{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:59:58Z","timestamp":1742925598401,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":45,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819754915"},{"type":"electronic","value":"9789819754922"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5492-2_2","type":"book-chapter","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T09:02:47Z","timestamp":1721898167000},"page":"16-27","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Elastic Filter Prune in Deep Neural Networks Using Modified Weighted Hybrid Criterion"],"prefix":"10.1007","author":[{"given":"Wei","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingce","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2022.10.021","volume":"516","author":"S Chen","year":"2023","unstructured":"Chen, S., Zhou, J., Sun, W., Huang, L.: Joint matrix decomposition for deep convolutional neural networks compression. Neurocomputing 516, 11\u201326 (2023)","journal-title":"Neurocomputing"},{"key":"2_CR3","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"2_CR4","unstructured":"Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. In: International Conference on Learning Representations (2019)"},{"key":"2_CR5","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: International Conference on Learning Representations (2017)"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. IJCAI (2018)","DOI":"10.24963\/ijcai.2018\/309"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: IEEE\/CVF CVPR, pp. 4335\u20134344 (2019)","DOI":"10.1109\/CVPR.2019.00447"},{"key":"2_CR8","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. In: ICLR (2016)"},{"key":"2_CR9","unstructured":"Guo, Y., Yao, A., Chen, Y.: Dynamic network surgery for efficient DNNs. In: 30th NIPS, p. 1387\u20131395.\u201916, Curran Associates Inc., Red Hook, NY, USA (2016)"},{"issue":"6","key":"2_CR10","doi-asserted-by":"publisher","first-page":"4156","DOI":"10.1109\/TII.2019.2948094","volume":"16","author":"K Gai","year":"2020","unstructured":"Gai, K., Wu, Y., Zhu, L., Zhang, Z., Qiu, M.: Differential privacy-based blockchain for industrial Internet-of-Things. IEEE T. Ind. Inform. 16(6), 4156\u20134165 (2020)","journal-title":"IEEE T. Ind. Inform."},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Wu, J., Lin, W.: ThiNet: a filter level pruning method for deep neural network compression. In: Proceedings of the IEEE ICCV (2017)","DOI":"10.1109\/ICCV.2017.541"},{"key":"2_CR12","unstructured":"Lee, J., Park, S., Mo, S., Ahn, S., Shin, J.: Layer-adaptive sparsity for the magnitude-based pruning. In: International Conference on Learning Representations (2021)"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Wu, J.: Neural network pruning with residual-connections and limited-data. In: 2020 IEEE\/CVF CVPR, pp. 1455\u20131464 (2020)","DOI":"10.1109\/CVPR42600.2020.00153"},{"key":"2_CR14","first-page":"1","volume":"2023","author":"S Chen","year":"2023","unstructured":"Chen, S., Sun, W., Huang, L.: WHC: Weighted hybrid criterion for filter pruning on convolutional neural networks. ICASSP 2023, 1\u20135 (2023)","journal-title":"ICASSP"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: IEEE ICCV, pp. 2755\u20132763 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"2_CR16","unstructured":"Ye, J., Lu, X., Lin, Z., Wang, J.Z.: Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers. ICLR (2018)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Mallya, A., Tyree, S., Frosio, I., Kautz, J.: Importance estimation for neural network pruning. In: IEEE\/CVF CVPR, pp. 11256\u201311264 (2019)","DOI":"10.1109\/CVPR.2019.01152"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Yu, S., Yao, Z., et al., Hessian-aware pruning and optimal neural implant. In: 2022 IEEE\/CVF Winter Conf. on App. of Computer Vision (WACV), pp. 3665\u20133676 (2022)","DOI":"10.1109\/WACV51458.2022.00372"},{"key":"2_CR19","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Yu, R., Li, A., Chen, C.F., et al.: Nisp: pruning networks using neuron importance score propagation. In: 2018 IEEE\/CVF CVPR, pp. 9194\u20139203 (2018)","DOI":"10.1109\/CVPR.2018.00958"},{"issue":"2","key":"2_CR21","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat Methodol. 67(2), 301\u2013320 (2005)","journal-title":"J. R. Stat. Soc. Ser. B Stat Methodol."},{"issue":"17","key":"2_CR22","doi-asserted-by":"publisher","first-page":"3716","DOI":"10.1016\/j.neucom.2011.06.013","volume":"74","author":"JM Mart\u00ednez-Mart\u00ednez","year":"2011","unstructured":"Mart\u00ednez-Mart\u00ednez, J.M., Escandell-Montero, P., et al.: Regularized extreme learning machine for regression problems. Neurocomputing 74(17), 3716\u20133721 (2011)","journal-title":"Neurocomputing"},{"key":"2_CR23","unstructured":"Ben-Guigui, Y., Goldberger, J., Riklin-Raviv, T.: The role of regularization in shaping weight and node pruning dependency and dynamics. ArXiv abs\/2012.03827 (2020)"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Lin, S., Ji, R., Yan, C., Zhang, B., Cao, L., Ye, Q., Huang, F., Doermann, D.: Towards optimal structured CNN pruning via generative adversarial learning. IEEE\/CVF CVPR (2019)","DOI":"10.1109\/CVPR.2019.00290"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Lin, M., Ji, R., Wang, Y., Zhang, Y., Zhang, B., Tian, Y., Shao, L.: Hrank: filter pruning using high-rank feature map. In: Proceedings of the IEEE\/CVF CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00160"},{"issue":"8","key":"2_CR26","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1109\/TCYB.2019.2933477","volume":"50","author":"Y He","year":"2020","unstructured":"He, Y., Dong, X., Kang, G., Fu, Y., Yan, C., Yang, Y.: Asymptotic soft filter pruning for deep convolutional neural networks. IEEE T. Cybern. 