{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T18:16:15Z","timestamp":1778264175793,"version":"3.51.4"},"reference-count":116,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"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":["Proc. IEEE"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1109\/jproc.2023.3247480","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T18:25:30Z","timestamp":1677695130000},"page":"465-499","source":"Crossref","is-referenced-by-count":297,"title":["Model-Based Deep Learning"],"prefix":"10.1109","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2234-929X","authenticated-orcid":false,"given":"Nir","family":"Shlezinger","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering (ECE), Ben-Gurion University of the Negev, Be’er Sheva, Israel"}]},{"given":"Jay","family":"Whang","sequence":"additional","affiliation":[{"name":"Department of Computer Science (CS), The University of Texas at Austin, Austin, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4358-5304","authenticated-orcid":false,"given":"Yonina C.","family":"Eldar","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science (CS), Weizmann Institute of Science, Rehovot, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4244-7033","authenticated-orcid":false,"given":"Alexandros G.","family":"Dimakis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering (ECE), The University of Texas at Austin, Austin, TX, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/nature24270"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1724-z"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3016905"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104374"},{"key":"ref9","first-page":"6828","article-title":"Learning a compressed sensing measurement matrix via gradient unrolling","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref10","first-page":"537","article-title":"Compressed sensing using generative models","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (JMLR)","volume":"70","author":"Bora"},{"key":"ref11","article-title":"Compressed sensing with invertible generative models and dependent noise","volume-title":"Proc. Deep Learn. Inverse Problems NeurIPS Workshop","author":"Whang"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2019.2948732"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6737048"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2865356"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2972352"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3032663"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2788405"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2899805"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2976585"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2996144"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3045199"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.4310\/CIS.2020.v20.n3.a2"},{"key":"ref23","first-page":"685","article-title":"Neural enhanced belief propagation on factor graphs","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Satorras"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2019.2921627"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2924010"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2957798"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2017.2758370"},{"key":"ref28","article-title":"Communication algorithms via deep learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kim"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3043502"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref31","first-page":"1772","article-title":"Learned D-AMP: Principled neural network based compressive image recovery","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Metzler"},{"key":"ref32","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref34","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vaswani"},{"issue":"10","key":"ref35","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume-title":"The Handbook of Brain Theory and Neural Networks","volume":"3361","author":"LeCun","year":"1995"},{"issue":"2","key":"ref36","first-page":"26","article-title":"Lecture 6.5-RMSPROP: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"Tieleman","year":"2012","journal-title":"COURSERA, Neural Netw. Mach. Learn."},{"key":"ref37","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"key":"ref40","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref41","first-page":"5767","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Gulrajani"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref43","article-title":"Geometric GAN","author":"Lim","year":"2017","journal-title":"arXiv:1705.02894"},{"key":"ref44","article-title":"The relativistic discriminator: A key element missing from standard GAN","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Jolicoeur-Martineau"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"issue":"2","key":"ref47","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Workshop Challenges Represent. Learn., ICML","volume":"3","author":"Lee"},{"key":"ref48","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Laine"},{"key":"ref49","first-page":"5050","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref51","first-page":"9493","article-title":"Convolutional dictionary learning based auto-encoders for natural exponential-family distributions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tolooshams"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9415083"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3218802"},{"key":"ref54","first-page":"841","article-title":"On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"14","author":"Ng"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2023.3271431"},{"key":"ref56","article-title":"Deep unfolding: Model-based inspiration of novel deep architectures","author":"Hershey","year":"2014","journal-title":"arXiv:1409.2574"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2020.2964202"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2941271"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3018802"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2019.8918878"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/SiPS47522.2019.9020494"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2927997"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3033334"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3117503"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TUFFC.2020.3026598"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1364\/OE.401925"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"ref68","first-page":"5632","article-title":"Unfolding the alternating optimization for blind super resolution","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Huang"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1137\/140979861"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2859044"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_78"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746637"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3193201"},{"key":"ref74","article-title":"CausalGAN: Learning causal implicit generative models with adversarial training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kocaoglu"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2000.904647"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2991563"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1561\/9781601984616"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-010-0251-1"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2949470"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.300"},{"key":"ref82","first-page":"5546","article-title":"Plug-and-play methods provably converge with properly trained denoisers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ryu"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2710233"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2763583"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.198"},{"key":"ref86","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"arXiv:1511.06434"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref88","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013","journal-title":"arXiv:1312.6114"},{"key":"ref89","volume-title":"MNIST Handwritten Digit Database","author":"LeCun","year":"2010"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2839891"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT44484.2020.9174361"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2762355"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_25"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00792"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3008537"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3158588"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/ICAS49788.2021.9551175"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2557301"},{"key":"ref99","volume-title":"Adaptive Filter Theory","author":"Haykin","year":"2005"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1054010"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3139506"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/18.910572"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2868322"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3241841"},{"key":"ref105","first-page":"13802","article-title":"Combining generative and discriminative models for hybrid inference","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Satorras"},{"key":"ref106","first-page":"10471","article-title":"Neural enhanced dynamic message passing","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Gao"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9048920"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414141"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746487"},{"key":"ref110","first-page":"9079","article-title":"Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3125282"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.001.2200152"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.862083"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2004.1267047"}],"container-title":["Proceedings of the IEEE"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5\/10127106\/10056957.pdf?arnumber=10056957","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T01:13:31Z","timestamp":1705022011000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10056957\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":116,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/jproc.2023.3247480","relation":{},"ISSN":["0018-9219","1558-2256"],"issn-type":[{"value":"0018-9219","type":"print"},{"value":"1558-2256","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]}}}