{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T17:31:09Z","timestamp":1767720669204,"version":"3.37.3"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100011273","name":"European ICT Programme","doi-asserted-by":"publisher","award":["FP7-224631"],"award-info":[{"award-number":["FP7-224631"]}],"id":[{"id":"10.13039\/100011273","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tools for Brain-Computer Interaction"},{"DOI":"10.13039\/501100003475","name":"Hasler Foundation, Switzerland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003475","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1109\/tnnls.2020.3011671","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T21:16:29Z","timestamp":1597094189000},"page":"3471-3483","source":"Crossref","is-referenced-by-count":7,"title":["Context-Aware Learning for Generative Models"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2033-2486","authenticated-orcid":false,"given":"Serafeim","family":"Perdikis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Leeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8879-2860","authenticated-orcid":false,"given":"Ricardo","family":"Chavarriaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5819-1522","authenticated-orcid":false,"given":"Jose del R.","family":"Millan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"9","article-title":"A P300 BCI for the masses: Prior information enables instant unsupervised spelling","author":"kindermans","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","first-page":"52","article-title":"Text classification by bootstrapping with keywords, EM and shrinkage","author":"mccallum","year":"1999","journal-title":"Proc Workshop Unsupervised Learn Natural Lang Process"},{"key":"ref33","first-page":"43","article-title":"Alternating projections for learning with expectation constraints","author":"bellare","year":"2009","journal-title":"Proc 25th Conf Uncertainty Artif Intell (UAI)"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390436"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273571"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553457"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2009.5178922"},{"key":"ref36","first-page":"1799","article-title":"Bayesian inference with posterior regularization and applications to infinite latent SVMs","volume":"15","author":"zhu","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1031"},{"key":"ref34","first-page":"208","article-title":"An efficient posterior regularized latent variable model for interactive sound source separation","volume":"28","author":"bryan","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn (ICML)"},{"key":"ref28","first-page":"465","article-title":"Computing Gaussian mixture models with EM using equivalence constraints","author":"shental","year":"2004","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","first-page":"27","article-title":"Semi-supervised clustering by seeding","author":"basu","year":"2002","journal-title":"Proc 19th Int'l Conf Machine Learning (ICML)"},{"key":"ref29","first-page":"280","article-title":"Guiding semi-supervision with constraint-driven learning","author":"chang","year":"2007","journal-title":"Proc Assoc Comp Ling (ACL)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.84"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2010.0034"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.03.027"},{"key":"ref24","first-page":"1252","article-title":"Learning from weak teachers","volume":"22","author":"urner","year":"2012","journal-title":"J Mach Learn Res Proc Track"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2004.00156.x"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00654"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977671"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/13\/3\/036018"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/11\/3\/036003"},{"key":"ref52","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"kingma","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","first-page":"593","article-title":"Multi-label learning with weak label","author":"sun","year":"2010","journal-title":"Proc 24th AAAI Conf Artif Intell"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23808-6_36"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.07.014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.139"},{"key":"ref13","first-page":"225","article-title":"TrueLabel + Confusions: A spectrum of probabilistic models in analyzing multiple ratings","author":"liu","year":"2012","journal-title":"Proc 29th Int Conf Mach Learn (ICML)"},{"key":"ref14","first-page":"161","author":"ambroise","year":"2000","journal-title":"EM Algorithm for Partially Known Labels"},{"key":"ref15","first-page":"435","article-title":"Active + semi-supervised learning = robust multi-view learning","author":"muslea","year":"2002","journal-title":"Proc 19th Int Conf Mach Learn ICML"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/11551188_45"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972818.52"},{"key":"ref18","first-page":"1504","article-title":"Learning from candidate labeling sets","author":"luo","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref19","first-page":"1279","article-title":"A convex relaxation for weakly supervised classifiers","author":"joulin","year":"2012","journal-title":"Proc 29th Int Conf Mach Learn (ICML)"},{"key":"ref4","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"arXiv 1312 6114"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref3"},{"key":"ref6","first-page":"2001","article-title":"Posterior regularization for structured latent variable models","volume":"11","author":"ganchev","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref5","first-page":"955","article-title":"Generalized expectation criteria for semi-supervised learning with weakly labeled data","volume":"11","author":"mann","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref8","first-page":"1501","article-title":"Learning from partial labels","volume":"12","author":"cour","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref7","first-page":"697","article-title":"A missing information principle: Theory and applications","volume":"1","author":"orchard","year":"1972","journal-title":"Proc 6th Berkeley Symp Math Stat Prob"},{"key":"ref49","first-page":"1864","article-title":"Dvae#: Discrete variational autoencoders with relaxed Boltzmann priors","author":"vahdat","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.52"},{"key":"ref46","article-title":"The EM algorithm extensions","author":"mclachlan","year":"2008","journal-title":"Wiley Series in Probability and Statistics"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"dempster","year":"1977","journal-title":"J Roy Statist Soc B Statist Methodol"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(94)90363-8"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/S0024-3795(99)00013-0"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1228"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0211-3"},{"key":"ref44","article-title":"Theory point estimation","author":"lehmann","year":"1998","journal-title":"Springer Texts in Statistics"},{"key":"ref43","article-title":"Bayesian representation learning with oracle constraints","author":"karaletsos","year":"2015","journal-title":"arXiv 1506 05011"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9505270\/09163155.pdf?arnumber=9163155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T16:07:47Z","timestamp":1723392467000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9163155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":54,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2020.3011671","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2021,8]]}}}