Differentiable programming is a programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation.[1][2][3][4][5] This allows for gradient-based optimization of parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher order derivative information. Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning.[5] One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016.[6]
^Izzo, Dario; Biscani, Francesco; Mereta, Alessio (2017). "Differentiable Genetic Programming". Genetic Programming. Lecture Notes in Computer Science. Vol. 10196. pp. 35–51. arXiv:1611.04766. doi:10.1007/978-3-319-55696-3_3. ISBN 978-3-319-55695-6. S2CID 17786263.
^Baydin, Atilim Gunes; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark (2018). "Automatic Differentiation in Machine Learning: a Survey". Journal of Marchine Learning Research. 18 (153): 1–43.
^Wang, Fei; Decker, James; Wu, Xilun; Essertel, Gregory; Rompf, Tiark (2018). "Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming" (PDF). In Bengio, S.; Wallach, H.; Larochelle, H.; Grauman, K (eds.). NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Curran Associates. pp. 10201–10212.
^Innes, Mike (2018). "On Machine Learning and Programming Languages" (PDF). SysML Conference 2018. Archived from the original (PDF) on 2019-07-17. Retrieved 2019-07-04.
^ abInnes, Mike; Edelman, Alan; Fischer, Keno; Rackauckas, Chris; Saba, Elliot; Viral B Shah; Tebbutt, Will (2019). "A Differentiable Programming System to Bridge Machine Learning and Scientific Computing". arXiv:1907.07587.
^"Differential Intelligence". October 2016. Retrieved 2022-10-19.
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