{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gpflow-a-gaussian-process-library-using","title":"GPflow: A Gaussian process library using TensorFlow","arxiv_id":"1610.08733","date":"2016-10-27","proceeding":null,"authors":["Alexander G. de G. Matthews","Mark van der Wilk","Tom Nickson","Keisuke Fujii","Alexis Boukouvalas","Pablo León-Villagrá","Zoubin Ghahramani","James Hensman"],"abstract":"GPflow is a Gaussian process library that uses TensorFlow for its core\ncomputations and Python for its front end. The distinguishing features of\nGPflow are that it uses variational inference as the primary approximation\nmethod, provides concise code through the use of automatic differentiation, has\nbeen engineered with a particular emphasis on software testing and is able to\nexploit GPU hardware.","url_abs":"http://arxiv.org/abs/1610.08733v1","url_pdf":"http://arxiv.org/pdf/1610.08733v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gpflow-a-gaussian-process-library-using","repo_url":"https://github.com/GPflow/GPflow","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"software-testing","task_name":"software testing"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.08733","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}