{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:09:58Z","timestamp":1776784198379,"version":"3.51.2"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["No.YDZJ202201ZYTS519"],"award-info":[{"award-number":["No.YDZJ202201ZYTS519"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["No.YDZJ202201ZYTS585"],"award-info":[{"award-number":["No.YDZJ202201ZYTS585"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62176051"],"award-info":[{"award-number":["No.62176051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10489-024-05303-6","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T06:02:33Z","timestamp":1708063353000},"page":"2361-2378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ABNGrad: adaptive step size gradient descent for optimizing neural networks"],"prefix":"10.1007","volume":"54","author":[{"given":"Wenhan","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Yuqing","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6773-1444","authenticated-orcid":false,"given":"Zhixia","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Dongpo","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Linhua","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"5303_CR1","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human language technologies, (NAACL), Minneapolis, Minnesota, June, vol 1, pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"5303_CR2","doi-asserted-by":"publisher","unstructured":"Dai Z, Yang Z, Yang Y, Carbonell J, Le Q, Salakhutdinov R (2019) Transformer-XL: attentive language models beyond a fixed-length context, 2978\u20132988. https:\/\/doi.org\/10.18653\/v1\/P19-1285","DOI":"10.18653\/v1\/P19-1285"},{"key":"5303_CR3","doi-asserted-by":"publisher","unstructured":"Zhang T, Chen S, Wulamu A, Guo X, Li Q, Zheng H (2023) Transg-net: transformer and graph neural network based multi-modal data fusion network for molecular properties prediction, 16077\u201316088. https:\/\/doi.org\/10.1007\/s10489-022-04351-0","DOI":"10.1007\/s10489-022-04351-0"},{"key":"5303_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2023.103498","volume":"158","author":"E Kononov","year":"2023","unstructured":"Kononov E, Tashkinov M, Silberschmidt VV (2023) Reconstruction of 3d random media from 2d images: generative adversarial learning approach. Comput Aided Des 158:103498. https:\/\/doi.org\/10.1016\/j.cad.2023.103498","journal-title":"Comput Aided Des"},{"issue":"9","key":"5303_CR5","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1038\/s41593-018-0209-y","volume":"21","author":"A Mathis","year":"2018","unstructured":"Mathis A, Mamidanna P, Cury KM et al (2018) Deeplabcut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci 21(9):1281\u20131289. https:\/\/doi.org\/10.1038\/s41593-018-0209-y","journal-title":"Nat Neurosci"},{"key":"5303_CR6","doi-asserted-by":"publisher","first-page":"5607","DOI":"10.1007\/s10489-022-03779-8","volume":"53","author":"B Huang","year":"2022","unstructured":"Huang B, Zhang S, Huang J, Yu Y, Shi Z, Xiong Y (2022) Knowledge distilled pre-training model for vision-language-navigation. Appl Intell 53:5607\u20135619. https:\/\/doi.org\/10.1007\/s10489-022-03779-8","journal-title":"Appl Intell"},{"key":"5303_CR7","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s40860-021-00140-7","volume":"8","author":"A Kumar","year":"2022","unstructured":"Kumar A, Aggarwal RK (2022) An exploration of semi-supervised and language-adversarial transfer learning using hybrid acoustic model for hindi speech recognition. J Reliab Intell Environ 8:117\u2013132. https:\/\/doi.org\/10.1007\/s40860-021-00140-7","journal-title":"J Reliab Intell Environ"},{"key":"5303_CR8","doi-asserted-by":"publisher","unstructured":"Hu L, Fu C, Ren Zea (2023) Sselm-neg: spherical search-based extreme learning machine for drug-target interaction prediction. BMC Bioinformatics 24(38):1471\u20132105. https:\/\/doi.org\/10.1186\/s12859-023-05153-y","DOI":"10.1186\/s12859-023-05153-y"},{"issue":"6","key":"5303_CR9","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.1021\/acs.jcim.0c00073","volume":"60","author":"Y Xu","year":"2020","unstructured":"Xu