{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:57:44Z","timestamp":1774079864260,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s13042-025-02819-2","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T06:11:53Z","timestamp":1774073513000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tensortrim: dynamic tensor-train decomposition for efficient neural network compression"],"prefix":"10.1007","volume":"17","author":[{"given":"Shiyi","family":"Luo","sequence":"first","affiliation":[]},{"given":"Mingshuo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yunduan","family":"Lou","sequence":"additional","affiliation":[]},{"given":"Pu","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yifeng","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Shangping","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"key":"2819_CR1","doi-asserted-by":"publisher","unstructured":"Ma X, Han K, Yang Y, DeMara RF, Bai Y (2022) Hardware oriented strip-wise optimization (hoso) framework for efficient deep neural network. 2022 IEEE 35th international system-on-chip conference (SOCC). pp 1\u20136. https:\/\/doi.org\/10.1109\/SOCC56010.2022.9908125","DOI":"10.1109\/SOCC56010.2022.9908125"},{"key":"2819_CR2","doi-asserted-by":"publisher","unstructured":"Ma X, Tang J, Bai Y (2023) Locality-sensing fast neural network (lfnn): An efficient neural network acceleration framework via locality sensing for real-time videos queries. 2023 24th international symposium on quality electronic design (ISQED). pp 1\u20138. https:\/\/doi.org\/10.1109\/ISQED57927.2023.10129395","DOI":"10.1109\/ISQED57927.2023.10129395"},{"key":"2819_CR3","doi-asserted-by":"crossref","unstructured":"Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: Imagenet classification using binary convolutional neural networks. Computer vision\u2013ECCV 2016: 14th european conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV, pp. 525\u2013542 . Springer","DOI":"10.1007\/978-3-319-46493-0_32"},{"issue":"0","key":"2819_CR4","doi-asserted-by":"publisher","first-page":"10068","DOI":"10.1016\/j.iot.2023.100680","volume":"22","author":"M Liu","year":"2023","unstructured":"Liu M, Yin M, Han K, DeMara RF, Yuan B, Bai Y (2023) Algorithm and hardware co-design co-optimization framework for lstm accelerator using quantized fully decomposed tensor train. Internet of Things 22(0):10068. https:\/\/doi.org\/10.1016\/j.iot.2023.100680","journal-title":"Internet of Things"},{"issue":"2","key":"2819_CR5","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1109\/TAC.1980.1102314","volume":"25","author":"V Klema","year":"1980","unstructured":"Klema V, Laub A (1980) The singular value decomposition: its computation and some applications. IEEE Trans Autom Control 25(2):164\u2013176","journal-title":"IEEE Trans Autom Control"},{"issue":"3","key":"2819_CR6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF02289464","volume":"31","author":"LR Tucker","year":"1966","unstructured":"Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika 31(3):279\u2013311","journal-title":"Psychometrika"},{"issue":"10","key":"2819_CR7","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.3390\/mi13101738","volume":"13","author":"M Liu","year":"2022","unstructured":"Liu M, Luo S, Han K, DeMara RF, Bai Y (2022) Autonomous binarized focal loss enhanced model compression design using tensor train decomposition. Micromachines 13(10):1738","journal-title":"Micromachines"},{"key":"2819_CR8","doi-asserted-by":"publisher","unstructured":"Liu M, Luo S, Han K, Yuan B, DeMara RF, Bai Y (2021) An efficient real-time object detection framework on resource-constricted hardware devices via software and hardware co-design. 