GPflow

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 25 articles , 1 standard article )

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  1. García, Constantino A.; Félix, Paulo; Presedo, Jesús M.; Otero, Abraham: Stochastic embeddings of dynamical phenomena through variational autoencoders (2022)
  2. Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder: GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language (2022) not zbMATH
  3. Wan, Pengbo; Alhebaishi, Nawaf; Liu, Qi: Financial time series using nonlinear differential equation of Gaussian distribution probability density (2022)
  4. 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)
  5. Hebbal, Ali; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Melab, Nouredine: Bayesian optimization using deep Gaussian processes with applications to aerospace system design (2021)
  6. Manson, Jamie A.; Chamberlain, Thomas W.; Bourne, Richard A.: MVMOO: mixed variable multi-objective optimisation (2021)
  7. Maulik, Romit; Botsas, Themistoklis; Ramachandra, Nesar; Mason, Lachlan R.; Pan, Indranil: Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation (2021)
  8. Pelamatti, Julien; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Guerin, Yannick: Bayesian optimization of variable-size design space problems (2021)
  9. Sadr, Mohsen; Wang, Qian; Gorji, M. Hossein: Coupling kinetic and continuum using data-driven maximum entropy distribution (2021)
  10. 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
  11. Wilson, James T.; Borovitskiy, Viacheslav; Terenin, Alexander; Mostowsky, Peter; Deisenroth, Marc Peter: Pathwise conditioning of Gaussian processes (2021)
  12. Burt, David R.; Rasmussen, Carl Edward; van der Wilk, Mark: Convergence of sparse variational inference in Gaussian processes regression (2020)
  13. Monterrubio-Gómez, Karla; Roininen, Lassi; Wade, Sara; Damoulas, Theodoros; Girolami, Mark: Posterior inference for sparse hierarchical non-stationary models (2020)
  14. Sadr, Mohsen; Torrilhon, Manuel; Gorji, M. Hossein: Gaussian process regression for maximum entropy distribution (2020)
  15. Schürch, Manuel; Azzimonti, Dario; Benavoli, Alessio; Zaffalon, Marco: Recursive estimation for sparse Gaussian process regression (2020)
  16. Bonilla, Edwin V.; Krauth, Karl; Dezfouli, Amir: Generic inference in latent Gaussian process models (2019)
  17. Dahl, Astrid; Bonilla, Edwin V.: Grouped Gaussian processes for solar power prediction (2019)
  18. Laloy, Eric; Jacques, Diederik: Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks (2019)
  19. 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
  20. Donner, Christian; Opper, Manfred: Efficient Bayesian inference of sigmoidal Gaussian Cox processes (2018)

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