{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T09:25:45Z","timestamp":1780046745174,"version":"3.53.1"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100008536","name":"Amazon Web Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008536","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/tsp.2020.3014716","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T21:13:38Z","timestamp":1597094018000},"page":"4727-4742","source":"Crossref","is-referenced-by-count":3,"title":["Deeply-Sparse Signal rePresentations (D$\\text{S}^2$P)"],"prefix":"10.1109","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1139-1030","authenticated-orcid":false,"given":"Demba","family":"Ba","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1974.223784"},{"key":"ref38","article-title":"Sparse coding of natural images produces localized, oriented, bandpass receptive fields","volume":"381","author":"olshausen","year":"1996","journal-title":"Nature"},{"key":"ref33","year":"2016","journal-title":"Dask Library for dynamic task scheduling"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553417"},{"key":"ref31","article-title":"The MOSEK optimization software","year":"2018"},{"key":"ref30","first-page":"1","article-title":"CVXPY: A Python-embedded modeling language for convex optimization","volume":"17","author":"diamond","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2001.937655"},{"key":"ref36","first-page":"9061","article-title":"Theoretical linear convergence of unfolded ista and its practical weights and thresholds","author":"chen","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref35","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf on Learning Rep"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2904255"},{"key":"ref10","article-title":"Discriminative recurrent sparse auto-encoders","author":"rolfe","year":"0"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2018.8516996"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2019.8918878"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1017\/S0021900200012547","article-title":"New results on a generalized coupon collector problem using Markov chains","volume":"52","author":"anceaume","year":"0","journal-title":"J Appl Prob"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.crma.2008.03.014"},{"key":"ref15","article-title":"Introduction to the non-asymptotic analysis of random matrices","author":"vershynin","year":"2010"},{"key":"ref16","article-title":"Learned convolutional sparse coding","author":"sreter","year":"2017"},{"key":"ref17","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"0","journal-title":"Proc Fourteenth Int Conf Artif Intell Statist"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3005348"},{"key":"ref28","first-page":"37","article-title":"Exact recovery of sparsely-used dictionaries","author":"spielman","year":"0","journal-title":"Proc Conf Learn Theory"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2846226"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2357773"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2733447"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ITW.2015.7133169"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2016.2517006"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1137\/17M1141771"},{"key":"ref8","first-page":"2858","article-title":"On the dynamics of gradient descent for autoencoders","author":"nguyen","year":"0","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1137\/140979861"},{"key":"ref2","first-page":"2887","article-title":"Convolutional neural networks analyzed via convolutional sparse coding","volume":"18","author":"papyan","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref9","first-page":"399","article-title":"Learning fast approximations of sparse coding","author":"gregor","year":"0","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref1","article-title":"A probabilistic theory of deep learning","author":"patel","year":"2015"},{"key":"ref20","author":"theodoridis","year":"2015","journal-title":"Machine Learning A Bayesian and Optimization Perspective"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2012.03.006"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/18M1183352"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.029"},{"key":"ref23","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2357776"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178579"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/8933520\/09163297.pdf?arnumber=9163297","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T14:41:18Z","timestamp":1651070478000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9163297\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/tsp.2020.3014716","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"value":"1053-587X","type":"print"},{"value":"1941-0476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}