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2017 JMLR JMLR 2017

GPflow: A Gaussian Process Library using TensorFlow

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. [abs] [ pdf ][ bib ] [ code ] [ webpage ] © JMLR 2017. (edit, beta)

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Alexander G. de G. Matthews , Mark van der Wilk , Tom Nickson , Keisuke Fujii , Alexis Boukouvalas , Pablo León-Villagrá , Zoubin Ghahramani , James Hensman

Topics

Machine Learning > Optimization & Theory > Bayesian Inference Deep Learning > Models > Variational Inference

Keywords

variational inference automatic differentiation gaussian process
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