{"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/gaussian-process-random-fields","title":"Gaussian Process Random Fields","arxiv_id":"1511.00054","date":"2015-10-31","proceeding":"NeurIPS 2015 12","authors":["David A. Moore","Stuart J. Russell"],"abstract":"Gaussian processes have been successful in both supervised and unsupervised\nmachine learning tasks, but their computational complexity has constrained\npractical applications. We introduce a new approximation for large-scale\nGaussian processes, the Gaussian Process Random Field (GPRF), in which local\nGPs are coupled via pairwise potentials. The GPRF likelihood is a simple,\ntractable, and parallelizeable approximation to the full GP marginal\nlikelihood, enabling latent variable modeling and hyperparameter selection on\nlarge datasets. We demonstrate its effectiveness on synthetic spatial data as\nwell as a real-world application to seismic event location.","url_abs":"http://arxiv.org/abs/1511.00054v1","url_pdf":"http://arxiv.org/pdf/1511.00054v1.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":"gaussian-process-random-fields","repo_url":"https://github.com/davmre/gprf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-2.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.00054","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}