{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:31:20Z","timestamp":1773246680019,"version":"3.50.1"},"reference-count":51,"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":["92167106"],"award-info":[{"award-number":["92167106"]}],"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":["62473103"],"award-info":[{"award-number":["62473103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science and Technology Major Project of China","award":["2022ZD0120001"],"award-info":[{"award-number":["2022ZD0120001"]}]},{"name":"Jiangsu Provincial Scientific Research Center of Applied Mathematics","award":["BK20233002"],"award-info":[{"award-number":["BK20233002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Syst. Man Cybern, Syst."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1109\/tsmc.2024.3493071","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T19:15:38Z","timestamp":1732216538000},"page":"1043-1055","source":"Crossref","is-referenced-by-count":8,"title":["Improving Data-Driven Inferential Sensor Modeling by Industrial Knowledge: A Bayesian Perspective"],"prefix":"10.1109","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5785-0741","authenticated-orcid":false,"given":"Zhichao","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-487X","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4098-6479","authenticated-orcid":false,"given":"Zhihuan","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2071-4380","authenticated-orcid":false,"given":"Zhiqiang","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southeast University, Nanjing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3053128"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2024.3452241"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3051054"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3010331"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.9b02513"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3073702"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122412"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3059002"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615487"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.3c02383"},{"key":"ref12","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"key":"ref13","article-title":"JAX: Autograd and XLA","author":"Bradbury","year":"2021","journal-title":"Astrophys. Source Code Library"},{"key":"ref14","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2022.3215448"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3277978"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2023.01.010"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123078"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719383"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2023.09.061"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3319606"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3127204"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671903"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2022.09.005"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2022.3198833"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2958184"},{"key":"ref27","first-page":"16362","article-title":"Bayesian attention modules","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst.","volume":"33","author":"Fan"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1017\/9781108924184"},{"key":"ref29","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref30","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. 31st Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref31","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Veli\u010dkovi\u0107"},{"key":"ref32","first-page":"1","article-title":"Variational dropout and the local reparameterization trick","volume-title":"Proc. 28th Conf. Neural Inf. Process. Syst.","volume":"28","author":"Kingma"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-71050-9","volume-title":"Optimal Transport: Old and New","volume":"338","author":"Villani","year":"2009"},{"key":"ref34","first-page":"1","article-title":"Optimal transport for treatment effect estimation","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","volume":"36","author":"Wang"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s13373-017-0101-1"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3435466"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2023.3302838"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1057\/jors.1984.92"},{"key":"ref39","first-page":"1","article-title":"Sampling with mirrored stein operators","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Shi"},{"key":"ref40","first-page":"9244","article-title":"GNNExplainer: Generating explanations for graph neural networks","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","volume":"32","author":"Ying"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1017\/9781108186735"},{"key":"ref42","first-page":"1","article-title":"GAD-PVI: A general accelerated dynamic-weight particle-based variational inference framework","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"37","author":"Wang"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3233789"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615167"},{"key":"ref45","first-page":"24226","article-title":"Flowformer: Linearizing transformers with conservation flows","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref46","first-page":"1","article-title":"iTransformer: Inverted transformers are effective for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref47","first-page":"10183","article-title":"Synthesizer: Rethinking self-attention for transformer models","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Tay"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3240114"},{"key":"ref49","first-page":"1","article-title":"How attentive are graph attention networks?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Brody"},{"key":"ref50","first-page":"11576","article-title":"Variance reduction and quasi-Newton for particle-based variational inference","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671915"}],"container-title":["IEEE Transactions on Systems, Man, and Cybernetics: Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6221021\/10843062\/10759849.pdf?arnumber=10759849","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T18:57:16Z","timestamp":1737399436000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10759849\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":51,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tsmc.2024.3493071","relation":{},"ISSN":["2168-2216","2168-2232"],"issn-type":[{"value":"2168-2216","type":"print"},{"value":"2168-2232","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2]]}}}