ML Anthology

GPflow: A Gaussian Process Library Using TensorFlow

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman
MLOSS 2017 pp. 1-6
/mloss/2017/degmatthews2017jmlr-gpflow/

Abstract

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.

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Cite

Text

de G. Matthews et al. "GPflow: A Gaussian Process Library Using TensorFlow." Machine Learning Open Source Software, 2017.

Markdown

[de G. Matthews et al. "GPflow: A Gaussian Process Library Using TensorFlow." Machine Learning Open Source Software, 2017.](https://mlanthology.org/mloss/2017/degmatthews2017jmlr-gpflow/)

BibTeX

@article{degmatthews2017jmlr-gpflow,
  title     = {{GPflow: A Gaussian Process Library Using TensorFlow}},
  author    = {de G. Matthews, Alexander G. and van der Wilk, Mark and Nickson, Tom and Fujii, Keisuke and Boukouvalas, Alexis and León-Villagrá, Pablo and Ghahramani, Zoubin and Hensman, James},
  journal   = {Machine Learning Open Source Software},
  year      = {2017},
  pages     = {1-6},
  volume    = {18},
  url       = {https://mlanthology.org/mloss/2017/degmatthews2017jmlr-gpflow/}
}