Y, Verma D, Sheridan RP et al (2020) Deep dive into machine learning models for protein engineering. J Chem Inf Model 60(6):2773\u20132790. https:\/\/doi.org\/10.1021\/acs.jcim.0c00073","journal-title":"J Chem Inf Model"},{"key":"5303_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101822","volume":"104","author":"J Waring","year":"2020","unstructured":"Waring J, Lindvall C, Umeton R (2020) Automated machine learning: review of the state-of-the-art and opportunities for healthcare. Artif Intell Med 104:101822. https:\/\/doi.org\/10.1016\/j.artmed.2020.101822","journal-title":"Artif Intell Med"},{"issue":"1","key":"5303_CR11","doi-asserted-by":"publisher","first-page":"26","DOI":"10.11989\/JEST.1674-862X.80904120","volume":"17","author":"J Wu","year":"2019","unstructured":"Wu J, Chen X-Y, Zhang H, Xiong L-D, Lei H, Deng S-H (2019) Hyperparameter optimization for machine learning models based on bayesian optimizationb. Journal of Electronic Science and Technology 17(1):26\u201340. https:\/\/doi.org\/10.11989\/JEST.1674-862X.80904120","journal-title":"Journal of Electronic Science and Technology"},{"issue":"4","key":"5303_CR12","doi-asserted-by":"publisher","first-page":"3335","DOI":"10.1007\/s00366-021-01444-1","volume":"38","author":"A Abbaszadeh Shahri","year":"2022","unstructured":"Abbaszadeh Shahri A, Pashamohammadi F, Asheghi R, Abbaszadeh Shahri H (2022) Automated intelligent hybrid computing schemes to predict blasting induced ground vibration. Engineering with Computers 38(4):3335\u20133349. https:\/\/doi.org\/10.1007\/s00366-021-01444-1","journal-title":"Engineering with Computers"},{"key":"5303_CR13","doi-asserted-by":"publisher","first-page":"3939","DOI":"10.1007\/s10489-021-02224-6","volume":"52","author":"W Yuan","year":"2022","unstructured":"Yuan W, Hu F, Lu L (2022) A new non-adaptive optimization method: stochastic gradient descent with momentum and difference. Appl Intell 52:3939\u20133953. https:\/\/doi.org\/10.1007\/s10489-021-02224-6","journal-title":"Appl Intell"},{"key":"5303_CR14","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization, vol 12, pp 2121\u20132159. https:\/\/www.jmlr.org\/papers\/volume12\/duchi11a\/duchi11a.pdf"},{"key":"5303_CR15","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1007\/s10489-020-01892-0","volume":"51","author":"R Yedida","year":"2021","unstructured":"Yedida R, Aha S, Prashanth T (2021) Lipschitzlr: using theoretically computed adaptive learning rates for fast convergence. Appl Intell 51:1460\u20131478. https:\/\/doi.org\/10.1007\/s10489-020-01892-0","journal-title":"Appl Intell"},{"key":"5303_CR16","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: 3rd International conference on learning representations, ICLR, San Diego, CA, USA, May, San Diego, CA, USA. http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"5303_CR17","unstructured":"Reddi SJ, Kale S, Kumar S (2018) On the convergence of adam and beyond. In: 6th International conference on learning representations, ICLR, Vancouver, BC, Canada, April, Vancouver, BC, Canada. https:\/\/openreview.net\/forum?id=ryQu7f-RZ"},{"key":"5303_CR18","unstructured":"Luo L, Xiong Y, Liu Y, Sun X (2019) Adaptive gradient methods with dynamic bound of learning rate. In: 7th International conference on learning representations, ICLR, New Orleans, LA, USA, May 6-9, New Orleans, LA, USA. https:\/\/openreview.net\/forum?id=Bkg3g2R9FX"},{"key":"5303_CR19","unstructured":"Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. In: 7th International conference on learning representations, ICLR, New Orleans, LA, USA, May 6-9. https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"5303_CR20","unstructured":"Liu L, Jiang H, He P, Chen W, Liu X, Gao J, Han J (2020) On the variance of the adaptive learning rate and beyond. In: International conference on learning representations, Ethiopia, July. https:\/\/openreview.net\/forum?id=rkgz2aEKDr"},{"key":"5303_CR21","unstructured":"Zinkevich M (2003) Online convex programming and generalized infinitesimal gradient ascent. In: Proceedings of the 20th international conference on machine learning, ICML, Washington, DC, USA, August 