{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T19:38:23Z","timestamp":1730230703925,"version":"3.28.0"},"reference-count":43,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1109\/icassp40776.2020.9054436","type":"proceedings-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T20:21:13Z","timestamp":1586463673000},"page":"5410-5414","source":"Crossref","is-referenced-by-count":2,"title":["SSGD: Sparsity-Promoting Stochastic Gradient Descent Algorithm for Unbiased Dnn Pruning"],"prefix":"10.1109","author":[{"given":"Ching-Hua","family":"Lee","sequence":"first","affiliation":[]},{"given":"Igor","family":"Fedorov","sequence":"additional","affiliation":[]},{"given":"Bhaskar D.","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Harinath","family":"Garudadri","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-18842-3"},{"key":"ref38","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2017.8081202"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9048716"},{"journal-title":"Linear and Nonlinear Programming","year":"1996","author":"nash","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/78.738251"},{"key":"ref37","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref36","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Univ of Toronto Technical Report"},{"journal-title":"The MNIST Database of Handwritten Digits","year":"1998","author":"lecun","key":"ref35"},{"article-title":"Re-weighted learning for sparsifying deep neural networks","year":"2018","author":"fedorov","key":"ref34"},{"key":"ref10","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"Int Conf Learning Rep (ICLR)"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/89.861368"},{"key":"ref11","article-title":"Pruning filters for efficient ConvNets","author":"li","year":"2017","journal-title":"Int Conf Learning Rep (ICLR)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952583"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765695"},{"key":"ref15","first-page":"784","article-title":"AMC: AutoML for model compression and acceleration on mobile devices","author":"he","year":"2018","journal-title":"Proc Eur Conf Comput Vision (ECCV)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/78.558475"},{"key":"ref18","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"tipping","year":"2001","journal-title":"J Mach Learn Res"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2008.4518498"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682464"},{"key":"ref4","first-page":"2270","article-title":"Learning the number of neurons in deep networks","author":"alvarez","year":"2016","journal-title":"Adv Neural Inform Process Syst (NIPS)"},{"key":"ref27","first-page":"3966","article-title":"Learning compact neural networks with regularization","author":"oymak","year":"2018","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2018.2842159"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.029"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2010.2042413"},{"key":"ref5","first-page":"2148","article-title":"Predicting parameters in deep learning","author":"denil","year":"2013","journal-title":"Adv Neural Inform Process Syst (NIPS)"},{"key":"ref8","first-page":"1135","article-title":"Learning both weights and connections for efficient neural networks","author":"han","year":"2015","journal-title":"Adv Neural Inform Process Syst (NIPS)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2951145"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref9","first-page":"1379","article-title":"Dynamic network surgery for efficient DNNs","author":"guo","year":"2016","journal-title":"Adv Neural Inform Process Syst (NIPS)"},{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Adv Neural Inform Process Syst (NIPS)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s00041-008-9045-x"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.280"},{"key":"ref21","first-page":"806","article-title":"Sparse convolutional neural networks","author":"liu","year":"2015","journal-title":"Proc IEEE Comp Vision Pattern Recogn Conf (CVPR)"},{"article-title":"Adam: A method for stochastic optimization","year":"0","author":"kingma","key":"ref42"},{"key":"ref24","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"Adv Neural Inform Process Syst (NIPS)"},{"key":"ref41","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Neural Inform Process Syst Workshop (NIPS-W)"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_40"},{"key":"ref26","article-title":"Learning sparse neural networks through L0 regularization","author":"louizos","year":"2018","journal-title":"Int Conf Learning Rep (ICLR)"},{"key":"ref43","first-page":"3177","article-title":"Net-Trim: Convex pruning of deep neural networks with performance guarantee","author":"aghasi","year":"2017","journal-title":"Adv Neural Inform Process Syst (NIPS)"},{"key":"ref25","first-page":"3288","article-title":"Bayesian compression for deep learning","author":"louizos","year":"2017","journal-title":"Adv Neural Inform Process Syst (NIPS)"}],"event":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2020,5,4]]},"location":"Barcelona, Spain","end":{"date-parts":[[2020,5,8]]}},"container-title":["ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9040208\/9052899\/09054436.pdf?arnumber=9054436","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:14:58Z","timestamp":1656375298000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9054436\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/icassp40776.2020.9054436","relation":{},"subject":[],"published":{"date-parts":[[2020,5]]}}}