{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:50:39Z","timestamp":1763643039592,"version":"3.28.0"},"reference-count":33,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T00:00:00Z","timestamp":1602979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T00:00:00Z","timestamp":1602979200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T00:00:00Z","timestamp":1602979200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100006190","name":"Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10,18]]},"DOI":"10.1109\/iecon43393.2020.9254493","type":"proceedings-article","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T16:55:32Z","timestamp":1605718532000},"page":"5343-5349","source":"Crossref","is-referenced-by-count":6,"title":["Soft Taylor Pruning for Accelerating Deep Convolutional Neural Networks"],"prefix":"10.1109","author":[{"given":"Jintao","family":"Rong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiyi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Ou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"ref32","article-title":"Graph pruning for model compression","author":"zhang","year":"2019","journal-title":"arXiv preprint arXiv 1911 12945"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00339"},{"key":"ref30","article-title":"Network slimming by slimmable networks: Towards one-shot architecture search for channel numbers","author":"yu","year":"2019","journal-title":"arXiv preprint arXiv 1903 11593"},{"key":"ref10","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"molchanov","year":"2016","journal-title":"arXiv preprint arXiv 1611 06440"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/309"},{"key":"ref12","article-title":"Network trimming: A data-driven neuron pruning approach towards efficient deep architectures","author":"hu","year":"2016","journal-title":"arXiv preprint arXiv 1607 03250"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref16","article-title":"Principal filter analysis for guided network compression","volume":"2","author":"suau","year":"2018","journal-title":"arXiv preprint arX-iv 1807 10585"},{"key":"ref17","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2015","journal-title":"arXiv preprint arXiv 1510 00149"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.31"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"article-title":"Automatic differentiation in pytorch","year":"2017","author":"paszke","key":"ref28"},{"key":"ref4","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.1700789"},{"key":"ref29","article-title":"Slimmable neural networks","author":"yu","year":"2018","journal-title":"arXiv preprint arXiv 1812 11467"},{"key":"ref5","article-title":"A survey of model compression and acceleration for deep neural networks","author":"cheng","year":"2017","journal-title":"arXiv preprint arXiv 1710 09282"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01152"},{"key":"ref7","article-title":"Rethinking the value of network pruning","author":"liu","year":"2018","journal-title":"arXiv preprint arXiv 1810 06008"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref9","article-title":"Pruning filters for efficient convnets","author":"li","year":"2016","journal-title":"arXiv preprint arXiv 1608 08710"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","article-title":"Rethinking the smaller-normless-informative assumption in channel pruning of convolution layers","author":"ye","year":"2018","journal-title":"arXiv preprint arXiv 1802 00420"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_19"},{"key":"ref24","first-page":"164","article-title":"Second order derivatives for network pruning: Optimal brain surgeon","author":"hassibi","year":"1993","journal-title":"Advances in neural information processing systems"},{"key":"ref23","first-page":"598","article-title":"Optimal brain damage","author":"lecun","year":"1990","journal-title":"Advances in neural information processing systems"},{"key":"ref26","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv preprint arX-iv 1704 04861"},{"key":"ref25","article-title":"Faster gaze prediction with dense networks and fisher pruning","author":"theis","year":"2018","journal-title":"arXiv preprint arXiv 1801 05078"}],"event":{"name":"IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society","start":{"date-parts":[[2020,10,18]]},"location":"Singapore, Singapore","end":{"date-parts":[[2020,10,21]]}},"container-title":["IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9254213\/9254215\/09254493.pdf?arnumber=9254493","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T20:07:50Z","timestamp":1656360470000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9254493\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,18]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/iecon43393.2020.9254493","relation":{},"subject":[],"published":{"date-parts":[[2020,10,18]]}}}