%0 Generic %1 matthews2016gpflow %A Matthews, Alexander G. de G. %A van der Wilk, Mark %A Nickson, Tom %A Fujii, Keisuke %A Boukouvalas, Alexis %A León-Villagrá, Pablo %A Ghahramani, Zoubin %A Hensman, James %D 2016 %K auto automatic derivation gaussian graphical inference model process %T GPflow: A Gaussian process library using TensorFlow %U http://arxiv.org/abs/1610.08733 %X 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.

@misc{matthews2016gpflow, 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.}, added-at = {2017-06-22T17:35:35.000+0200}, author = {Matthews, Alexander G. de 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}, biburl = {https://www.bibsonomy.org/bibtex/2fcb48cce13ada5a89540bea3b53f1e09/becker}, description = {[1610.08733] GPflow: A Gaussian process library using TensorFlow}, interhash = {a9e19ab5d3a9df991ae55f32daf4e449}, intrahash = {fcb48cce13ada5a89540bea3b53f1e09}, keywords = {auto automatic derivation gaussian graphical inference model process}, note = {cite arxiv:1610.08733}, timestamp = {2017-06-22T17:35:35.000+0200}, title = {GPflow: A Gaussian process library using TensorFlow}, url = {http://arxiv.org/abs/1610.08733}, year = 2016 }