GPflow: a Gaussian process library using tensorflow. GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware.

References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Grosnit, Antoine; Cowen-Rivers, Alexander I.; Tutunov, Rasul; Griffiths, Ryan-Rhys; Wang, Jun; Bou-Ammar, Haitham: Are we forgetting about compositional optimisers in Bayesian optimisation? (2021)
  2. Hebbal, Ali; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Melab, Nouredine: Bayesian optimization using deep Gaussian processes with applications to aerospace system design (2021)
  3. Manson, Jamie A.; Chamberlain, Thomas W.; Bourne, Richard A.: MVMOO: mixed variable multi-objective optimisation (2021)
  4. Pelamatti, Julien; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Guerin, Yannick: Bayesian optimization of variable-size design space problems (2021)
  5. Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc P. Deisenroth, ST John: GPflux: A Library for Deep Gaussian Processes (2021) arXiv
  6. Wilson, James T.; Borovitskiy, Viacheslav; Terenin, Alexander; Mostowsky, Peter; Deisenroth, Marc Peter: Pathwise conditioning of Gaussian processes (2021)
  7. Burt, David R.; Rasmussen, Carl Edward; van der Wilk, Mark: Convergence of sparse variational inference in Gaussian processes regression (2020)
  8. Monterrubio-Gómez, Karla; Roininen, Lassi; Wade, Sara; Damoulas, Theodoros; Girolami, Mark: Posterior inference for sparse hierarchical non-stationary models (2020)
  9. Schürch, Manuel; Azzimonti, Dario; Benavoli, Alessio; Zaffalon, Marco: Recursive estimation for sparse Gaussian process regression (2020)
  10. Bonilla, Edwin V.; Krauth, Karl; Dezfouli, Amir: Generic inference in latent Gaussian process models (2019)
  11. Dahl, Astrid; Bonilla, Edwin V.: Grouped Gaussian processes for solar power prediction (2019)
  12. Laloy, Eric; Jacques, Diederik: Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks (2019)
  13. Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz: Neural Tangents: Fast and Easy Infinite Neural Networks in Python (2019) arXiv
  14. Donner, Christian; Opper, Manfred: Efficient Bayesian inference of sigmoidal Gaussian Cox processes (2018)
  15. Hensman, James; Durrande, Nicolas; Solin, Arno: Variational Fourier features for Gaussian processes (2018)
  16. Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson: GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration (2018) arXiv
  17. Schulz, Eric; Speekenbrink, Maarten; Krause, Andreas: A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (2018)
  18. Matthews, Alexander G. De G.; van der Wilk, Mark; Nickson, Tom; Fujii, Keisuke; Boukouvalas, Alexis; León-Villagrá, Pablo; Ghahramani, Zoubin; Hensman, James: GPflow: a Gaussian process library using TensorFlow (2017)
  19. Nicolas Knudde, Joachim van der Herten, Tom Dhaene, Ivo Couckuyt: GPflowOpt: A Bayesian Optimization Library using TensorFlow (2017) arXiv