{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:26:22Z","timestamp":1763202382441,"version":"3.37.3"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100010677","name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","doi-asserted-by":"publisher","award":["769574"],"award-info":[{"award-number":["769574"]}],"id":[{"id":"10.13039\/100010677","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1109\/tnnls.2020.2984514","type":"journal-article","created":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T19:40:21Z","timestamp":1587411621000},"page":"1304-1313","source":"Crossref","is-referenced-by-count":6,"title":["Sparsely Activated Networks"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7659-2354","authenticated-orcid":false,"given":"Paschalis","family":"Bizopoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1205-9918","authenticated-orcid":false,"given":"Dimitrios","family":"Koutsouris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(64)90223-2"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020994407185"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"ref31","first-page":"2216","article-title":"The description length of deep learning models","author":"blier","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref37","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"ng","year":"2011","journal-title":"Cs294a lecture notes"},{"key":"ref36","first-page":"1","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Proc NIPS"},{"key":"ref35","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"ArXiv 1708 07747"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref10","first-page":"1","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"frankle","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"journal-title":"Rate-Distortion Theory","year":"1971","author":"burger","key":"ref40"},{"key":"ref11","first-page":"3177","article-title":"Net-trim: Convex pruning of deep neural networks with performance guarantee","author":"aghasi","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","first-page":"2181","article-title":"Runtime neural pruning","author":"lin","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","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":"ref14","doi-asserted-by":"publisher","DOI":"10.1126\/science.1089662"},{"key":"ref15","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref17","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref18","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref4","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":"ref28","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref3","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref27","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"simonyan","year":"2013","journal-title":"arXiv 1312 6034"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref7","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref9","article-title":"Understanding deep learning requires rethinking generalization","author":"zhang","year":"2016","journal-title":"arXiv 1611 03530"},{"key":"ref20","first-page":"17","article-title":"Maxout networks","volume":"28","author":"goodfellow","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref45","first-page":"2017","article-title":"Spatial transformer networks","author":"jaderberg","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/BF00160806"},{"key":"ref21","article-title":"K-sparse autoencoders","author":"makhzani","year":"2013","journal-title":"arXiv 1312 5663"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638346"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10827-006-0003-9"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref23","article-title":"Towards biologically plausible deep learning","author":"bengio","year":"2015","journal-title":"arXiv 1502 04156"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0182500"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2018.2885714"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1038\/381607a0"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10827-018-0693-9"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9365681\/09072295.pdf?arnumber=9072295","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:08Z","timestamp":1652194388000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9072295\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":45,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2020.2984514","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2021,3]]}}}