2021 IEEE 32nd international conference on application-specific systems, architectures and processors (ASAP). pp 77\u201384. https:\/\/doi.org\/10.1109\/ASAP52443.2021.00020","DOI":"10.1109\/ASAP52443.2021.00020"},{"key":"2819_CR9","unstructured":"Han S, Mao H, Dally WJ (2015) Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv:1510.00149"},{"key":"2819_CR10","doi-asserted-by":"crossref","unstructured":"Zhang T, Ye S, Zhang K, Tang J, Wen W, Fardad M, Wang Y (2018) A systematic dnn weight pruning framework using alternating direction method of multipliers. Proceedings of the european conference on computer vision (ECCV). pp 184\u2013199","DOI":"10.1007\/978-3-030-01237-3_12"},{"key":"2819_CR11","doi-asserted-by":"crossref","unstructured":"Luo J-H, Wu J, Lin W (2017) Thinet: a filter level pruning method for deep neural network compression. Proceedings of the IEEE international conference on computer vision. pp 5058\u20135066","DOI":"10.1109\/ICCV.2017.541"},{"key":"2819_CR12","doi-asserted-by":"crossref","unstructured":"He Y, Liu P, Wang Z, Hu Z, Yang Y (2019) Filter pruning via geometric median for deep convolutional neural networks acceleration. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 4340\u20134349","DOI":"10.1109\/CVPR.2019.00447"},{"key":"2819_CR13","doi-asserted-by":"crossref","unstructured":"Lin M, Ji R, Wang Y, Zhang Y, Zhang B, Tian Y, Shao L (2020) Hrank: Filter pruning using high-rank feature map. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 1529\u20131538","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"2819_CR14","first-page":"24604","volume":"34","author":"Y Sui","year":"2021","unstructured":"Sui Y, Yin M, Xie Y, Phan H, Aliari Zonouz S, Yuan B (2021) Chip: Channel independence-based pruning for compact neural networks. Adv Neural Inf Process Syst 34:24604\u201324616","journal-title":"Adv Neural Inf Process Syst"},{"key":"2819_CR15","doi-asserted-by":"crossref","unstructured":"He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. Proceedings of the IEEE international conference on computer vision. pp 1389\u20131397","DOI":"10.1109\/ICCV.2017.155"},{"key":"2819_CR16","first-page":"1","volume":"31","author":"Z Zhuang","year":"2018","unstructured":"Zhuang Z, Tan M, Zhuang B, Liu J, Guo Y, Wu Q, Huang J, Zhu J (2018) Discrimination-aware channel pruning for deep neural networks. Adv Neural Inf Process Syst 31:1","journal-title":"Adv Neural Inf Process Syst"},{"key":"2819_CR17","first-page":"5113","volume-title":"International conference on machine learning","author":"H Peng","year":"2019","unstructured":"Peng H, Wu J, Chen S, Huang J (2019) Collaborative channel pruning for deep networks. International conference on machine learning. PMLR, pp 5113\u20135122"},{"issue":"3","key":"2819_CR18","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1145\/3007787.3001163","volume":"44","author":"S Han","year":"2016","unstructured":"Han S, Liu X, Mao H, Pu J, Pedram A, Horowitz MA, Dally WJ (2016) Eie: efficient inference engine on compressed deep neural network. ACM SIGARCH Comp Arch News 44(3):243\u2013254","journal-title":"ACM SIGARCH Comp Arch News"},{"issue":"3","key":"2819_CR19","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1145\/3007787.3001177","volume":"44","author":"Y-H Chen","year":"2016","unstructured":"Chen Y-H, Emer J, Sze V (2016) Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks. ACM SIGARCH Comp Arch News 44(3):367\u2013379","journal-title":"ACM SIGARCH Comp Arch News"},{"key":"2819_CR20","first-page":"1","volume":"28","author":"M Courbariaux","year":"2015","unstructured":"Courbariaux M, Bengio Y, David J-P (2015) Binaryconnect: training deep neural networks with binary weights during propagations. Adv Neural Inf Process Syst 28:1","journal-title":"Adv Neural Inf Process Syst"},{"key":"2819_CR21","doi-asserted-by":"crossref","unstructured":"Hoff PD (2016) Equivariant and scale-free tucker decomposition models","DOI":"10.1214\/14-BA934"},{"key":"2819_CR22","first-page":"1800","volume-title":"International conference on machine learning","author":"P Rai","year":"2014","unstructured":"Rai P, Wang Y, Guo S, Chen G, Dunson D, Carin L (2014) Scalable Bayesian