{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:22:00Z","timestamp":1772907720936,"version":"3.50.1"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["335\/18"],"award-info":[{"award-number":["335\/18"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018707","name":"Technion Hiroshi Fujiwara Cyber Security Research Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018707","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Israel Cyber Bureau"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,12,1]]},"DOI":"10.1109\/tpami.2021.3125041","type":"journal-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T19:28:44Z","timestamp":1636054124000},"page":"9222-9235","source":"Crossref","is-referenced-by-count":23,"title":["Ada-LISTA: Learned Solvers Adaptive to Varying Models"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0084-5022","authenticated-orcid":false,"given":"Aviad","family":"Aberdam","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Technion Institute of Technology, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7103-8047","authenticated-orcid":false,"given":"Alona","family":"Golts","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technion Institute of Technology, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8131-6928","authenticated-orcid":false,"given":"Michael","family":"Elad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technion Institute of Technology, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2791945"},{"key":"ref32","article-title":"Learning deep $\\ell _0$?0 encoders","volume":"30","author":"wang","year":"2016","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref31","first-page":"4340","article-title":"Maximal sparsity with deep networks?","author":"xin","year":"2016","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref30","article-title":"Deep unfolding: Model-based inspiration of novel deep architectures","author":"hershey","year":"2014"},{"key":"ref37","first-page":"416","article-title":"A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics","author":"martin","year":"2011","journal-title":"Proc Int Conf Comput Vis"},{"key":"ref36","article-title":"Learning step sizes for unfolded sparse coding","author":"ablin","year":"2019"},{"key":"ref35","first-page":"3981","article-title":"Learning to learn by gradient descent by gradient descent","author":"andrychowicz","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref34","first-page":"3424","article-title":"Accelerating eulerian fluid simulation with convolutional networks","author":"tompson","year":"0","journal-title":"Proc Int Conf Mach Learn 2017"},{"key":"ref10","first-page":"399","article-title":"Learning fast approximations of sparse coding","author":"gregor","year":"2010","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2008.2008065"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995524"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.55"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2013.07.005"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.911828"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6737048"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1137\/16M1102884"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2021.3049634"},{"key":"ref19","article-title":"Deep network classification by scattering and homotopy dictionary learning","author":"zarka","year":"2019"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2708040"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.50"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.20042"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974997"},{"key":"ref29","first-page":"10","article-title":"Deep admm-net for compressive sensing MRI","author":"sun","year":"2016","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1137\/050626090"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref7","first-page":"2287","article-title":"Spectral regularization algorithms for learning large incomplete matrices","volume":"11","author":"mazumder","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1137\/S003614450037906X"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2392779"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref20","article-title":"ALISTA: Analytic weights are as good as learned weights in LISTA","author":"liu","year":"2019","journal-title":"Proc Int Conf Learn Representation"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1137\/060657704"},{"key":"ref21","article-title":"Sparse coding with gated learned {ISTA}","author":"wu","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref24","first-page":"9061","article-title":"Theoretical linear convergence of unfolded ista and its practical weights and thresholds","author":"chen","year":"2018","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2904255"},{"key":"ref26","first-page":"1772","article-title":"Learned d-amp: Principled neural network based compressive image recovery","author":"metzler","year":"2017","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00196"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9940446\/09601284.pdf?arnumber=9601284","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T22:37:20Z","timestamp":1670279840000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9601284\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":37,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2021.3125041","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]}}}