{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:17:13Z","timestamp":1781741833548,"version":"3.54.5"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2016,5,1]],"date-time":"2016-05-01T00:00:00Z","timestamp":1462060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Signal Process. Lett."],"published-print":{"date-parts":[[2016,5]]},"DOI":"10.1109\/lsp.2016.2548245","type":"journal-article","created":{"date-parts":[[2016,3,29]],"date-time":"2016-03-29T22:29:36Z","timestamp":1459290576000},"page":"747-751","source":"Crossref","is-referenced-by-count":78,"title":["Learning Optimal Nonlinearities for Iterative Thresholding Algorithms"],"prefix":"10.1109","volume":"23","author":[{"given":"Ulugbek S.","family":"Kamilov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Mansour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2015.7282734"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression and selection via the lasso","volume":"58","author":"tibshirani","year":"1996","journal-title":"J R Statist Soc B (Methodological)"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/5.843002"},{"key":"ref10","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","author":"bishop","year":"1995","journal-title":"Neural Networks for Pattern Recognition"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.909319"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382273"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s00041-008-9035-z"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2011.09.017"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2309076"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2412915"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0909892106"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ITWKSPS.2010.5503193"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2011.6033942"},{"key":"ref28","article-title":"Online convex programming and generalized infinitesimal gradient ascent","author":"zinkevich","year":"0","journal-title":"Proc 20th Int Conf Mach Learn (ICML)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.814255"},{"key":"ref27","first-page":"1","article-title":"Learning activation functions to improve deep neural networks","author":"agostinelli","year":"2014"},{"key":"ref3","author":"bauschke","year":"2010","journal-title":"Convex Analysis and Monotone Operator Theory in Hilbert Spaces"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.20042"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/79.799930"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24673-2_1"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/BF01581204"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.862083"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2014.2309005"},{"key":"ref22","first-page":"399","article-title":"Learning fast approximation of sparse coding","author":"gregor","year":"0","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref21","first-page":"198","article-title":"Sparsity-driven statistical inference for inverse problems","author":"kamilov","year":"2015"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.349"},{"key":"ref23","first-page":"615","article-title":"Learning efficient structured sparse models","author":"sprechmann","year":"0","journal-title":"Proc 29th Int Conf Mach Learn (ICML)"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2015.7447163"},{"key":"ref25","first-page":"5261","article-title":"On learning optimized reaction diffuction processes for effective image restoration","author":"chen","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"}],"container-title":["IEEE Signal Processing Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/97\/7439893\/07442798.pdf?arnumber=7442798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T10:18:13Z","timestamp":1718446693000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/7442798\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,5]]},"references-count":32,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/lsp.2016.2548245","relation":{},"ISSN":["1070-9908","1558-2361"],"issn-type":[{"value":"1070-9908","type":"print"},{"value":"1558-2361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,5]]}}}