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GPflow: A Gaussian process library using TensorFlow

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Leon-Villagra, Zoubin Ghahramani, James Hensman
Department of Engineering, University of Cambridge, Cambridge, UK
arXiv:1610.08733 [stat.ML], (27 Oct 2016)

@article{matthews2016gpflow,

   title={GPflow: A Gaussian process library using TensorFlow},

   author={Matthews, Alexander G. de G. and Wilk, Mark van der and Nickson, Tom and Fujii, Keisuke and Boukouvalas, Alexis and Leon-Villagra, Pablo and Ghahramani, Zoubin and Hensman, James},

   year={2016},

   month={oct},

   archivePrefix={"arXiv"},

   primaryClass={stat.ML}

}

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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