{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:29:57Z","timestamp":1781872197931,"version":"3.54.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s10489-021-02243-3","type":"journal-article","created":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T19:38:11Z","timestamp":1616096291000},"page":"7837-7854","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Joint sparse neural network compression via multi-application multi-objective optimization"],"prefix":"10.1007","volume":"51","author":[{"given":"Jinzhuo","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongnan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8658-7775","authenticated-orcid":false,"given":"Weize","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,18]]},"reference":[{"key":"2243_CR1","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"2243_CR2","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp 1725\u20131732","DOI":"10.1109\/CVPR.2014.223"},{"key":"2243_CR3","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2243_CR4","unstructured":"Soomro K, Zamir A R, Shah M (2012) Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv:1212.0402"},{"key":"2243_CR5","doi-asserted-by":"crossref","unstructured":"Ghadiyaram D, Tran D, Mahajan D (2019) Large-scale weakly-supervised pre-training for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 12046\u201312055","DOI":"10.1109\/CVPR.2019.01232"},{"issue":"6","key":"2243_CR6","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton G, Deng L, Yu D, Dahl G E, Mohamed A-R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T N et al (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag 29(6):82\u201397","journal-title":"IEEE Signal Process Mag"},{"key":"2243_CR7","doi-asserted-by":"crossref","unstructured":"Xiong W, Droppo J, Huang X, Seide F, Seltzer M, Stolcke A, Yu D, Zweig G (2016) Achieving human parity in conversational speech recognition. arXiv:1610.05256","DOI":"10.1109\/TASLP.2017.2756440"},{"key":"2243_CR8","unstructured":"Oord Avd, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: A generative model for raw audio. arXiv:1609.03499"},{"key":"2243_CR9","doi-asserted-by":"crossref","unstructured":"Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv:1404.2188","DOI":"10.3115\/v1\/P14-1062"},{"key":"2243_CR10","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"2243_CR11","doi-asserted-by":"crossref","unstructured":"Xu J, Wang P, Tian G, Xu B, Zhao J, Wang F, Hao H (2015) Short text clustering via convolutional neural networks. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp 62\u201369","DOI":"10.3115\/v1\/W15-1509"},{"issue":"11","key":"2243_CR12","doi-asserted-by":"publisher","first-page":"4047","DOI":"10.1007\/s10489-018-1190-6","volume":"48","author":"LM Abualigah","year":"2018","unstructured":"Abualigah L M, Khader A T, Hanandeh E S (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047\u20134071","journal-title":"Appl Intell"},{"key":"2243_CR13","doi-asserted-by":"crossref","unstructured":"Abualigah L M Q (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer","DOI":"10.1007\/978-3-030-10674-4"},{"issue":"16-18","key":"2243_CR14","doi-asserted-by":"publisher","first-page":"3943","DOI":"10.1016\/j.neucom.2009.04.017","volume":"72","author":"NP Bidargaddi","year":"2009","unstructured":"Bidargaddi N P, Chetty M, Kamruzzaman J (2009) Combining segmental semi-markov models with neural networks for protein secondary structure prediction. Neurocomputing 72(16-18):3943\u20133950","journal-title":"Neurocomputing"},{"issue":"5","key":"2243_CR15","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1021\/acs.molpharmaceut.5b00982","volume":"13","author":"P Mamoshina","year":"2016","unstructured":"Mamoshina P, Vieira A, Putin E, Zhavoronkov A (2016) Applications of deep learning in biomedicine. Mol Pharm 13(5):1445\u2013 1454","journal-title":"Mol Pharm"},{"key":"2243_CR16","doi-asserted-by":"crossref","unstructured":"Yu X, Liu T, Wang X, Tao D (2017) On compressing deep models by low rank and sparse decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7370\u20137379","DOI":"10.1109\/CVPR.2017.15"},{"key":"2243_CR17","unstructured":"Iandola F N, Han S, Moskewicz M W, Ashraf K, Dally W J, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and<\u20090.5 mb model size. arXiv:1602.07360"},{"key":"2243_CR18","unstructured":"Howard A G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"2243_CR19","unstructured":"Denton E L, Zaremba W, Bruna J, LeCun Y, Fergus R (2014) Exploiting linear structure within convolutional networks for efficient evaluation. In: Advances in neural information processing systems, pp 1269\u20131277"},{"key":"2243_CR20","doi-asserted-by":"crossref","unstructured":"Zhang X, Zou J, Ming X, He K, Sun J (2015) Efficient and accurate approximations of nonlinear convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and pattern Recognition, pp 1984\u20131992","DOI":"10.1109\/CVPR.2015.7298809"},{"key":"2243_CR21","doi-asserted-by":"crossref","unstructured":"Sainath T N, Kingsbury B, Sindhwani V, Arisoy E, Ramabhadran B (2013) Low-rank matrix factorization for deep neural network training with high-dimensional output targets. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 6655\u20136659","DOI":"10.1109\/ICASSP.2013.6638949"},{"key":"2243_CR22","unstructured":"Han S, Mao H, Dally W J (2015) Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv:1510.00149"},{"key":"2243_CR23","unstructured":"Chen W, Wilson J, Tyree S, Weinberger K, Chen Y (2015) Compressing neural networks with the hashing trick. In: International conference on machine learning, pp 2285\u20132294"},{"key":"2243_CR24","unstructured":"Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. In: Advances in neural information processing systems, pp 1135\u20131143"},{"issue":"18","key":"2243_CR25","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1049\/el.2017.2621","volume":"53","author":"Q Xu","year":"2017","unstructured":"Xu Q, Pan G (2017) Sparseconnect: regularising cnns on fully connected layers. Electron Lett 53(18):1246\u20131248","journal-title":"Electron Lett"},{"key":"2243_CR26","doi-asserted-by":"crossref","unstructured":"Luo J-H, Wu J, Lin W (2017) Thinet: A filter level pruning method for deep neural network compression. In: Proceedings of the IEEE international conference on computer vision, pp 5058\u20135066","DOI":"10.1109\/ICCV.2017.541"},{"key":"2243_CR27","doi-asserted-by":"crossref","unstructured":"Shao M, Dai J, Kuang J, Meng D (2020) A dynamic cnn pruning method based on matrix similarity. SIViP:1\u20139","DOI":"10.1007\/s11760-020-01760-x"},{"key":"2243_CR28","unstructured":"Schaffer J D (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the first international conference on genetic algorithms and their applications. Lawrence Erlbaum Associates. Inc., Publishers"},{"issue":"12","key":"2243_CR29","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.1109\/TNNLS.2015.2469673","volume":"26","author":"M Gong","year":"2015","unstructured":"Gong M, Liu J, Li H, Cai Q, Su L (2015) A multiobjective sparse feature learning model for deep neural networks. IEEE Trans Neural Netw Learn Syst 26(12):3263\u20133277","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"2243_CR30","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.4249\/scholarpedia.5947","volume":"4","author":"GE Hinton","year":"2009","unstructured":"Hinton G E (2009) Deep belief networks. Scholarpedia 4(5):5947","journal-title":"Scholarpedia"},{"issue":"6","key":"2243_CR31","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang Q, Li H (2007) Moea\/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712\u2013731","journal-title":"IEEE Trans Evol Comput"},{"issue":"10","key":"2243_CR32","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TNNLS.2016.2582798","volume":"28","author":"C Zhang","year":"2016","unstructured":"Zhang C, Lim P, Qin A K, Tan K C (2016) Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans Neural Netw Learn Syst 28(10):2306\u20132318","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"2243_CR33","doi-asserted-by":"publisher","first-page":"2450","DOI":"10.1109\/TNNLS.2017.2695223","volume":"29","author":"J Liu","year":"2017","unstructured":"Liu J, Gong M, Miao Q, Wang X, Li H (2017) Structure learning for deep neural networks based on multiobjective optimization. IEEE Trans Neural Netw Learn Syst 29(6):2450\u20132463","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"10","key":"2243_CR34","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan S J, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2243_CR35","doi-asserted-by":"crossref","unstructured":"Molchanov P, Mallya A, Tyree S, Frosio I, Kautz J (2019) Importance estimation for neural network pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 11264\u201311272","DOI":"10.1109\/CVPR.2019.01152"},{"issue":"4","key":"2243_CR36","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257\u2013271","journal-title":"IEEE Trans Evol Comput"},{"key":"2243_CR37","unstructured":"Zitzler E, Laumanns M, Thiele L (2001) Spea2: Improving the strength pareto evolutionary algorithm. TIK-report 