50(8), 3594\u20133604 (2020)","journal-title":"IEEE T. Cybern."},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Gai, K., Xiao, Q., et al.: Digital twin-enabled AI enhancement in smart critical infrastructures for 5g. ACM Trans. Sen. Netw. 18(3) (2022)","DOI":"10.1145\/3526195"},{"key":"2_CR28","first-page":"20378","volume":"33","author":"V Sanh","year":"2020","unstructured":"Sanh, V., Wolf, T., Rush, A.: Movement pruning: adaptive sparsity by fine-tuning. Adv. Neural. Inf. Process. Syst. 33, 20378\u201320389 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"6","key":"2_CR29","first-page":"546","volume":"69","author":"M Qiu","year":"2009","unstructured":"Qiu, M., Guo, M., et al.: Loop scheduling and bank type assignment for heterogeneous multi-bank memory. JPDC 69(6), 546\u2013558 (2009)","journal-title":"JPDC"},{"issue":"2s","key":"2_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2544375.2544390","volume":"13","author":"H Huang","year":"2014","unstructured":"Huang, H., Chaturvedi, V., et al.: Throughput maximization for periodic real-time systems under the maximal temperature constraint. ACM TECS 13(2s), 1\u201322 (2014)","journal-title":"ACM TECS"},{"issue":"1","key":"2_CR31","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1109\/TCC.2016.2607708","volume":"7","author":"M Qiu","year":"2016","unstructured":"Qiu, M., Dai, W., Vasilakos, A.: Loop parallelism maximization for multimedia data processing in mobile vehicular clouds. IEEE Trans. Cloud Comput. 7(1), 250\u2013258 (2016)","journal-title":"IEEE Trans. Cloud Comput."},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Qiu, M., Qiu, H.: Review on image processing based adversarial example defenses in computer vision. In: IEEE 6th BigDataSecurity (2020)","DOI":"10.1109\/BigDataSecurity-HPSC-IDS49724.2020.00027"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Qiu, H., et al.: A data augmentation-based defense method against adversarial attacks in neural networks. In: ICA3PP 2020, New York City (2020)","DOI":"10.1007\/978-3-030-60239-0_19"},{"issue":"3","key":"2_CR34","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/MWC.2019.1800407","volume":"26","author":"K Gai","year":"2019","unstructured":"Gai, K., Xu, K., et al.: Fusion of cognitive wireless networks and edge computing. IEEE Wirel. Comm. 26(3), 69\u201375 (2019)","journal-title":"IEEE Wirel. Comm."},{"issue":"1","key":"2_CR35","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/JSYST.2015.2460747","volume":"11","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., Qiu, M., et al.: Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88\u201395 (2015)","journal-title":"IEEE Syst. J."},{"issue":"1","key":"2_CR36","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1109\/COMST.2021.3134955","volume":"24","author":"X Wei","year":"2021","unstructured":"Wei, X., Guo, H., et al.: Reliable data collection techniques in underwater wireless sensor networks: a survey. IEEE Comm. Surv. Tutor. 24(1), 404\u2013431 (2021)","journal-title":"IEEE Comm. Surv. Tutor."},{"key":"2_CR37","unstructured":"Qiu, M., Zhang, K., Huang, M.: Usability in mobile interface browsing. Web Intell. Agent Syst. Intl. J. 4(1), 43\u201359 (2006)"},{"issue":"5","key":"2_CR38","doi-asserted-by":"publisher","first-page":"3180","DOI":"10.1109\/JIOT.2020.3004498","volume":"8","author":"H Qiu","year":"2020","unstructured":"Qiu, H., Zheng, Q., et al.: Toward secure and efficient deep learning inference in dependable IoT systems. IEEE Internet Things J. 8(5), 3180\u20133188 (2020)","journal-title":"IEEE Internet Things J."},{"key":"2_CR39","unstructured":"Cui, Y., Cao, K., et al.: Client scheduling and resource management for efficient training in heterogeneous IoT-edge federated learning. In: IEEE TCAD (2021)"},{"issue":"9","key":"2_CR40","doi-asserted-by":"publisher","first-page":"6163","DOI":"10.1109\/TII.2019.2950667","volume":"16","author":"Y Song","year":"2019","unstructured":"Song, Y., Li, Y., et al.: Retraining strategy-based domain adaption network for intelligent fault diagnosis. IEEE T. Ind. Inform. 16(9), 6163\u20136171 (2019)","journal-title":"IEEE T. Ind. Inform."},{"key":"2_CR41","unstructured":"Ling, C., Jiang, J., et al.: Deep graph representation learning and optimization for influence maximization. In: ICML (2023)"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qiu, M., Gao, H.: Communication-efficient stochastic gradient descent ascent with momentum algorithms. In: IJCAI (2023)","DOI":"10.24963\/ijcai.2023\/512"},{"key":"2_CR43","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Pan, M., et al.: Narcissus: a practical clean-label backdoor attack with limited information. In: ACM CCS (2023)","DOI":"10.1145\/3576915.3616617"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Gai, K., Zhang, Y., et al.: Blockchain-enabled service optimizations in supply chain digital twin. IEEE Trans. Serv. Comput. (2022)","DOI":"10.1109\/TSC.2022.3192166"},{"key":"2_CR45","doi-asserted-by":"crossref","unstructured":"Li, C., M.: Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies. CRC Press (2019)","DOI":"10.1201\/9781351006620"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5492-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T09:03:50Z","timestamp":1721898230000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5492-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819754915","9789819754922"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5492-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Birmingham","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ai-edge.net\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}