low-rank decomposition of incomplete multiway tensors. International conference on machine learning. PMLR, pp 1800\u20131808"},{"issue":"9","key":"2819_CR23","doi-asserted-by":"publisher","first-page":"1751","DOI":"10.1109\/TPAMI.2015.2392756","volume":"37","author":"Q Zhao","year":"2015","unstructured":"Zhao Q, Zhang L, Cichocki A (2015) Bayesian cp factorization of incomplete tensors with automatic rank determination. IEEE Trans Pattern Anal Mach Intell 37(9):1751\u20131763","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2819_CR24","unstructured":"Ermis B, Cemgil AT (2014) A bayesian tensor factorization model via variational inference for link prediction. arXiv:1409.8276"},{"key":"2819_CR25","unstructured":"J\u00f8rgensen PJ, Nielsen SF, Hinrich JL, Schmidt MN, Madsen KH, M\u00f8rup M (2018) Probabilistic parafac2. arXiv:1806.08195"},{"key":"2819_CR26","doi-asserted-by":"crossref","unstructured":"Liu M, Han K, Luo S, Pan M, Hossain M, Yuan B, DeMara RF, Bai Y (2021) An efficient video prediction recurrent network using focal loss and decomposed tensor train for imbalance dataset. Proceedings of the 2021 on great lakes symposium on VLSI. pp 391\u2013396","DOI":"10.1145\/3453688.3461748"},{"issue":"5","key":"2819_CR27","doi-asserted-by":"publisher","first-page":"2295","DOI":"10.1137\/090752286","volume":"33","author":"IV Oseledets","year":"2011","unstructured":"Oseledets IV (2011) Tensor-train decomposition. SIAM J Sci Comput 33(5):2295\u20132317","journal-title":"SIAM J Sci Comput"},{"key":"2819_CR28","doi-asserted-by":"crossref","unstructured":"Deng C, Sun F, Qian X, Lin J, Wang Z, Yuan, B.: Tie, (2019) Energy-efficient tensor train-based inference engine for deep neural network. Proceedings of the 46th international symposium on computer architecture. pp 264\u2013278","DOI":"10.1145\/3307650.3322258"},{"key":"2819_CR29","unstructured":"Garipov T, Podoprikhin D, Novikov A, Vetrov D (2016) Ultimate tensorization: compressing convolutional and fc layers alike. arXiv:1611.03214"},{"issue":"2","key":"2819_CR30","doi-asserted-by":"publisher","first-page":"025010","DOI":"10.1088\/0266-5611\/27\/2\/025010","volume":"27","author":"S Gandy","year":"2011","unstructured":"Gandy S, Recht B, Yamada I (2011) Tensor completion and low-n-rank tensor recovery via convex optimization. Inverse Prob 27(2):025010","journal-title":"Inverse Prob"},{"issue":"1","key":"2819_CR31","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1137\/130905010","volume":"35","author":"D Goldfarb","year":"2014","unstructured":"Goldfarb D, Qin Z (2014) Robust low-rank tensor recovery: models and algorithms. SIAM J Matrix Anal Appl 35(1):225\u2013253","journal-title":"SIAM J Matrix Anal Appl"},{"issue":"502","key":"2819_CR32","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1080\/01621459.2013.776499","volume":"108","author":"H Zhou","year":"2013","unstructured":"Zhou H, Li L, Zhu H (2013) Tensor regression with applications in neuroimaging data analysis. J Am Stat Assoc 108(502):540\u2013552","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"2819_CR33","first-page":"2733","volume":"18","author":"R Guhaniyogi","year":"2017","unstructured":"Guhaniyogi R, Qamar S, Dunson DB (2017) Bayesian tensor regression. J Mach Learn Res 18(1):2733\u20132763","journal-title":"J Mach Learn Res"},{"key":"2819_CR34","doi-asserted-by":"crossref","unstructured":"Li N, Pan Y, Chen Y, Ding Z, Zhao D, Xu Z (2021) Heuristic rank selection with progressively searching tensor ring network. Complex Intell Syst :1\u201315","DOI":"10.1007\/s40747-021-00308-x"},{"key":"2819_CR35","unstructured":"Cai H, Zhu L, Han S (2018) Proxylessnas: direct neural architecture search on target task and hardware.arXiv:1812.00332"},{"key":"2819_CR36","first-page":"1","volume":"28","author":"S Han","year":"2015","unstructured":"Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. Adv Neural Inf Process Syst 28:1","journal-title":"Adv Neural Inf Process Syst"},{"key":"2819_CR37","doi-asserted-by":"crossref","unstructured":"Xiao J, Zhang C, Gong Y, Yin M, Sui Y, Xiang L, Tao D, Yuan B (2023) Haloc: hardware-aware automatic low-rank compression for compact neural networks","DOI":"10.1609\/aaai.v37i9.26244"},{"key":"2819_CR38","doi-asserted-by":"crossref","unstructured":"Liu H, Elkerdawy S, Ray N, Elhoushi M (2021) Layer importance estimation with imprinting for neural network quantization. