{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:29Z","timestamp":1760245049167,"version":"3.37.3"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2018]]},"DOI":"10.1109\/access.2018.2863606","type":"journal-article","created":{"date-parts":[[2018,8,6]],"date-time":"2018-08-06T22:19:24Z","timestamp":1533593964000},"page":"42961-42970","source":"Crossref","is-referenced-by-count":8,"title":["Bridgeout: Stochastic Bridge Regularization for Deep Neural Networks"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7465-9698","authenticated-orcid":false,"given":"Najeeb","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jawad","family":"Shah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian","family":"Stavness","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref38"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref32","first-page":"1","article-title":"Cs229 lecture notes","volume":"1","author":"ng","year":"2000","journal-title":"CS229 Lecture Notes"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/11503415_37"},{"journal-title":"Weight uncertainty in neural networks","year":"2015","author":"blundell","key":"ref30"},{"journal-title":"Fashion-mnist a novel image dataset for benchmarking machine learning algorithms","year":"2017","author":"xiao","key":"ref37"},{"key":"ref36","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"journal-title":"Adam A method for stochastic optimization","year":"2014","author":"kingma","key":"ref34"},{"key":"ref10","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"Understanding deep learning requires rethinking generalization","year":"2016","author":"zhang","key":"ref40"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.2307\/1267351"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2010.2044470"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"tibshirani","year":"1996","journal-title":"J Roy Statist Soc Ser B Stat Methodol"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jspi.2011.05.004"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2007.02.006"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-010-0090-0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.10.023"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.06.061"},{"key":"ref28","first-page":"28","article-title":"Swapout: Learning an ensemble of deep architectures","author":"singh","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","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":"ref27","first-page":"2","article-title":"Dropout as a Bayesian approximation: Insights and applications","author":"gal","year":"2015","journal-title":"Deep Learning Workshop (ICML)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/72.248452"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1993.10485033"},{"key":"ref29","first-page":"1","article-title":"Practical variational inference for neural networks","author":"graves","year":"2011","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref5","first-page":"1751","article-title":"Shakeout: A new regularized deep neural network training scheme","author":"kang","year":"2016","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref8","first-page":"107","article-title":"Skeletonization: A technique for trimming the fat from a network via relevance assessment","author":"mozer","year":"1989","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref2"},{"key":"ref9","first-page":"598","article-title":"Optimal brain damage","volume":"2","author":"lecun","year":"1989","journal-title":"Proc NIPS"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2347736.2347755"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/02664760500079464"},{"key":"ref22","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639346"},{"key":"ref24","first-page":"351","article-title":"Dropout training as adaptive regularization","author":"wager","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","volume":"118","author":"neal","year":"2012","journal-title":"Bayesian learning for neural networks"},{"key":"ref26","first-page":"3084","article-title":"Adaptive dropout for training deep neural networks","author":"ba","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","first-page":"1058","article-title":"Regularization of neural networks using dropconnect","author":"wan","year":"2013","journal-title":"Proc Int Conf Mach Learn (ICML)"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8274985\/08425643.pdf?arnumber=8425643","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T23:43:19Z","timestamp":1720482199000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8425643\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2863606","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2018]]}}}