{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:35:35Z","timestamp":1770917735145,"version":"3.50.1"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2006"],"award-info":[{"award-number":["U21B2006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771361"],"award-info":[{"award-number":["61771361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Youth Innovation Team Project"},{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","award":["B18039"],"award-info":[{"award-number":["B18039"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chinese Central Government"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61525105"],"award-info":[{"award-number":["61525105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Innovation Team Project"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1812699"],"award-info":[{"award-number":["IIS-1812699"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tsp.2022.3160535","type":"journal-article","created":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T01:38:21Z","timestamp":1647653901000},"page":"2224-2238","source":"Crossref","is-referenced-by-count":3,"title":["Infinite Switching Dynamic Probabilistic Network With Bayesian Nonparametric Learning"],"prefix":"10.1109","volume":"70","author":[{"given":"Wenchao","family":"Chen","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5151-9388","authenticated-orcid":false,"given":"Bo","family":"Chen","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"}]},{"given":"Yicheng","family":"Liu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7644-7621","authenticated-orcid":false,"given":"Chaojie","family":"Wang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"}]},{"given":"Xiaojun","family":"Peng","sequence":"additional","affiliation":[{"name":"Research Academy of Rocket, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4046-163X","authenticated-orcid":false,"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"}]},{"given":"Mingyuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"McCombs School of Business, The University of Texas at Austin, Austin, TX, USA"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2009.2024987"},{"key":"ref38","article-title":"WHAI: Weibull hybrid autoencoding inference for deep topic modeling","author":"zhang","year":"0","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/281"},{"key":"ref32","article-title":"Auto-encoding variational bayes","author":"diederik","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref31","first-page":"2917","article-title":"A complete recipe for stochastic gradient MCMC","author":"ma","year":"0","journal-title":"Proc 29th Conf Neural Inf Process Syst"},{"key":"ref30","first-page":"3102","article-title":"Stochastic gradient Riemannian Langevin dynamics on the probability simplex","author":"patterson","year":"0","journal-title":"Proc 27th Conf Neural Inf Process Syst"},{"key":"ref37","article-title":"Categorical reparameterization with gumbel-softmax","author":"jang","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1214\/06-BA104"},{"key":"ref35","article-title":"Combinatorial stochastic processes","author":"jim","year":"2002"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.21236\/ADA238689"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2011.2141664"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1049\/cce:19920031"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref28","article-title":"Stick-breaking variational autoencoders","author":"eric","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref64","first-page":"662","article-title":"Radar HRRP target recognition based on linear dynamic model","author":"wang","year":"0","journal-title":"Proc CIE Int Conf of Radar"},{"key":"ref27","first-page":"1923","article-title":"Dirichlet process mixtures of generalized linear models","volume":"12","author":"hannah","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref29","first-page":"2101","article-title":"Structured inference networks for nonlinear state space models","author":"krishnan","year":"0","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref2","first-page":"431","article-title":"Learning nonlinear dynamical systems using an EM algorithm","volume":"11","author":"ghahramani","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MASSP.1986.1165342"},{"key":"ref20","first-page":"1135","article-title":"Infinite edge partition models for overlapping community detection and link prediction","author":"zhou","year":"0","journal-title":"Proc 18th Int Conf Artif Intell Statist"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2007.900167"},{"key":"ref21","first-page":"781","article-title":"Poisson-randomized gamma dynamical systems","author":"aaron","year":"0","journal-title":"Proc 33rd Conf Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/EUSIPCO.2015.7362728"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2010.2102756"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342360"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1134\/S0005117919020024"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref51","first-page":"4880","article-title":"Full-capacity unitary recurrent neural networks","author":"wisdom","year":"0","journal-title":"Proc 