew W.
1 Fuchs, Robin
1 Gallagher, Jonathan
1 Gelman, Andrew
1 Ghosh, Soumya Kanti
1 Giesen, Joachim
1 Giordano, Ryan
1 Giraldi, Loïc
1 Girolami, Mark A.
1 Gustafsson, Tom
1 Hild, Patrick
1 Hoteit, Ibrahim
1 Jordan, Michael Irwin
1 Joswig, Fabian
1 Kamm, Jack
1 Katrutsa, Aleksandr
1 Knio, Omar M.
1 Kuberski, Simon
1 Kucukelbir, Alp
1 Kuhlmann, Justus T.
1 Kukelova, Zuzana
1 Laue, Sören
1 Le Maître, Olivier P.
1 Lee, Jaehoon
1 Liang, Yanying
1 Lou, Zhihao
1 Masood, Muhammad A.
1 Mitterreiter, Matthias
1 Neuendorf, Jan
1 Novak, Roman
1 Oates, Chris J.
1 Omella, Ángel Javier
1 Oseledets, Ivan Valer’evich
1 Pardo, David
1 Peng, Wei
1 Pennington, Jeffrey S.
1 Pommeret, Denys
1 Prabhu, Rahul Ganesh
1 Pronk, Dorette A.
1 Ranganath, Rajesh
1 Sanderson, Conrad
1 Sapsis, Themistoklis P.
1 Shen, Xinyue
1 Silvén, Olli
1 Sohl-Dickstein, Jascha
1 Song, Yun S.
1 Srajer, Filip
1 Terhorst, Jonathan
1 Tran, Dustin
1 Viroli, Cinzia
1 Xiao, Lechao
1 Yao, Jiayu
1 Zhao, Guoying
1 Zheng, Zhujun
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Cited in 17 Serials

5 Journal of Machine Learning Research (JMLR)
2 Computer Methods in Applied Mechanics and Engineering
2 Journal of Statistical Mechanics: Theory and Experiment
1 Computers & Mathematics with Applications
1 Computer Physics Communications
1 Journal of the American Statistical Association
1 Journal of Computational and Applied Mathematics
1 Neural Networks
1 Computational Statistics and Data Analysis
1 Bernoulli
1 Optimization Methods & Software
1 Chaos
1 Fundamenta Informaticae
1 Optimization and Engineering
1 European Series in Applied and Industrial Mathematics (ESAIM): Mathematical Modelling and Numerical Analysis
1 Advances in Data Analysis and Classification. ADAC
1 Foundations and Trends in Machine Learning
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Cited in 14 Fields

14 Computer science (68-XX)
10 Statistics (62-XX)
8 Numerical analysis (65-XX)
3 Mechanics of deformable solids (74-XX)
3 Operations research, mathematical programming (90-XX)
2 Partial differential equations (35-XX)
2 Quantum theory (81-XX)
1 Linear and multilinear algebra; matrix theory (15-XX)
1 Category theory; homological algebra (18-XX)
1 Dynamical systems and ergodic theory (37-XX)
1 Calculus of variations and optimal control; optimization (49-XX)
1 Probability theory and stochastic processes (60-XX)
1 Mechanics of particles and systems (70-XX)
1 Biology and other natural sciences (92-XX)

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