{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:20:47Z","timestamp":1772119247050,"version":"3.50.1"},"reference-count":32,"publisher":"Informa UK Limited","issue":"4","funder":[{"name":"2017YFB","award":["2017YFB0802803"],"award-info":[{"award-number":["2017YFB0802803"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Experimental & Theoretical Artificial Intelligence"],"published-print":{"date-parts":[[2025,5,19]]},"DOI":"10.1080\/0952813x.2023.2238738","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T04:22:29Z","timestamp":1691122949000},"page":"531-550","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":2,"title":["Explicit matrix gradient expression for residual network"],"prefix":"10.1080","volume":"37","author":[{"given":"Yudao","family":"Sun","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications","place":["Beijing, China"]}]},{"given":"Kangfeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications","place":["Beijing, China"]}]},{"given":"Juan","family":"Yin","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0535-6236","authenticated-orcid":false,"given":"Chunhua","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications","place":["Beijing, China"]}]},{"given":"Xinxin","family":"Niu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications","place":["Beijing, China"]}]}],"member":"301","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"issue":"4","key":"e_1_3_4_2_1","article-title":"Back propagation algorithm: The best algorithm among the multi-layer perceptron algorithm","volume":"9","author":"Alsamdi M.","year":"2009","unstructured":"Alsamdi, M., Omar, K., & Noah, S. (2009). Back propagation algorithm: The best algorithm among the multi-layer perceptron algorithm. International Journal of Computer Science & Network Security, 9(4). http:\/\/paper.ijcsns.org\/07_book\/200904\/20090451.pdf","journal-title":"International Journal of Computer Science & Network Security"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.23919\/MIPRO48935.2020.9245375"},{"key":"e_1_3_4_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101964"},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2654357"},{"key":"e_1_3_4_7_1","first-page":"9185","volume-title":"International conference on learning representations","author":"Goodfellow I. J.","year":"2018","unstructured":"Goodfellow, I. J., Shlens, J., & Szegedy, C. (2018). Explaining and harnessing adversarial examples. In International conference on learning representations (pp. 9185\u20139193). IEEE. https:\/\/dblp.org\/rec\/journals\/corr\/GoodfellowTour204.html"},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-020-00327-9"},{"key":"e_1_3_4_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SLT48900.2021.9383492"},{"key":"e_1_3_4_11_1","doi-asserted-by":"crossref","unstructured":"Kurakin A. Goodfellow I. J. & Bengio S. (2018). Adversarial examples in the physical world. Chapman and Hall\/crc. https:\/\/www.ijcai.org\/proceedings\/2021\/0694.pdf.","DOI":"10.1201\/9781351251389-8"},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.zemedi.2018.11.002"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13673-020-00250-w"},{"key":"e_1_3_4_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2020.106886"},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3074329"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"e_1_3_4_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01995-8"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3069470"},{"key":"e_1_3_4_19_1","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.011843"},{"key":"e_1_3_4_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/icABCD51485.2021.9519355"},{"key":"e_1_3_4_21_1","doi-asserted-by":"crossref","unstructured":"Rumelhart D. E. Hinton G. E. & Williams R. J. (1985). Learning internal representations by error propagation. Technical Report. https:\/\/apps.dtic.mil\/sti\/citations\/ADA164453.","DOI":"10.21236\/ADA164453"},{"key":"e_1_3_4_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_4_23_1","first-page":"3319","volume-title":"International conference on machine learning","author":"Sundararajan M.","year":"2017","unstructured":"Sundararajan, M., Taly, A., & Yan, Q. (2017). Axiomatic attribution for deep networks. In International conference on machine learning (pp. 3319\u20133328). August 6-11, 2017. PMLR. http:\/\/proceedings.mlr.press\/v70\/sundararajan17a"},{"key":"e_1_3_4_24_1","first-page":"110","volume-title":"2nd International conference on learning representations","author":"Szegedy C.","year":"2014","unstructured":"Szegedy, C., Zaremba, W., & Sutskever, I. (2014). Intriguing properties of neural networks. 2nd International conference on learning representations (pp. 110\u2013122). April 14-16, 2014. IEEE. https:\/\/openreview.net\/forum?id=kklr_MTHMRQjG"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3035992"},{"key":"e_1_3_4_26_1","first-page":"1","article-title":"Ensemble adversarial training: Attacks and defenses","volume":"5","author":"Tramer F.","year":"2017","unstructured":"Tramer, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017). Ensemble adversarial training: Attacks and defenses. Arxiv, 5, 1\u201322. https:\/\/arxiv.org\/abs\/1705.07204","journal-title":"Arxiv"},{"issue":"2","key":"e_1_3_4_27_1","first-page":"393","article-title":"Big data service architecture: A survey","volume":"21","author":"Wang J.","year":"2020","unstructured":"Wang, J., Yang, Y., Wang, T., Sherratt, R. S., & Zhang, J. (2020). Big data service architecture: A survey. Journal of Internet Technology, 21(2), 393\u2013405. https:\/\/jit.ndhu.edu.tw\/article\/view\/2261\/2274","journal-title":"Journal of Internet Technology"},{"issue":"4","key":"e_1_3_4_28_1","first-page":"1161","article-title":"Research on recurrent neural network based crack opening prediction of concrete dam","volume":"21","author":"Wang J.","year":"2020","unstructured":"Wang, J., Zou, Y., Lei, P., Sherratt, R. S., & Wang, L. (2020). Research on recurrent neural network based crack opening prediction of concrete dam. Journal of Internet Technology, 21(4), 1161\u20131169. https:\/\/jit.ndhu.edu.tw\/article\/view\/2378\/2394","journal-title":"Journal of Internet Technology"},{"key":"e_1_3_4_29_1","unstructured":"Wiyatno R. & Xu A. (2018). Maximal jacobian-based saliency map attack. arxiv. https:\/\/arxiv.org\/abs\/1808.07945"},{"key":"e_1_3_4_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/VCIP49819.2020.9301828"},{"key":"e_1_3_4_31_1","first-page":"32","article-title":"Are deep resnets provably better than linear predictors?","author":"Yun C.","year":"2019","unstructured":"Yun, C., Sra, S., & Jadbabaie, A. (2019). Are deep resnets provably better than linear predictors? Advances in Neural Information Processing Systems, 32. https:\/\/dl.acm.org\/doi\/10.5555\/3454287.3455692","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_4_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"e_1_3_4_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02572-0"}],"container-title":["Journal of Experimental & Theoretical Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/0952813X.2023.2238738","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T13:49:12Z","timestamp":1746107352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/0952813X.2023.2238738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,3]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,5,19]]}},"alternative-id":["10.1080\/0952813X.2023.2238738"],"URL":"https:\/\/doi.org\/10.1080\/0952813x.2023.2238738","relation":{},"ISSN":["0952-813X","1362-3079"],"issn-type":[{"value":"0952-813X","type":"print"},{"value":"1362-3079","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,3]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=teta20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=teta20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2022-04-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}