13th Conf Neural Inf Process Syst"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-019-0603-y"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/78.942617"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/7.845214"},{"key":"ref56","first-page":"1822","article-title":"Architectural complexity measures of recurrent neural networks","author":"zhang","year":"0","journal-title":"Proc 13th Conf Neural Inf Process Syst"},{"key":"ref55","first-page":"77","article-title":"Low-rank passthrough neural networks","author":"barone","year":"0","journal-title":"Proc Workshop Deep Learn Approaches Low-Resource NLP"},{"key":"ref54","first-page":"2980","article-title":"A recurrent latent variable model for sequential data","author":"chung","year":"0","journal-title":"Proc 29th Conf Neural Inf Process Syst"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"ref10","first-page":"8451","article-title":"Deep Poisson gamma dynamical systems","author":"guo","year":"0","journal-title":"Proc 32nd Conf Neural Inf Process Syst"},{"key":"ref11","first-page":"1","article-title":"Augmentable gamma belief networks","volume":"17","author":"zhou","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/0022-1694(80)90036-0"},{"key":"ref12","first-page":"914","article-title":"Recurrent switching linear dynamical systems","author":"linderman","year":"0","journal-title":"Proc 19th Int Conf Artif Intell Statist"},{"key":"ref13","first-page":"3601","article-title":"A disentangled recognition and nonlinear dynamics model for unsupervised learning","author":"fraccaro","year":"0","journal-title":"Proc 31st Annu Conf Neural Inf Process Syst"},{"key":"ref14","first-page":"553","article-title":"Switching linear dynamics for variational bayes filtering","author":"ehmck","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2398844"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2011.2168521"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3027470"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3065847"},{"key":"ref19","first-page":"6515","article-title":"Convolutional Poisson gamma belief network","author":"wang","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref4","first-page":"1462","article-title":"Beta-negative binomial process and Poisson factor analysis","author":"zhou","year":"0","journal-title":"Proc 15th Int Conf Artif Intell Statist"},{"key":"ref3","first-page":"1462","article-title":"Nonparametric Bayesian factor analysis for dynamic count matrices","volume":"38","author":"acharya","year":"0","journal-title":"Proc 18th Int Conf Artif Intell Statist"},{"key":"ref6","first-page":"1666","article-title":"Deep dynamic poisson factorization model","author":"gong","year":"0","journal-title":"Proc 31st Conf Neural Inf Process Syst"},{"key":"ref5","first-page":"5006","article-title":"Poisson-gamma dynamical systems","author":"schein","year":"0","journal-title":"Proc 13th Conf Neural Inf Process Syst"},{"key":"ref8","first-page":"2467","article-title":"Deep temporal sigmoid belief networks for sequence modeling","author":"gan","year":"0","journal-title":"Proc 29th Conf Neural Inf Process Syst"},{"key":"ref7","first-page":"1033","article-title":"Learning recurrent neural networks with hessian-free optimization","author":"martens","year":"0","journal-title":"Proc 28th Int Conf Mach Learn"},{"key":"ref49","article-title":"Skip RNN: Learning to skip state updates in recurrent neural networks","author":"campos","year":"0","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref9","first-page":"268","article-title":"Learning deep sigmoid belief networks with data augmentation","author":"gan","year":"0","journal-title":"Proc 18th Int Conf Artif Intell Statist"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2969450"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2018.09.041"},{"key":"ref48","first-page":"1120","article-title":"Unitary evolution recurrent neural networks","author":"arjovsky","year":"0","journal-title":"Proc 33rd Int Conf Mach Learn"},{"key":"ref47","article-title":"A simple way to initialize recurrent networks of rectified linear units","author":"le","year":"2015"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxh025"},{"key":"ref41","first-page":"864","article-title":"Deep latent Dirichlet allocation with topic-layer-adaptive stochastic gradient Riemannian MCMC","author":"cong","year":"0","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref44","first-page":"681","article-title":"Bayesian Learning via stochastic gradient langevin dynamics","author":"welling","year":"0","journal-title":"Proc 28th Int Conf Mach Learn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.211"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/78\/9675017\/9738490-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/9675017\/09738490.pdf?arnumber=9738490","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T20:21:02Z","timestamp":1655151662000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9738490\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":64,"URL":"https:\/\/doi.org\/10.1109\/tsp.2022.3160535","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"value":"1053-587X","type":"print"},{"value":"1941-0476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}