21-24, pp 928\u2013936. https:\/\/icml.cc\/Conferences\/2010\/papers\/473.pdf"},{"key":"5303_CR22","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s10994-007-5016-8","volume":"69","author":"E Hazan","year":"2007","unstructured":"Hazan E, Agarwal A, Kale S (2007) Logarithmic regret algorithms for online convex optimization. Mach Learn 69:169\u2013192. https:\/\/doi.org\/10.1007\/s10994-007-5016-8","journal-title":"Mach Learn"},{"key":"5303_CR23","doi-asserted-by":"publisher","unstructured":"Zeng K, Liu J, Jiang Z, Xu D (2022) A decreasing scaling transition scheme from adam to sgd. Adv Theory Simul 5(7). https:\/\/doi.org\/10.1002\/adts.202100599","DOI":"10.1002\/adts.202100599"},{"issue":"4","key":"5303_CR24","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1609\/aimag.v40i4.4812","volume":"40","author":"B Jalaian","year":"2019","unstructured":"Jalaian B, Lee M, Russell S (2019) Uncertain context: uncertainty quantification in machine learning. AI Mag 40(4):40\u201349. https:\/\/doi.org\/10.1609\/aimag.v40i4.4812","journal-title":"AI Mag"},{"key":"5303_CR25","doi-asserted-by":"publisher","unstructured":"Wu X, Wagner P, Huber MF (2023) In: Shajek A, Hartmann EA (eds) Quantification of uncertainties in neural networks. Springer, Cham, pp 276\u2013287. https:\/\/doi.org\/10.1007\/978-3-031-26490-0_16","DOI":"10.1007\/978-3-031-26490-0_16"},{"key":"5303_CR26","unstructured":"Zhuang J, Tang T, Ding Y, Tatikonda SC, Dvornek N, Papademetris X, Duncan J (2020) Adabelief optimizer: adapting stepsizes by the belief in observed gradients. In: Advances in neural information processing systems, vol 33, pp 18795\u201318806. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/d9d4f495e875a2e075a1a4a6e1b9770f-Paper.pdf"},{"issue":"3","key":"5303_CR27","doi-asserted-by":"publisher","first-page":"2458","DOI":"10.1002\/num.22726","volume":"37","author":"H Ko\u00e7ak","year":"2021","unstructured":"Ko\u00e7ak H (2021) A combined meshfree exponential Rosenbrock integrator for the third-order dispersive partial differential equations. Numer Methods Partial Differ Equ 37(3):2458\u20132468. https:\/\/doi.org\/10.1002\/num.22726","journal-title":"Numer Methods Partial Differ Equ"},{"key":"5303_CR28","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/978-3-030-73280-6_42","volume":"12672","author":"U Oza","year":"2021","unstructured":"Oza U, Patel S, Kumar P (2021) Noveme - color space net for image classification. Intell Inf Database Syst 12672:531\u2013543. https:\/\/doi.org\/10.1007\/978-3-030-73280-6_42","journal-title":"Intell Inf Database Syst"},{"key":"5303_CR29","doi-asserted-by":"publisher","unstructured":"Branco A, Carvalheiro C, Costa F, Castro S, Silva J, Martins C, Ramos J (2014) Deepbankpt and companion portuguese treebanks in a multilingual collection of treebanks aligned with the penn treebank. Computational Processing of the Portuguese Language 207\u2013213. https:\/\/doi.org\/10.1007\/978-3-319-09761-9_23","DOI":"10.1007\/978-3-319-09761-9_23"},{"key":"5303_CR30","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","volume":"54","author":"X Ma","year":"2015","unstructured":"Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies 54:187\u2013197. https:\/\/doi.org\/10.1016\/j.trc.2015.03.014","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"5303_CR31","unstructured":"McMahan HB, Streeter MJ (2010) Adaptive bound optimization for online convex optimization, pp 224\u2013256. https:\/\/www.learningtheory.org\/colt2010\/conference-website\/papers\/104mcmahan.pdf"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05303-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05303-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05303-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:43:29Z","timestamp":1710362609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05303-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5303"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05303-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"30 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no confict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}