{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:10:07Z","timestamp":1772302207460,"version":"3.50.1"},"reference-count":29,"publisher":"IEEE","funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,25]]},"DOI":"10.23919\/acc50511.2021.9482874","type":"proceedings-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T20:29:16Z","timestamp":1627504156000},"page":"2824-2829","source":"Crossref","is-referenced-by-count":4,"title":["A Convex Parameterization of Robust Recurrent Neural Networks"],"prefix":"10.23919","author":[{"given":"Max","family":"Revay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruigang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian R.","family":"Manchester","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2003.814273"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2013.04.028"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2018.06.036"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2694820"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2017.7963165"},{"key":"ref15","first-page":"11128","article-title":"Learning stable deep dynamics models","author":"kolter","year":"0","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1346-8"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/72.991416"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/9.751342","article-title":"Bounds of the induced norm and model reduction errors for systems with repeated scalar nonlinearities","volume":"44","author":"chu","year":"1999","journal-title":"IEEE Transactions on Automatic Control"},{"key":"ref19","first-page":"11423","article-title":"Efficient and accurate estimation of lipschitz constants for deep neural networks","author":"fazlyab","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-002-0352-8"},{"key":"ref4","volume":"55","author":"desoer","year":"1975","journal-title":"Feedback Systems Input-Output Properties"},{"key":"ref27","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"frankle","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1966.1098316"},{"key":"ref6","first-page":"6240","article-title":"Spectrally-normalized margin bounds for neural networks","author":"bartlett","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref29","article-title":"Adam: A Method for Stochastic Optimization","author":"kingma","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-1098(98)00019-3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-13453-2_2"},{"key":"ref7","author":"zhou","year":"2019","journal-title":"An analysis of the expressiveness of deep neural network architectures based on their Lipschitz constants"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5767"},{"key":"ref9","article-title":"L2-nonexpansive neural networks","author":"qian","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref20","article-title":"Stable recurrent models","author":"miller","year":"0","journal-title":"Proceedings of International Conference on Learning Representations 2019"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2018.2867358"},{"key":"ref21","article-title":"Contracting implicit recurrent neural networks: Stable models with improved trainability","author":"revay","year":"0","journal-title":"Learning for Dynamics and Control"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/0364-0213(90)90002-E"},{"key":"ref23","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492900002919"}],"event":{"name":"2021 American Control Conference (ACC)","location":"New Orleans, LA, USA","start":{"date-parts":[[2021,5,25]]},"end":{"date-parts":[[2021,5,28]]}},"container-title":["2021 American Control Conference (ACC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9482409\/9482614\/09482874.pdf?arnumber=9482874","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T20:54:58Z","timestamp":1635800098000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9482874\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,25]]},"references-count":29,"URL":"https:\/\/doi.org\/10.23919\/acc50511.2021.9482874","relation":{},"subject":[],"published":{"date-parts":[[2021,5,25]]}}}