{"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/recurrent-gaussian-processes","title":"Recurrent Gaussian Processes","arxiv_id":"1511.06644","date":"2015-11-20","proceeding":null,"authors":["César Lincoln C. Mattos","Zhenwen Dai","Andreas Damianou","Jeremy Forth","Guilherme A. Barreto","Neil D. Lawrence"],"abstract":"We define Recurrent Gaussian Processes (RGP) models, a general family of\nBayesian nonparametric models with recurrent GP priors which are able to learn\ndynamical patterns from sequential data. Similar to Recurrent Neural Networks\n(RNNs), RGPs can have different formulations for their internal states,\ndistinct inference methods and be extended with deep structures. In such\ncontext, we propose a novel deep RGP model whose autoregressive states are\nlatent, thereby performing representation and dynamical learning\nsimultaneously. To fully exploit the Bayesian nature of the RGP model we\ndevelop the Recurrent Variational Bayes (REVARB) framework, which enables\nefficient inference and strong regularization through coherent propagation of\nuncertainty across the RGP layers and states. We also introduce a RGP extension\nwhere variational parameters are greatly reduced by being reparametrized\nthrough RNN-based sequential recognition models. We apply our model to the\ntasks of nonlinear system identification and human motion modeling. The\npromising obtained results indicate that our RGP model maintains its highly\nflexibility while being able to avoid overfitting and being applicable even\nwhen larger datasets are not available.","url_abs":"http://arxiv.org/abs/1511.06644v6","url_pdf":"http://arxiv.org/pdf/1511.06644v6.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":"recurrent-gaussian-processes","repo_url":"https://github.com/zhenwendai/RGP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.06644","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.06644"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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