{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:49Z","timestamp":1750220329671,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["OAC-1925717"],"award-info":[{"award-number":["OAC-1925717"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482230","type":"proceedings-article","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T18:33:11Z","timestamp":1635618791000},"page":"2050-2059","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks"],"prefix":"10.1145","author":[{"given":"Yuke","family":"Wang","sequence":"first","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyuan","family":"Feng","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueqiao","family":"Peng","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Ding","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"volume-title":"Designing neural network architectures using reinforcement learning. ICLR","year":"2017","author":"Baker Bowen","key":"e_1_3_2_2_1_1"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"volume-title":"Simple and efficient architecture search for convolutional neural networks. arXiv","year":"2017","author":"Elsken Thomas","key":"e_1_3_2_2_4_1"},{"volume-title":"Jan Hendrik Metzen, and Frank Hutter","year":"2019","author":"Elsken Thomas","key":"e_1_3_2_2_5_1"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"volume-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv e-prints","year":"2017","author":"Howard Andrew G.","key":"e_1_3_2_2_8_1"},{"volume-title":"Rc-darts: Resource constrained differentiable architecture search. arXiv preprint arXiv:1912.12814","year":"2019","author":"Jin Xiaojie","key":"e_1_3_2_2_9_1"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"volume-title":"Pruning filters for efficient convnets. ICLR","year":"2017","author":"Li Hao","key":"e_1_3_2_2_13_1"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_2"},{"volume-title":"Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055","year":"2018","author":"Liu Hanxiao","key":"e_1_3_2_2_15_1"},{"volume-title":"Learning Efficient Convolutional Networks Through Network Slimming. In The IEEE International Conference on Computer Vision (ICCV).","year":"2017","author":"Liu Zhuang","key":"e_1_3_2_2_16_1"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"volume-title":"AtomNAS: Fine-Grained End-to-End Neural Architecture Search. In International Conference on Learning Representations (ICLR).","year":"2020","author":"Mei Jieru","key":"e_1_3_2_2_19_1"},{"volume-title":"International Conference on Machine Learning. PMLR, 4095?4104","year":"2018","author":"Pham Hieu","key":"e_1_3_2_2_20_1"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"volume-title":"International Conference on Machine Learning. PMLR, 4095--4104","year":"2018","author":"Pham Hieu","key":"e_1_3_2_2_22_1"},{"volume-title":"Regularized evolution for image classifier architecture search. AAAI","year":"2019","author":"Real Esteban","key":"e_1_3_2_2_23_1"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3305981"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"volume-title":"Observations on the scratch-reflex in the spinal dog. The Journal of physiology","year":"1906","author":"Sherrington Charles Scott","key":"e_1_3_2_2_26_1"},{"volume-title":"Very deep convolutional networks for large-scale image recognition. ICLR","year":"2015","author":"Simonyan Karen","key":"e_1_3_2_2_28_1"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298188"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.214"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999611.2999702"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"volume-title":"Scaling Up Neural Architecture Search with Big Single-Stage Models. arXiv preprint","year":"2019","author":"Yu Jiahui","key":"e_1_3_2_2_33_1"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00825"},{"volume-title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","year":"2018","author":"Zhang Xiangyu","key":"e_1_3_2_2_35_1"},{"key":"e_1_3_2_2_36_1","unstructured":"Yanqi Zhou and Gregory Diamos. 2018. Neural architect: A multi-objective neural architecture search with performance prediction. In SysML.  Yanqi Zhou and Gregory Diamos. 2018. Neural architect: A multi-objective neural architecture search with performance prediction. In SysML."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event Queensland Australia","acronym":"CIKM '21"},"container-title":["Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482230","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482230","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482230","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:12Z","timestamp":1750191132000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":35,"alternative-id":["10.1145\/3459637.3482230","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482230","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}