{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T14:14:39Z","timestamp":1778249679966,"version":"3.51.4"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030304836","type":"print"},{"value":"9783030304843","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30484-3_25","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:03:18Z","timestamp":1567969398000},"page":"299-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Multi-objective Pruning for CNNs Using Genetic Algorithm"],"prefix":"10.1007","author":[{"given":"Chuanguang","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhulin","family":"An","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyu","family":"Diao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"25_CR1","unstructured":"MNIST dataset. \n                      http:\/\/yann.lecun.com\/exdb\/mnist\/\n                      \n                    . Accessed 23 Mar 2019"},{"key":"25_CR2","unstructured":"Louizos, C., Welling, M., Kingma, D.P.: Learning sparse neural networks through \n                      \n                        \n                      \n                      $$l_{0}$$\n                      \n                        \n                          \n                            l\n                            0\n                          \n                        \n                      \n                     regularization. In: Proceedings of the International Conference on Learning Representations (2018)"},{"key":"25_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01418-6_1","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"X Dong","year":"2018","unstructured":"Dong, X., Liu, L., Li, G., Zhao, P., Feng, X.: Fast CNN pruning via redundancy-aware training. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 3\u201313. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01418-6_1"},{"issue":"11","key":"25_CR4","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., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). \n                      https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"25_CR5","doi-asserted-by":"publisher","unstructured":"Mao, H., et al.: Exploring the granularity of sparsity in convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 13\u201320 (2017). \n                      https:\/\/doi.org\/10.1109\/cvprw.2017.241","DOI":"10.1109\/cvprw.2017.241"},{"key":"25_CR6","unstructured":"Molchanov, D., Ashukha, A., Vetrov, D.: Variational dropout sparsifies deep neural networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2498\u20132507. JMLR.org (2017)"},{"key":"25_CR7","unstructured":"Neklyudov, K., Molchanov, D., Ashukha, A., Vetrov, D.P.: Structured Bayesian pruning via log-normal multiplicative noise. In: Advances in Neural Information Processing Systems, pp. 6775\u20136784 (2017)"},{"key":"25_CR8","doi-asserted-by":"publisher","unstructured":"Srinivas, S., Babu, V.: Learning neural network architectures using backpropagation. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 104.1\u2013104.11. BMVA Press, September 2016. \n                      https:\/\/doi.org\/10.5244\/C.30.104","DOI":"10.5244\/C.30.104"},{"key":"25_CR9","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. In: Advances in Neural Information Processing Systems, vol. 29, pp. 2074\u20132082 (2016)"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Srinivas, S., Subramanya, A., Babu, R.V.: Training sparse neural networks. arXiv preprint \n                      arXiv:1611.06694\n                      \n                     (2016)","DOI":"10.1109\/CVPRW.2017.61"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Deep Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30484-3_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:44:18Z","timestamp":1567971858000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30484-3_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304836","9783030304843"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30484-3_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}