Back برمجة تفاضلية Arabic Programació diferenciable Catalan Diferencovatelné programování Czech Дифференцируемое программирование Russian Differentierbar programmering Swedish 可微分编程 Chinese 可微編程 ZH-YUE
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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.
- ^ a b Innes, 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 [cs.PL].
- ^ "Differential Intelligence". October 2016. Retrieved 2022-10-19.
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