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 2408\u20132417","DOI":"10.1109\/CVPRW53098.2021.00273"},{"key":"2819_CR39","doi-asserted-by":"crossref","unstructured":"Qi H, Brown M, Lowe DG (2018) Low-shot learning with imprinted weights. Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5822\u20135830","DOI":"10.1109\/CVPR.2018.00610"},{"key":"2819_CR40","doi-asserted-by":"publisher","first-page":"2940","DOI":"10.1109\/ICIP40778.2020.9191238","volume-title":"2020 IEEE international conference on image processing (ICIP)","author":"S Elkerdawy","year":"2020","unstructured":"Elkerdawy S, Elhoushi M, Singh A, Zhang H, Ray N (2020) One-shot layer-wise accuracy approximation for layer pruning. 2020 IEEE international conference on image processing (ICIP). IEEE, pp 2940\u20132944"},{"key":"2819_CR41","doi-asserted-by":"crossref","unstructured":"Elkerdawy S, Elhoushi M, Singh A, Zhang H, Ray N (2020) To filter prune, or to layer prune, that is the question. Proceedings of the Asian conference on computer vision","DOI":"10.1007\/978-3-030-69535-4_45"},{"key":"2819_CR42","unstructured":"Krizhevsky A, Hinton G, et al (2009) Learning multiple layers of features from tiny images"},{"key":"2819_CR43","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211\u2013252","journal-title":"Int J Comput Vision"},{"key":"2819_CR44","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2819_CR45","first-page":"5328","volume":"34","author":"L Liebenwein","year":"2021","unstructured":"Liebenwein L, Maalouf A, Feldman D, Rus D (2021) Compressing neural networks:towards determining the optimal layer-wise decomposition. Adv Neural Inf Process Syst 34:5328\u20135344","journal-title":"Adv Neural Inf Process Syst"},{"issue":"20","key":"2819_CR46","doi-asserted-by":"publisher","first-page":"3801","DOI":"10.3390\/math10203801","volume":"10","author":"K Sobolev","year":"2022","unstructured":"Sobolev K, Ermilov D, Phan A-H, Cichocki A (2022) Pars: proxy-based automatic rank selection for neural network compression via low-rank weight approximation. Mathematics 10(20):3801","journal-title":"Mathematics"},{"key":"2819_CR47","doi-asserted-by":"crossref","unstructured":"Idelbayev Y, Carreira-Perpin\u00e1n MA (2020) Low-rank compression of neural nets: Learning the rank of each layer. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 8049\u20138059","DOI":"10.1109\/CVPR42600.2020.00807"},{"key":"2819_CR48","doi-asserted-by":"crossref","unstructured":"Yin, M., Phan, H., Zang, X., Liao, S., Yuan, B (2022) Batude: budget-aware neural network compression based on tucker decomposition. Proceedings of the AAAI conference on artificial intelligence, vol. 36, pp. 8874\u20138882","DOI":"10.1609\/aaai.v36i8.20869"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02819-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02819-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02819-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T06:12:01Z","timestamp":1774073521000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02819-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,21]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["2819"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02819-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,21]]},"assertion":[{"value":"5 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","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 declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"222"}}