{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:32Z","timestamp":1740108092978,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T00:00:00Z","timestamp":1714176000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T00:00:00Z","timestamp":1714176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Ouyangjihong","award":["No.62006094, No.61876071,No.2019M661210"],"award-info":[{"award-number":["No.62006094, No.61876071,No.2019M661210"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00521-024-09783-y","type":"journal-article","created":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T11:01:46Z","timestamp":1714215706000},"page":"13731-13744","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Applying Kumaraswamy distribution on stick-breaking process: a Dirichlet neural topic model approach"],"prefix":"10.1007","volume":"36","author":[{"given":"Jihong","family":"Ouyang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyue","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6867-670X","authenticated-orcid":false,"given":"Yiming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"9783_CR1","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"issue":"12","key":"9783_CR2","doi-asserted-by":"publisher","first-page":"2928","DOI":"10.1109\/TKDE.2014.2313872","volume":"26","author":"X Cheng","year":"2014","unstructured":"Cheng X, Yan X, Lan Y, Guo J (2014) BTM: topic modeling over short texts. IEEE Trans Knowl Data Eng 26(12):2928\u20132941","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9783_CR3","doi-asserted-by":"crossref","unstructured":"Wang Y, Tong Y, Shi D (2020) Federated latent Dirichlet allocation: a local differential privacy based framework. In: The thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, the thirty-second innovative applications of artificial intelligence conference, IAAI 2020, the tenth AAAI symposium on educational advances in artificial intelligence, EAAI 2020, New York, NY, USA, February 7\u201312, 2020, pp 6283\u20136290","DOI":"10.1609\/aaai.v34i04.6096"},{"key":"9783_CR4","doi-asserted-by":"crossref","unstructured":"Zhou Q, Chen H, Zheng Y, Wang Z (2021) Evalda: Efficient evasion attacks towards latent Dirichlet allocation. In: Thirty-fifth AAAI conference on artificial intelligence, AAAI 2021, thirty-third conference on innovative applications of artificial intelligence, IAAI 2021, the eleventh symposium on educational advances in artificial intelligence, EAAI 2021, Virtual Event, February 2\u20139, 2021, pp 14602\u201314611","DOI":"10.1609\/aaai.v35i16.17716"},{"key":"9783_CR5","doi-asserted-by":"crossref","unstructured":"Cheevaprawatdomrong J, Schofield, A, Rutherford A (2022) More than words: collocation retokenization for latent Dirichlet allocation models. In: Muresan S, Nakov P, Villavicencio A (eds) Findings of the association for computational linguistics: ACL 2022, Dublin, Ireland, May 22\u201327, 2022, pp 2696\u20132704","DOI":"10.18653\/v1\/2022.findings-acl.212"},{"key":"9783_CR6","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: Bengio Y, LeCun Y (eds) International conference on learning representations, Banff, AB, Canada"},{"key":"9783_CR7","unstructured":"Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. In: Proceedings of the 31th international conference on machine learning, Beijing, China, pp 1278\u20131286"},{"key":"9783_CR8","doi-asserted-by":"crossref","unstructured":"Zhang L, Hu X, Wang B, Zhou D, Zhang Q, Cao Y (2022) Pre-training and fine-tuning neural topic model: a simple yet effective approach to incorporating external knowledge. In: Muresan S, Nakov P, Villavicencio A (eds) Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), ACL 2022, Dublin, Ireland, May 22\u201327, 2022, pp 5980\u20135989","DOI":"10.18653\/v1\/2022.acl-long.413"},{"key":"9783_CR9","doi-asserted-by":"crossref","unstructured":"Wang B, Zhang L, Zhou D, Cao Y, Ding J (2023) Neural topic modeling based on cycle adversarial training and contrastive learning. In: Rogers A, Boyd-Graber JL, Okazaki N (eds) Findings of the association for computational linguistics: ACL 2023, Toronto, Canada, July 9\u201314, 2023, pp 9720\u20139731","DOI":"10.18653\/v1\/2023.findings-acl.616"},{"key":"9783_CR10","doi-asserted-by":"crossref","unstructured":"Li R, Gonz\u00e1lez-Pizarro F, Xing L, Murray G, Carenini G (2023) Diversity-aware coherence loss for improving neural topic models. In: Rogers A, Boyd-Graber JL, Okazaki N (eds) Proceedings of the 61st annual meeting of the association for computational linguistics (volume 2: short papers), ACL 2023, Toronto, Canada, July 9\u201314, 2023, pp 1710\u20131722","DOI":"10.18653\/v1\/2023.acl-short.145"},{"key":"9783_CR11","unstructured":"Wallach HMA, Mimno D (2009) Rethinking LDA: why priors matter. In: Advances in neural information processing systems"},{"key":"9783_CR12","doi-asserted-by":"crossref","unstructured":"Nan F, Ding R, Nallapati R, Xiang B (2019) Topic modeling with Wasserstein autoencoders. