{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:22:59Z","timestamp":1743060179899,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031490071"},{"type":"electronic","value":"9783031490088"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-49008-8_12","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T13:04:15Z","timestamp":1702559055000},"page":"146-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DyPrune: Dynamic Pruning Rates for\u00a0Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3806-6940","authenticated-orcid":false,"given":"Richard Adolph Aires","family":"Jonker","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6450-023X","authenticated-orcid":false,"given":"Roshan","family":"Poudel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1957-4947","authenticated-orcid":false,"given":"Olga","family":"Fajarda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6672-6176","authenticated-orcid":false,"given":"Jos\u00e9 Lu\u00eds","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-5078","authenticated-orcid":false,"given":"Rui Pedro","family":"Lopes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1941-3983","authenticated-orcid":false,"given":"S\u00e9rgio","family":"Matos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"12_CR1","first-page":"129","volume":"2","author":"D Blalock","year":"2020","unstructured":"Blalock, D., Gonzalez Ortiz, J.J., Frankle, J., Guttag, J.: What is the state of neural network pruning? Proc. Mach. Learn. Syst. 2, 129\u2013146 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Finnoff, W., Hergert, F., Zimmermann, H.G.: Improving model selection by nonconvergent methods. Neural Netw. 6(6), 771\u2013783 (1993). https:\/\/doi.org\/10.1016\/S0893-6080(05)80122-4","DOI":"10.1016\/S0893-6080(05)80122-4"},{"key":"12_CR3","unstructured":"Gale, T., Elsen, E., Hooker, S.: The State of Sparsity in Deep Neural Networks (2019). https:\/\/doi.org\/10.48550\/arXiv.1902.09574, arXiv:1902.09574 [cs, stat]"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Hagiwara, M.: Removal of hidden units and weights for back propagation networks. In: Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan). vol. 1, pp. 351\u2013354 vol 1 (1993). https:\/\/doi.org\/10.1109\/IJCNN.1993.713929","DOI":"10.1109\/IJCNN.1993.713929"},{"key":"12_CR5","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. Curran Associates, Inc. (2015)"},{"key":"12_CR6","unstructured":"Hoefler, T., Alistarh, D., Ben-Nun, T., Dryden, N., Peste, A.: Sparsity in deep learning: pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22(1), 241:10882\u2013241:11005 (2021)"},{"issue":"12","key":"12_CR7","doi-asserted-by":"publisher","first-page":"6600","DOI":"10.1103\/PhysRevA.39.6600,","volume":"39","author":"SA Janowsky","year":"1989","unstructured":"Janowsky, S.A.: Pruning versus clipping in neural networks. Phys. Rev. A 39(12), 6600\u20136603 (1989). https:\/\/doi.org\/10.1103\/PhysRevA.39.6600,","journal-title":"Phys. Rev. A"},{"issue":"1","key":"12_CR8","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/21.101159","volume":"21","author":"JK Kruschke","year":"1991","unstructured":"Kruschke, J.K., Movellan, J.R.: Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks. IEEE Trans. Syst. Man Cybern. 21(1), 273\u2013280 (1991)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"11","key":"12_CR9","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.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"12_CR10","unstructured":"LeCun, Y., Denker, J., Solla, S.: Optimal brain damage. Advances in Neural Information Processing Systems, vol. 2 (1989)"},{"key":"12_CR11","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets (2016). arXiv:1608.08710"},{"key":"12_CR12","unstructured":"Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: Pruning convolutional neural networks for resource efficient inference (2016). arXiv:1611.06440"},{"issue":"13","key":"12_CR13","doi-asserted-by":"publisher","first-page":"2831","DOI":"10.1016\/j.neucom.2007.08.026,","volume":"71","author":"PL Narasimha","year":"2008","unstructured":"Narasimha, P.L., Delashmit, W.H., Manry, M.T., Li, J., Maldonado, F.: An integrated growing-pruning method for feedforward network training. Neurocomputing 71(13), 2831\u20132847 (2008). https:\/\/doi.org\/10.1016\/j.neucom.2007.08.026,","journal-title":"Neurocomputing"},{"key":"12_CR14","unstructured":"Thimm, G., Fiesler, E.: Evaluating pruning methods. In: Proceedings of the International Symposium on Artificial Neural Networks, pp. 20\u201325 (1995)"},{"key":"12_CR15","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms (2017). https:\/\/doi.org\/10.48550\/arXiv.1708.07747, arXiv:1708.07747 [cs, stat]"},{"key":"12_CR16","unstructured":"Zhang, Q., Zhang, R., Sun, J., Liu, Y.: How Sparse Can We Prune A Deep Network: A Geometric Viewpoint (2023). https:\/\/doi.org\/10.48550\/arXiv.2306.05857, arXiv:2306.05857 [cs, stat]"},{"key":"12_CR17","unstructured":"Zhu, M., Gupta, S.: To prune, or not to prune: exploring the efficacy of pruning for model compression (2017). https:\/\/doi.org\/10.48550\/arXiv.1710.01878,, arXiv:1710.01878 [cs, stat]"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49008-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T13:14:43Z","timestamp":1702559683000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49008-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031490071","9783031490088"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49008-8_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faial Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2023.inesctec.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"163","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"52% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}