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.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Monterrubio-Gómez, Karla; Roininen, Lassi; Wade, Sara; Damoulas, Theodoros; Girolami, Mark: Posterior inference for sparse hierarchical non-stationary models (2020)
- Bonilla, Edwin V.; Krauth, Karl; Dezfouli, Amir: Generic inference in latent Gaussian process models (2019)
- Dahl, Astrid; Bonilla, Edwin V.: Grouped Gaussian processes for solar power prediction (2019)
- Laloy, Eric; Jacques, Diederik: Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks (2019)
- Donner, Christian; Opper, Manfred: Efficient Bayesian inference of sigmoidal Gaussian Cox processes (2018)
- Hensman, James; Durrande, Nicolas; Solin, Arno: Variational Fourier features for Gaussian processes (2018)
- Schulz, Eric; Speekenbrink, Maarten; Krause, Andreas: A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (2018)
- 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)
- Nicolas Knudde, Joachim van der Herten, Tom Dhaene, Ivo Couckuyt: GPflowOpt: A Bayesian Optimization Library using TensorFlow (2017) arXiv