103"},{"issue":"3","key":"2243_CR38","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1162\/evco.1994.2.3.221","volume":"2","author":"N Srinivas","year":"1994","unstructured":"Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221\u2013248","journal-title":"Evol Comput"},{"issue":"2","key":"2243_CR39","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182\u2013197","journal-title":"IEEE Trans Evol Comput"},{"key":"2243_CR40","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.neucom.2015.02.051","volume":"160","author":"G-Q Zeng","year":"2015","unstructured":"Zeng G-Q, Chen J, Dai Y-X, Li L-M, Zheng C-W, Chen M-R (2015) Design of fractional order pid controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing 160:173\u2013184","journal-title":"Neurocomputing"},{"key":"2243_CR41","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.ins.2015.10.010","volume":"330","author":"G-Q Zeng","year":"2016","unstructured":"Zeng G-Q, Chen J, Li L-M, Chen M-R, Wu L, Dai Y-X, Zheng C-W (2016) An improved multi-objective population-based extremal optimization algorithm with polynomial mutation. Inf Sci 330:49\u201373","journal-title":"Inf Sci"},{"key":"2243_CR42","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.renene.2019.05.024","volume":"143","author":"M-R Chen","year":"2019","unstructured":"Chen M-R, Zeng G-Q, Lu K-D (2019) Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources. Renew Energy 143:277\u2013294","journal-title":"Renew Energy"},{"issue":"1-2","key":"2243_CR43","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S0004-3702(00)00007-2","volume":"119","author":"S Boettcher","year":"2000","unstructured":"Boettcher S, Percus A (2000) Nature\u2019s way of optimizing. Artif Intell 119(1-2):275\u2013286","journal-title":"Artif Intell"},{"issue":"2","key":"2243_CR44","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1002\/cplx.10072","volume":"8","author":"S Boettcher","year":"2002","unstructured":"Boettcher S, Percus A G (2002) Optimization with extremal dynamics. Complexity 8(2):57\u201362","journal-title":"Complexity"},{"key":"2243_CR45","unstructured":"Reiners M, Klamroth K, Stiglmayr M (2020) Efficient and sparse neural networks by pruning weights in a multiobjective learning approach. arXiv:2008.13590"},{"key":"2243_CR46","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.neucom.2019.10.053","volume":"378","author":"J Huang","year":"2020","unstructured":"Huang J, Sun W, Huang L (2020) Deep neural networks compression learning based on multiobjective evolutionary algorithms. Neurocomputing 378:260\u2013269","journal-title":"Neurocomputing"},{"key":"2243_CR47","doi-asserted-by":"crossref","unstructured":"Wang Z, Li F, Shi G, Xie X, Wang F (2020) Network pruning using sparse learning and genetic algorithm. Neurocomputing","DOI":"10.1016\/j.neucom.2020.03.082"},{"key":"2243_CR48","doi-asserted-by":"crossref","unstructured":"Yang C, An Z, Li C, Diao B, Xu Y (2019) Multi-objective pruning for cnns using genetic algorithm. In: International Conference on Artificial Neural NetworksSpringer, pp 299\u2013305","DOI":"10.1007\/978-3-030-30484-3_25"},{"issue":"11","key":"2243_CR49","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"11","key":"2243_CR50","doi-asserted-by":"publisher","first-page":"10346","DOI":"10.1109\/TVT.2017.2737553","volume":"66","author":"Q Gao","year":"2017","unstructured":"Gao Q, Wang J, Ma X, Feng X, Wang H (2017) Csi-based device-free wireless localization and activity recognition using radio image features. IEEE Trans Veh Technol 66(11):10346\u201310356","journal-title":"IEEE Trans Veh Technol"},{"issue":"2","key":"2243_CR51","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1109\/TMC.2016.2557795","volume":"16","author":"H Wang","year":"2016","unstructured":"Wang H, Zhang D, Wang Y, Ma J, Wang Y, Li S (2016) Rt-fall: A real-time and contactless fall detection system with commodity wifi devices. IEEE Trans Mob Comput 16(2):511\u2013526","journal-title":"IEEE Trans Mob Comput"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02243-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02243-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02243-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T05:37:31Z","timestamp":1633585051000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02243-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,18]]},"references-count":51,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["2243"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02243-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,18]]},"assertion":[{"value":"27 January 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}