DiffSharp: Automatic differentiation library. DiffSharp is a functional automatic differentiation (AD) library. AD allows exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is affected by expression swell and cannot fully handle algorithmic control flow. Using the DiffSharp library, differentiation (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) is applied using higher-order functions, that is, functions which take other functions as arguments. Your functions can use the full expressive capability of the language including control flow. DiffSharp allows composition of differentiation using nested forward and reverse AD up to any level, meaning that you can compute exact higher-order derivatives or differentiate functions that are internally making use of differentiation. Please see the API Overview page for a list of available operations. The library is developed by Atılım Güneş Baydin and Barak A. Pearlmutter mainly for research applications in machine learning, as part of their work at the Brain and Computation Lab, Hamilton Institute, National University of Ireland Maynooth. DiffSharp is implemented in the F# language and can be used from C# and the other languages running on Mono, .NET Core, or the .Net Framework, targeting the 64 bit platform. It is tested on Linux and Windows. We are working on interfaces/ports to other languages.

References in zbMATH (referenced in 36 articles )

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  1. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  2. Liu, Zeyu; Yang, Yantao; Cai, Qingdong: Neural network as a function approximator and its application in solving differential equations (2019)
  3. Raissi, Maziar; Wang, Zhicheng; Triantafyllou, Michael S.; Karniadakis, George Em: Deep learning of vortex-induced vibrations (2019)
  4. Raissi, M.; Perdikaris, P.; Karniadakis, G. E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (2019)
  5. Saremi, Saeed; Hyvärinen, Aapo: Neural empirical Bayes (2019)
  6. Vorontsova, E. A.; Gasnikov, A. V.; Gorbunov, E. A.: Accelerated directional search with non-Euclidean prox-structure (2019)
  7. Yang, Yibo; Perdikaris, Paris: Adversarial uncertainty quantification in physics-informed neural networks (2019)
  8. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  9. Giordano, Ryan; Broderick, Tamara; Jordan, Michael I.: Covariances, robustness, and variational Bayes (2018)
  10. Li, Qianxiao; Chen, Long; Tai, Cheng; E, Weinan: Maximum principle based algorithms for deep learning (2018)
  11. Geeraert, Sébastien; Lehalle, Charles-Albert; Pearlmutter, Barak A.; Pironneau, Olivier; Reghai, Adil: Mini-symposium on automatic differentiation and its applications in the financial industry (2017)
  12. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for machine learning (2017)
  13. Tingelstad, Lars; Egeland, Olav: Automatic multivector differentiation and optimization (2017)
  14. Diamond, Steven; Boyd, Stephen: Matrix-free convex optimization modeling (2016)
  15. Hoffmann, Philipp H. W.: A Hitchhiker’s guide to automatic differentiation (2016)
  16. Townsend, James; Koep, Niklas; Weichwald, Sebastian: Pymanopt: a Python toolbox for optimization on manifolds using automatic differentiation (2016)