{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:56:42Z","timestamp":1776887802597,"version":"3.51.2"},"reference-count":81,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"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":["62102192"],"award-info":[{"award-number":["62102192"]}],"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":["62072252"],"award-info":[{"award-number":["62072252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fellowship of China Postdoctoral Science Foundation","award":["2022M710071"],"award-info":[{"award-number":["2022M710071"]}]},{"name":"Innovation and Entrepreneurship Program of Jiangsu Province","award":["JSSCBS20210530"],"award-info":[{"award-number":["JSSCBS20210530"]}]},{"name":"Introduction of Talent Research and Research Fund of Nanjing University of Posts and Telecommunications","award":["NY220132"],"award-info":[{"award-number":["NY220132"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1109\/tnnls.2024.3357698","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T18:50:04Z","timestamp":1707331804000},"page":"2523-2537","source":"Crossref","is-referenced-by-count":7,"title":["MIT: Mutual Information Topic Model for Diverse Topic Extraction"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9350-3667","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7702-9387","authenticated-orcid":false,"given":"Deyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4392-3599","authenticated-orcid":false,"given":"Haiping","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and the Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4404-952X","authenticated-orcid":false,"given":"Yongquan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1645953.1646003"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9526"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3094987"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3054422"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2018.2872997"},{"key":"ref6","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref8","first-page":"1727","article-title":"Neural variational inference for text processing","volume-title":"Proc. 33rd Int. Conf. Mach. Learn. (ICML)","volume":"48","author":"Miao"},{"key":"ref9","first-page":"2410","article-title":"Discovering discrete latent topics with neural variational inference","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Miao"},{"key":"ref10","first-page":"1","article-title":"Autoencoding variational inference for topic models","volume-title":"Proc. 5th Int. Conf. Learn. Represent. (ICLR)","author":"Srivastava"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1189"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00325"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102098"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.32"},{"key":"ref15","first-page":"9403","article-title":"Don\u2019t blame the elbo! A linear VAE perspective on posterior collapse","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Annu. Conf. Neural Inf. Process. Syst.","author":"Lucas"},{"key":"ref16","first-page":"3308","article-title":"VeeGAN: Reducing mode collapse in GANs using implicit variational learning","volume-title":"Proc. NIPS","author":"Srivastava"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.066138"},{"key":"ref18","first-page":"1","article-title":"On mutual information maximization for representation learning","volume-title":"Proc. 8th Int. Conf. Learn. Represent. (ICLR)","author":"Tschannen"},{"key":"ref19","first-page":"1","article-title":"Learning deep representations by mutual information estimation and maximization","volume-title":"Proc. 7th Int. Conf. Learn. Represent. (ICLR)","author":"Hjelm"},{"key":"ref20","first-page":"1","article-title":"Deep graph infomax","volume-title":"Proc. 7th Int. Conf. Learn. Represent. (ICLR)","author":"Velickovic"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1006\/jmva.1994.1033"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3390\/e19020047"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jspi.2013.03.018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0069"},{"issue":"25","key":"ref25","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref26","first-page":"1973","article-title":"Rethinking LDA: Why priors matter","volume-title":"Proc. Adv. Neural Inf. Process. Syst. 23rd Annu. Conf. Neural Inf. Process. Syst.","author":"Wallach"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685324"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-09070-2"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498518"},{"key":"ref30","first-page":"11974","article-title":"Contrastive learning for neural topic model","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Annu. Conf. Neural Inf. Process. Syst.","author":"Nguyen"},{"key":"ref31","article-title":"Bertopic: Neural topic modeling with a class-based TF-IDF procedure","author":"Grootendorst","year":"2022","journal-title":"arXiv:2203.05794"},{"key":"ref32","first-page":"128","article-title":"Improving contextualized topic models with negative sampling","volume-title":"Proc. 19th Int. Conf. Natural Lang. Process. (ICON)","author":"Adhya"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1027"},{"key":"ref34","first-page":"1","article-title":"Neural topic model via optimal transport","volume-title":"Proc. 9th Int. Conf. Learn. Represent. (ICLR)","author":"Zhao"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2022.100344"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i11.26612"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICKG55886.2022.00016"},{"issue":"1","key":"ref38","first-page":"3760","article-title":"Distribution-matching embedding for visual domain adaptation","volume":"17","author":"Baktashmotlagh","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref39","volume-title":"Reproducing Kernel Hilbert Spaces in Probability and Statistics","author":"Berlinet","year":"2011"},{"key":"ref40","first-page":"301","article-title":"The kernel trick for distances","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Papers Neural Inf. Process. Syst. (NIPS)","author":"Sch\u00f6lkopf"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01375"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3155924"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413986"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3487194"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103913"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12380"},{"key":"ref47","article-title":"Calibrated adversarial refinement for stochastic semantic segmentation","author":"Kassapis","year":"2020","journal-title":"arXiv:2006.13144"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01187"},{"issue":"73","key":"ref49","first-page":"1","article-title":"A kernel two-sample test for functional data","volume":"23","author":"Wynne","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01619"},{"key":"ref51","article-title":"MINE: Mutual information neural estimation","author":"Belghazi","year":"2018","journal-title":"arXiv:1801.04062"},{"key":"ref52","first-page":"271","article-title":"F-GAN: Training generative neural samplers using variational divergence minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Annu. Conf. Neural Inf. Process. Syst.","author":"Nowozin"},{"key":"ref53","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref54","first-page":"2445","article-title":"Information maximization for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Annu. Conf. Neural Inf. Process. Syst.","author":"Boudiaf"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00881"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00887"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01437"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00735"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01771"},{"key":"ref60","first-page":"7476","article-title":"Adversarial mutual information for text generation","volume-title":"Proc. 37th Int. Conf. Mach. Learn. (ICML)","volume":"119","author":"Pan"},{"key":"ref61","first-page":"1928","article-title":"Contrastive latent variable models for neural text generation","volume-title":"Proc. 38th Conf. Uncertainty Artif. Intell.","author":"Teng"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3160509"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.124"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108488"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3086570"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3135375"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3100583"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1375"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767749"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.05957"},{"key":"ref71","article-title":"Statistical inference for generative models with maximum mean discrepancy","author":"Briol","year":"2019","journal-title":"arXiv:1906.05944"},{"key":"ref72","first-page":"2172","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Chen"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103259"},{"key":"ref74","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-377-6.50048-7"},{"key":"ref76","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2017.12"},{"key":"ref79","first-page":"288","article-title":"Reading tea leaves: How humans interpret topic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst. 23rd Annu. Conf. Neural Inf. Process. Syst.","author":"Chang"},{"key":"ref80","first-page":"100","article-title":"Automatic evaluation of topic coherence","volume-title":"Proc. Hum. Lang. Technol., Annu. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Newman"},{"key":"ref81","first-page":"13","article-title":"Evaluating topic coherence using distributional semantics","volume-title":"Proc. 10th Int. Conf. Comput. Semantics (IWCS)","author":"Aletras"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10877690\/10423818.pdf?arnumber=10423818","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T07:11:35Z","timestamp":1738912295000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10423818\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":81,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3357698","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2]]}}}