{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:52:16Z","timestamp":1767707536895,"version":"3.37.3"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tsp.2023.3303648","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T18:05:05Z","timestamp":1692036305000},"page":"2968-2980","source":"Crossref","is-referenced-by-count":11,"title":["Sequential Estimation of Gaussian Process-Based Deep State-Space Models"],"prefix":"10.1109","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9636-747X","authenticated-orcid":false,"given":"Yuhao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4613-5018","authenticated-orcid":false,"given":"Marzieh","family":"Ajirak","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7791-3199","authenticated-orcid":false,"given":"Petar M.","family":"Djuri\u0107","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1080\/13873950500068567"},{"key":"ref12","first-page":"3680","article-title":"Variational Gaussian process state-space models","author":"frigola","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2007.4399284"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-009-9119-x"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2021.3131988"},{"key":"ref10","first-page":"567","article-title":"Variational learning of inducing variables in sparse Gaussian processes","author":"titsias","year":"0","journal-title":"Proc Artif Intell Stat PMLR"},{"key":"ref17","first-page":"4","article-title":"Gaussian process dynamical models","volume":"18","author":"wang","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","first-page":"213","article-title":"Computationally efficient Bayesian learning of Gaussian process state space models","author":"svensson","year":"0","journal-title":"Proc Artif Intell Stat PMLR"},{"key":"ref19","first-page":"4","article-title":"Identification of Gaussian process state space models","volume":"30","author":"eleftheriadis","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1167"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2017.02.030"},{"key":"ref46","first-page":"1280","article-title":"Probabilistic recurrent state-space models","author":"doerr","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref45","first-page":"1","article-title":"How deep are deep Gaussian processes?","volume":"19","author":"dunlop","year":"2018","journal-title":"J Mach Learn Res"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2017.06.010"},{"key":"ref47","first-page":"2","article-title":"Gaussian process training with input noise","volume":"24","author":"mchutchon","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","first-page":"1333","article-title":"Latent variable modeling with random features","author":"gundersen","year":"0","journal-title":"Proc Int Conf Artif Intell Stat"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2330626"},{"key":"ref44","first-page":"884","article-title":"Random feature expansions for deep Gaussian processes","author":"cutajar","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO55093.2022.9909843"},{"key":"ref49","first-page":"227","article-title":"Optimizing long-term predictions for model-based policy search","author":"doerr","year":"0","journal-title":"Proc Conf Robot Learn"},{"key":"ref8","first-page":"1939","article-title":"A unifying view of sparse approximate Gaussian process regression","volume":"6","author":"candela","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065704001899"},{"key":"ref9","first-page":"524","article-title":"Local and global sparse Gaussian process approximations","author":"snelson","year":"0","journal-title":"Proc Artif Intell Stat PMLR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-1066-6"},{"key":"ref6","volume":"2","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1201\/b22524"},{"key":"ref40","first-page":"3","article-title":"A tutorial on particle filtering and smoothing: Fifteen years later","volume":"12","author":"doucet","year":"2009","journal-title":"Handbook of Nonlinear Filtering"},{"key":"ref35","first-page":"1910","article-title":"Ensemble Gaussian processes with spectral features for online interactive learning with scalability","author":"lu","year":"0","journal-title":"Proc Int Conf Artif Intell Stat"},{"key":"ref34","first-page":"1897","article-title":"Variational mixture of Gaussian process experts","author":"yuan","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"An Introduction to Bayesian Inference in Econometrics","year":"1971","author":"zellner","key":"ref37"},{"key":"ref36","first-page":"1865","article-title":"Sparse spectrum Gaussian process regression","volume":"11","author":"l\u00e1zaro-gredilla","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref31","first-page":"883","article-title":"An alternative infinite mixture of Gaussian process experts","volume":"18","author":"meeds","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","first-page":"1481","article-title":"Distributed Gaussian processes","author":"deisenroth","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref33","first-page":"654","article-title":"Mixtures of Gaussian processes","author":"tresp","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","first-page":"881","article-title":"Infinite mixtures of Gaussian process experts","volume":"2","author":"rasmussen","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref2"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130030"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2003.1236770"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/78.978374"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00246"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00363"},{"key":"ref26","first-page":"7785","article-title":"Deep state space models for time series forecasting","volume":"31","author":"rangapuram","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"Deep latent variable models for sequential data","year":"2018","author":"fraccaro","key":"ref25"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2448527"},{"article-title":"Rao-Blackwellised particle filtering for dynamic Bayesian networks","year":"2013","author":"doucet","key":"ref22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2021.08.406"},{"key":"ref28","first-page":"6607","article-title":"Deep factors for forecasting","author":"wang","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"article-title":"Deep state-space Gaussian processes","year":"2020","author":"zhao","key":"ref29"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/78\/10040758\/10216326-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/10040758\/10216326.pdf?arnumber=10216326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T18:11:59Z","timestamp":1696270319000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10216326\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/tsp.2023.3303648","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"type":"print","value":"1053-587X"},{"type":"electronic","value":"1941-0476"}],"subject":[],"published":{"date-parts":[[2023]]}}}