{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:21:34Z","timestamp":1772907694854,"version":"3.50.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100005049","name":"Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funds","doi-asserted-by":"publisher","award":["A1892b0026"],"award-info":[{"award-number":["A1892b0026"]}],"id":[{"id":"10.13039\/501100005049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005049","name":"Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funds","doi-asserted-by":"publisher","award":["A19E3b0099"],"award-info":[{"award-number":["A19E3b0099"]}],"id":[{"id":"10.13039\/501100005049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1109\/tcsvt.2022.3167951","type":"journal-article","created":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T20:19:02Z","timestamp":1650313142000},"page":"6488-6501","source":"Crossref","is-referenced-by-count":24,"title":["QLP: Deep Q-Learning for Pruning Deep Neural Networks"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5342-5163","authenticated-orcid":false,"given":"Efe","family":"Camci","sequence":"first","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7522-0898","authenticated-orcid":false,"given":"Manas","family":"Gupta","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8971-0660","authenticated-orcid":false,"given":"Jie","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207490"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21931"},{"key":"ref3","article-title":"End-to-end motion planning of quadrotors using deep reinforcement learning","author":"Camci","year":"2019","journal-title":"arXiv:1909.13599"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1355"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.2976475"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3095970"},{"key":"ref7","article-title":"What is the state of neural network pruning?","author":"Blalock","year":"2020","journal-title":"arXiv:2003.03033"},{"key":"ref8","first-page":"1","article-title":"Soft weight-sharing for neural network compression","volume-title":"Proc. 5th Int. Conf. Learn. Represent., (ICLR)","author":"Ullrich"},{"key":"ref9","first-page":"1","article-title":"8-bit approximations for parallelism in deep learning","volume-title":"Proc. 4th Int. Conf. Learn. Represent., (ICLR)","author":"Dettmers"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3090902"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1137\/070690729"},{"key":"ref12","first-page":"4095","article-title":"Efficient neural architecture search via parameters sharing","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","volume":"80","author":"Pham"},{"key":"ref13","article-title":"Learning both weights and connections for efficient neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Han"},{"key":"ref14","first-page":"1","article-title":"SNIP: Single-shot network pruning based on connection sensitivity","volume-title":"Proc. 7th Int. Conf. Learn. Represent., (ICLR)","author":"Lee"},{"key":"ref15","first-page":"1","article-title":"Pruning filters for efficient convnets","volume-title":"Proc. 5th Int. Conf. Learn. Represent., (ICLR)","author":"Li"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989385"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967592"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","first-page":"2181","article-title":"Runtime neural pruning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"ref22","article-title":"Learning to prune deep neural networks via reinforcement learning","author":"Gupta","year":"2020","journal-title":"arXiv:2007.04756"},{"key":"ref23","first-page":"14747","article-title":"Storage efficient and dynamic flexible runtime channel pruning via deep reinforcement learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Chen"},{"key":"ref24","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref25","first-page":"107","article-title":"Skeletonization: A technique for trimming the fat from a network via relevance assessment","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS) Conf.","author":"Mozer"},{"key":"ref26","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref27","first-page":"6379","article-title":"Global sparse momentum SGD for pruning very deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Ding"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00890"},{"key":"ref29","first-page":"1","article-title":"Dynamic model pruning with feedback","volume-title":"Proc. 8th Int. Conf. Learn. Represent., (ICLR)","author":"Lin"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2996231"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2984569"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref33","first-page":"4646","article-title":"Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization","volume-title":"Proc. 36th Int. Conf. Mach. Learn., (ICML)","volume":"97","author":"Mostafa"},{"key":"ref34","article-title":"Sparse networks from scratch: Faster training without losing performance","author":"Dettmers","year":"2019","journal-title":"arXiv:1907.04840"},{"key":"ref35","first-page":"2943","article-title":"Rigging the lottery: Making all tickets winners","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Evci"},{"key":"ref36","first-page":"1","article-title":"To prune, or not to prune: Exploring the efficacy of pruning for model compression","volume-title":"Proc. 6th Int. Conf. Learn. Represent., (ICLR)","author":"Zhu"},{"key":"ref37","first-page":"2680","article-title":"Discovering neural wirings","volume-title":"Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst., (NeurIPS)","author":"Wortsman"},{"key":"ref38","first-page":"5544","article-title":"Soft threshold weight reparameterization for learnable sparsity","volume-title":"Proc. 37th Int. Conf. Mach. Learn., (ICML)","volume":"119","author":"Kusupati"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2911674"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3105820"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2938038"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2927295"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.3013170"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3071532"},{"issue":"3","key":"ref45","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF00992698","article-title":"Technical note Q-learning","volume":"8","author":"Watkins","year":"1992","journal-title":"Mach. Learn."},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_35"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"ref50","first-page":"1","article-title":"Nonparametric Bayesian inverse reinforcement learning for multiple reward functions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Choi"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/76\/9910246\/09758792.pdf?arnumber=9758792","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T21:05:55Z","timestamp":1705957555000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9758792\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":50,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2022.3167951","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10]]}}}