{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T07:52:42Z","timestamp":1764402762010,"version":"3.37.3"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T00:00:00Z","timestamp":1517443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology Taiwan","doi-asserted-by":"publisher","award":["MOST 105-2221-E-009-137-MY2"],"award-info":[{"award-number":["MOST 105-2221-E-009-137-MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2018,2,1]]},"DOI":"10.1109\/tpami.2017.2677439","type":"journal-article","created":{"date-parts":[[2017,3,2]],"date-time":"2017-03-02T19:37:18Z","timestamp":1488483438000},"page":"318-331","source":"Crossref","is-referenced-by-count":41,"title":["Deep Unfolding for Topic Models"],"prefix":"10.1109","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3466-8941","authenticated-orcid":false,"given":"Jen-Tzung","family":"Chien","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao-Hsi","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/160688.160758"},{"key":"ref38","first-page":"496","article-title":"Improving topic coherence with regularized topic models","author":"newman","year":"2011","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7471649"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7177933"},{"key":"ref31","first-page":"354","article-title":"A variational\n analysis of stochastic gradient algorithms","author":"mandt","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref30","first-page":"1070","article-title":"Early\n stopping as nonparametric variational inference","author":"duvenaud","year":"2016","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref37","first-page":"440","article-title":"Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification","author":"blitzer","year":"2007","journal-title":"Proc 32nd Ann Meeting Assoc for Computational Linguistics"},{"key":"ref36","first-page":"153","article-title":"Greedy layer-wise training of deep\n networks","author":"bengio","year":"2006","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1006\/inco.1996.2612"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472083"},{"key":"ref10","first-page":"897","article-title":"DiscLDA:\n Discriminative learning for dimensionality reduction and classification","author":"lacoste-julien","year":"2009","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref11","first-page":"2237","article-title":"MedLDA: Maximum margin\n supervised topic models","volume":"13","author":"zhu","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"5228","DOI":"10.1073\/pnas.0307752101","article-title":"Finding scientific topics","volume":"101","author":"griffiths","year":"2004","journal-title":"Proc Natl Acad Sci United States America"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107295360"},{"key":"ref15","first-page":"725","article-title":"Empirical\n risk minimization of graphical model parameters given approximate inference, decoding, and model structure","author":"stoyanov","year":"2011","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref16","first-page":"530","article-title":"Learning stochastic feedforward neural networks","author":"tang","year":"2013","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref17","first-page":"226","article-title":"Deep generative\n stochastic networks trainable by backprop","author":"bengio","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","first-page":"1782","article-title":"Efficient gradient-based inference through transformations between Bayes nets and neural nets","author":"kingma","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","first-page":"1791","article-title":"Neural variational inference and learning in belief networks","author":"mnih","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"article-title":"Deep\n unfolding: Model-based inspiration of novel deep architectures","year":"2014","author":"hershey","key":"ref28"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2007.909452"},{"key":"ref27","first-page":"1765","article-title":"End-to-end learning of LDA by mirror-descent\n back propagation over a deep architecture","author":"chen","year":"2015","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2143405"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2414658"},{"key":"ref29","article-title":"Importance weighted autoencoders","author":"burda","year":"2016","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref5","first-page":"1689","article-title":"Latent Dirichlet learning for document summarization","author":"chang","year":"2009","journal-title":"Proc Int Conf Acoust Speech Signal Process"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2015.2428632"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2010.2050717"},{"key":"ref2","first-page":"1903","article-title":"Simultaneous\n image classification and annotation","author":"wang","year":"2009","journal-title":"Proc IEEE Conf Comput Vis Pattern Recog"},{"key":"ref9","first-page":"121","article-title":"Supervised topic models","author":"mcauliffe","year":"2008","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref1","first-page":"993","article-title":"Latent\n Dirichlet allocation","volume":"3","author":"blei","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref20","first-page":"2980","article-title":"A recurrent latent variable model for sequential data","author":"chung","year":"2015","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref22","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref21","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2476802"},{"key":"ref23","first-page":"2708","article-title":"A neural autoregressive topic model","author":"larochelle","year":"2012","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref26","first-page":"1823","article-title":"Scalable deep poisson factor analysis for topic modeling","author":"gan","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref25","first-page":"762","article-title":"Deep exponential families","author":"ranganath","year":"2015","journal-title":"Proc Int Conf Artif Intell Statist"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/8249508\/07869412.pdf?arnumber=7869412","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:20:00Z","timestamp":1643145600000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7869412\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,1]]},"references-count":39,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2017.2677439","relation":{},"ISSN":["0162-8828","2160-9292"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"}],"subject":[],"published":{"date-parts":[[2018,2,1]]}}}