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, Italy, pp 6345\u20136381","DOI":"10.18653\/v1\/P19-1640"},{"key":"9783_CR13","unstructured":"Srivastava A, Sutton C (2017) Autoencoding variational inference for topic models. In: International conference on learning representations. OpenReview.net, Toulon, France"},{"key":"9783_CR14","unstructured":"Figurnov M, Mohamed S, Mnih A (2018) Implicit reparameterization gradients. In: Bengio S, Wallach HM, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Neural information processing systems, pp 439\u2013450"},{"key":"9783_CR15","first-page":"131","volume":"20","author":"S Burkhardt","year":"2019","unstructured":"Burkhardt S, Kramer S (2019) Decoupling sparsity and smoothness in the Dirichlet variational autoencoder topic model. J Mach Learn Res 20:131\u2013113127","journal-title":"J Mach Learn Res"},{"key":"9783_CR16","series-title":"Proceedings of machine learning research","first-page":"489","volume-title":"Artificial Intelligence and Statistics","author":"CA Naesseth","year":"2017","unstructured":"Naesseth CA, Ruiz FJR, Linderman SW, Blei DM (2017) Reparameterization gradients through acceptance-rejection sampling algorithms. In: Singh A, Zhu XJ (eds) Artificial Intelligence and Statistics, vol 54. Proceedings of machine learning research. PMLR, Fort Lauderdale, pp 489\u2013498"},{"key":"9783_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107514","volume":"107","author":"W Joo","year":"2020","unstructured":"Joo W, Lee W, Park S, Moon I (2020) Dirichlet variational autoencoder. Pattern Recognit 107:107514","journal-title":"Pattern Recognit"},{"key":"9783_CR18","unstructured":"Zhang H, Chen B, Guo D, Zhou M (2018) WHAI: Weibull hybrid autoencoding inference for deep topic modeling. In: International conference on learning representations. OpenReview.net, Vancouver, BC, Canada"},{"key":"9783_CR19","unstructured":"Li Y, Wang C, Duan Z, Wang D, Chen B, An B, Zhou M (2022) Alleviating \u201cposterior collapse\u201d in deep topic models via policy gradient. In: NeurIPS"},{"key":"9783_CR20","unstructured":"Stirn A, Jebara T, Knowles DA (2019) A new distribution on the simplex with auto-encoding applications. In: Wallach HM, Larochelle H, Beygelzimer A, d\u2019Alch\u00e9-Buc F, Fox EB, Garnett R (eds) Neural information processing systems. Vancouver, BC, Canada, pp 13670\u201313680"},{"key":"9783_CR21","doi-asserted-by":"crossref","unstructured":"Isonuma M, Mori J, Bollegala D, Sakata I (2020) Tree-structured neural topic model. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 800\u2013806","DOI":"10.18653\/v1\/2020.acl-main.73"},{"key":"9783_CR22","unstructured":"Zhang Z, Zhang X, Rao Y (2022) Nonparametric forest-structured neural topic modeling. In: Proceedings of the 29th international conference on computational linguistics, Gyeongju, Republic of Korea, pp 2585\u20132597"},{"key":"9783_CR23","unstructured":"Hao X, Shafto P (2023) Coupled variational autoencoder. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J (eds) International conference on machine learning, ICML 2023, 23\u201329 July 2023, Honolulu, Hawaii, USA, vol 202, pp 12546\u201312555"},{"key":"9783_CR24","unstructured":"Estermann B, Wattenhofer R (2023) DAVA: disentangling adversarial variational autoencoder. In: The eleventh international conference on learning representations, ICLR 2023, Kigali, Rwanda, May 1\u20135, 2023"},{"key":"9783_CR25","unstructured":"Zhao Y, Linderman SW (2023) Revisiting structured variational autoencoders. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J (eds) International conference on machine learning, ICML 2023, 23\u201329 July 2023, Honolulu, Hawaii, USA. Proceedings of machine learning research, vol 202, pp 42046\u201342057"},{"key":"9783_CR26","unstructured":"Detkov A, Salameh M, Qharabagh MF, Zhang J, Luwei R, Jui S, Niu D (2023) Reparameterization through spatial gradient scaling. In: The eleventh international conference on learning representations, ICLR 2023, Kigali, Rwanda, May 1\u20135, 2023"},{"key":"9783_CR27","doi-asserted-by":"crossref","unstructured":"Li C, Qiu Q, Zhang Z, Guo J, Cheng X (2023) Learning adversarially robust sparse networks via weight reparameterization. In: Williams B, Chen Y, Neville J (eds) Thirty-seventh AAAI conference on artificial intelligence, AAAI 2023, thirty-fifth conference on innovative applications of artificial intelligence, IAAI 2023, thirteenth symposium on educational advances in artificial intelligence, EAAI 2023, Washington, DC, USA, February 7\u201314, 2023, pp 8527\u20138535","DOI":"10.1609\/aaai.v37i7.26027"},{"key":"9783_CR28","unstructured":"Miao Y, Yu L, Blunsom P (2016) Neural variational inference for text processing. In: Balcan M, Weinberger KQ (eds) International conference on machine learning, vol 48. JMLR.org, New York City, pp 1727\u20131736"},{"key":"9783_CR29","first-page":"2410","volume-title":"International conference on machine learning","author":"Y Miao","year":"2017","unstructured":"Miao Y, Grefenstette E, Blunsom P (2017) Discovering discrete latent topics with neural variational inference. In: Precup D, Teh YW (eds) International conference on machine learning, vol 70. PMLR, Sydney, pp 2410\u20132419"},{"key":"9783_CR30","doi-asserted-by":"crossref","unstructured":"Wang R, Hu X, Zhou D, He Y, Xiong Y, Ye C, Xu H (2020) Neural topic modeling with bidirectional adversarial training. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 340\u2013350","DOI":"10.18653\/v1\/2020.acl-main.32"},{"key":"9783_CR31","unstructured":"Kingma DP, Mohamed S, Rezende DJ, Welling M (2014) Semi-supervised learning with deep generative models. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Neural information processing systems, pp 3581\u20133589"},{"key":"9783_CR32","unstructured":"Hoffman MD, Johnson MJ (2016) ELBO surgery: yet another way to carve up the variational evidence lower bound. In: Workshop in advances in approximate Bayesian inference, NIPS, vol 1"},{"key":"9783_CR33","unstructured":"Nalisnick ET, Smyth P (2017) Stick-breaking variational autoencoders. In: International conference on learning representations. OpenReview.net, Toulon, France"},{"key":"9783_CR34","unstructured":"Sethuraman J (1994) A constructive definition of Dirichlet priors. Statistica Sinica 639\u2013650"},{"issue":"453","key":"9783_CR35","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1198\/016214501750332758","volume":"96","author":"H Ishwaran","year":"2001","unstructured":"Ishwaran H, James LF (2001) Gibbs sampling methods for stick-breaking priors. J Am Stat Assoc 96(453):161\u2013173","journal-title":"J Am Stat Assoc"},{"key":"9783_CR36","unstructured":"Frigyik B A, GMR Kapila A (2010) Introduction to the Dirichlet distribution and related processes. Department of Electrical Engineering, University of Washington, 6\u2013127"},{"issue":"1\u20132","key":"9783_CR37","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/0022-1694(80)90036-0","volume":"46","author":"P Kumaraswamy","year":"1980","unstructured":"Kumaraswamy P (1980) A generalized probability density function for double-bounded random processes. J Hydrol 46(1\u20132):79\u201388","journal-title":"J Hydrol"},{"key":"9783_CR38","unstructured":"Hinz T, Wermter S (2018) Inferencing based on unsupervised learning of disentangled representations. In: European symposium on artificial neural networks"},{"key":"9783_CR39","doi-asserted-by":"crossref","unstructured":"Hoeffding W (1992) A class of statistics with asymptotically normal distribution. In: Breakthroughs in statistics. Springer, New York, pp 308\u2013334","DOI":"10.1007\/978-1-4612-0919-5_20"},{"key":"9783_CR40","unstructured":"Miao Y, Grefenstette E, Blunsom P (2017) Discovering discrete latent topics with neural variational inference. In: International conference on machine learning. PMLR, pp 2410\u20132419"},{"key":"9783_CR41","unstructured":"Nassar J, Linderman S, Bugallo M, Park IM (2019) Tree-structured recurrent switching linear dynamical systems for multi-scale modeling. In: International conference on learning representations"},{"issue":"8","key":"9783_CR42","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"127","key":"9783_CR43","first-page":"1","volume":"18","author":"V Perrone","year":"2017","unstructured":"Perrone V, Jenkins PA, Span\u00f2 D, Teh YW (2017) Poisson random fields for dynamic feature models. J Mach Learn Res 18(127):1\u201345","journal-title":"J Mach Learn Res"},{"key":"9783_CR44","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Bengio Y, LeCun Y (eds) ICLR"},{"key":"9783_CR45","first-page":"10","volume-title":"Computational natural language learning, CoNLL","author":"SR Bowman","year":"2016","unstructured":"Bowman SR, Vilnis L, Vinyals O, Dai AM, J\u00f3zefowicz R, Bengio S (2016) Generating sentences from a continuous space. In: Goldberg Y, Riezler S (eds) Computational natural language learning, CoNLL. ACL, Berlin, pp 10\u201321"},{"key":"9783_CR46","first-page":"27","volume-title":"Conference on information and knowledge management","author":"H Bai","year":"2018","unstructured":"Bai H, Chen Z, Lyu MR, King I, Xu Z (2018) Neural relational topic models for scientific article analysis. In: Cuzzocrea A, Allan J, Paton NW, Srivastava D, Agrawal R, Broder AZ, Zaki MJ, Candan KS, Labrinidis A, Schuster A, Wang H (eds) Conference on information and knowledge management. ACM, Torino, pp 27\u201336"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09783-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09783-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09783-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T18:11:26Z","timestamp":1723227086000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09783-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,27]]},"references-count":46,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["9783"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09783-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,4,27]]},"assertion":[{"value":"27 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research does not involvehuman participants nor animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Consent to submit this manuscript has been received tacitly from the author\u2019s institution, Jilin University.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors are consent for publication.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}