[1] |
Baydin, A., Pearlmutter, B., Radul, A., and Siskind, J.. 2018. Automatic differentiation in machine learning: A survey. J. Mach. Learn. Res.18 (2018), 1-43. · Zbl 06982909 |
[2] |
Berz, Martin, Bischof, Christian, Corliss, George, and Griewank, Andreas (Eds.). 1996. Computational Differentiation: Techniques, Applications and Tools. SIAM, Philadelphia, PA. · Zbl 0857.00033 |
[3] |
Bischof, Christian H., Bücker, H. Martin, Hovland, Paul D., Naumann, Uwe, and Utke, Jean (Eds.). 2008. Advances in Automatic Differentiation. , Vol. 64. Springer, Berlin. DOI: · Zbl 1143.65003 |
[4] |
Bradbury, James, Frostig, Roy, Hawkins, Peter, Johnson, Matthew James, Leary, Chris, Maclaurin, Dougal, Necula, George, Paszke, Adam, VanderPlas, Jake, Wanderman-Milne, Skye, and Zhang, Qiao. 2018. JAX: Composable Transformations of Python+NumPy Programs. Retrieved from http://github.com/google/jax |
[5] |
Bücker, M., Corliss, G., Hovland, P., Naumann, U., and Norris, B.. 2005. Automatic Differentiation: Applications, Theory, and Tools. Springer. |
[6] |
Christianson, Bruce, Forth, Shaun A., and Griewank, Andreas (Eds.). 2018. Special Issue of Optimization Methods & Software: Advances in Algorithmic Differentiation. Taylor & Francis, vol. 33. · Zbl 1397.00042 |
[7] |
Corliss, George, Faure, Christèle, Griewank, Andreas, Hascoët, Laurent, and Naumann, Uwe (Eds.). 2002. Automatic Differentiation of Algorithms: From Simulation to Optimization. Springer, New York, NY. DOI: |
[8] |
Fagan, M., Hascoët, L., and Utke, J.. 2006. Data representation alternatives in semantically augmented numerical models. In 6th IEEE International Workshop on Source Code Analysis and Manipulation. |
[9] |
Forth, Shaun, Hovland, Paul, Phipps, Eric, Utke, Jean, and Walther, Andrea (Eds.). 2012. Recent Advances in Algorithmic Differentiation. , Vol. 87. Springer, Berlin. DOI: · Zbl 1247.65002 |
[10] |
Griewank, Andreas and Corliss, George F. (Eds.). 1991. Automatic Differentiation of Algorithms: Theory, Implementation, and Application. SIAM, Philadelphia, PA. · Zbl 0747.00030 |
[11] |
Griewank, A. and Walther, A.. 2008. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (2nd ed.). Vol. 105. SIAM, Philadelphia, PA. Retrieved from http://www.ec-securehost.com/SIAM/OT105.html · Zbl 1159.65026 |
[12] |
Hascoët, L. and Morlighem, M.. 2017. Source-to-source adjoint algorithmic differentiation of an ice sheet model written in C. Optim. Meth. Softw. 33, 4-6 (2017), 829-843. DOI: · Zbl 1453.86048 |
[13] |
Hascoët, L., Naumann, U., and Pascual, V.. 2005. “To be recorded” analysis in reverse-mode automatic differentiation. Fut. Gen. Comput. Syst.21, 8 (2005), 1401-1417. DOI: |
[14] |
Hascoët, Laurent and Utke, Jean. 2016. Programming language features, usage patterns, and the efficiency of generated adjoint code. Optim. Meth. Softw.31 (2016), 885-903. DOI: · Zbl 1365.65054 |
[15] |
Innes, M.. 2019. Don’t unroll adjoint: Differentiating SSA-form programs. Retrieved from https://fluxml.ai/Zygote.jl/latest/ |
[16] |
Naumann, U.. 2012. The Art of Differentiating Computer Programs: An Introduction to Algorithmic Differentiation. Vol. 24. SIAM, Philadelphia, PA. Retrieved from http://www.ec-securehost.com/SIAM/SE24.html · Zbl 1275.65015 |
[17] |
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Demaison, A., Antiga, L., and Lerer, A.. 2017. Automatic differentiation in PyTorch. In NIPS 2017 Workshop Autodiff. Retrieved from https://pytorch.org/ |
[18] |
Sagebaum, M., Albring, T., and Gauger, N. R.. 2019. High-performance derivative computations using CoDiPack. ACM Trans. Math. Softw.45, 4, Article 38 (2019), 26 pages. DOI: · Zbl 1486.65029 |
[19] |
Slusanschi, Emil I. and Dumitrel, Vlad. 2016. ADiJaC—Automatic differentiation of Java classfiles. ACM Trans. Math. Softw.43, 2, Article 9 (Sep.2016), 33 pages. DOI: · Zbl 1391.65045 |
[20] |
Merriënboer, Bart van, Moldovan, Dan, and Wiltschko, Alex B.. 2018. Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming. In NeurIPS’18. Retrieved from: https://github.com/google/tangent |
[21] |
Walther, A. and Griewank, A.. 2012. Getting started with ADOL-C. In Combinatorial Scientific Computing, Naumann, U. and Schenk, O. (Eds.). Chapman-Hall CRC Computational Science, 181-202. |