{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T20:05:54Z","timestamp":1769457954676,"version":"3.49.0"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"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":["IEEE Comput. Intell. Mag."],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1109\/mci.2021.3084415","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T20:09:01Z","timestamp":1626898141000},"page":"33-49","source":"Crossref","is-referenced-by-count":21,"title":["Self-Supervised Representation Learning for Evolutionary Neural Architecture Search"],"prefix":"10.1109","volume":"16","author":[{"given":"Chen","family":"Wei","sequence":"first","affiliation":[]},{"given":"Yiping","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Chuang Niu","family":"Chuang Niu","sequence":"additional","affiliation":[]},{"given":"Haihong","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jimin","family":"Liang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290967"},{"key":"ref38","first-page":"3835","article-title":"Graph matching networks for learning the similarity of graph structured objects","author":"li","year":"2019","journal-title":"Proc 36th Int Conf Machine Learning ICML 2019"},{"key":"ref33","first-page":"2016","article-title":"Neural architecture search with Bayesian optimisation and optimal transport","author":"kandasamy","year":"2018","journal-title":"Proc Adv Neural Information Process Syst 31 Annu Conf Neural Information Process Syst 2018 NeurIPS 2018"},{"key":"ref32","article-title":"PC-DARTS: Partial channel connections for memory-efficient architecture search","author":"xu","year":"2020","journal-title":"8th International Conference on Learning Representations ICLR 2020"},{"key":"ref31","first-page":"1294","author":"chen","year":"2019","journal-title":"Progressive differentiable architecture search Bridging the depth gap between search and evaluation"},{"key":"ref30","article-title":"SNAS: Stochastic neural architecture search","author":"xie","year":"2019","journal-title":"Proc 7th Int Conf Learning Representations ICLR 2019"},{"key":"ref37","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2014","journal-title":"Proc 2nd Int Conf Learning Representations ICLR 2014"},{"key":"ref36","article-title":"Accelerating neural architecture search using performance prediction","author":"baker","year":"2018","journal-title":"Proc 6th Int Conf Learning Representations ICLR 2018"},{"key":"ref35","article-title":"BRP-NAS: Prediction-based NAS using GCNS","author":"dudziak","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst 33 Annu Conf Neural Inf Process Syst 2020"},{"key":"ref34","article-title":"Semi-supervised neural architecture search","author":"luo","year":"2020","journal-title":"Proc Adv Neural Information Processing Syst 33 Annu Conf Neural Information Process Syst 2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2916183"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2919608"},{"key":"ref29","first-page":"7603","article-title":"Bayesnas: A Bayesian approach for neural architecture search","volume":"97","author":"zhou","year":"2019","journal-title":"Proc 36th Int Conf Machine Learning ICML 2019"},{"key":"ref2","first-page":"19","article-title":"Progressive neural architecture search","volume":"11205","author":"liu","year":"0","journal-title":"Proc Computer Vision&#x2014;ECCV 2018&#x2014;15th European Conf"},{"key":"ref1","first-page":"55:1","article-title":"Neural architecture search: A survey","volume":"20","author":"elsken","year":"2018","journal-title":"J Mach Learning Res"},{"key":"ref20","article-title":"DARTS: Differentiable architecture search","author":"liu","year":"2019","journal-title":"Proc 7th Int Conf Learning Representations ICLR 2019"},{"key":"ref22","first-page":"4092","article-title":"Efficient neural architecture search via parameter sharing","volume":"80","author":"pham","year":"2018","journal-title":"Proc 35th Int Conf Machine Learning ICML 2018"},{"key":"ref21","article-title":"Neural architecture search with reinforcement learning","author":"zoph","year":"2017","journal-title":"Proc 5th Int Conf Learning Representations ICLR 2017"},{"key":"ref24","first-page":"2902","article-title":"Large-scale evolution of image classifiers","volume":"70","author":"real","year":"2017","journal-title":"Proc 34th Int Conf Machine Learning ICML 2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2983860"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"ref50","first-page":"129","article-title":"Random search and reproducibility for neural architecture search","author":"li","year":"2019","journal-title":"Proc 35th Conf Uncertainty Artif Intell UAI 2019"},{"key":"ref51","first-page":"7827","article-title":"Neural architecture optimization","author":"luo","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst 31 Annu Conf Neural Inf Process Syst 2018 NeurIPS 2018"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref11","first-page":"4171","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"Proc Conf North American Chapter Assoc Comput Linguistics Human Language Technol (NAACL-HLT 2019)"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/275"},{"key":"ref12","article-title":"Self-labelling via simultaneous clustering and representation learning","author":"asano","year":"2020","journal-title":"Proc 8th Int Conf Learning Representations ICLR 2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref14","author":"cheng","year":"2020","journal-title":"NASGEM Neural architecture search via graph embedding method"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref16","article-title":"Unsupervised representation learning by predicting image rotations","author":"gidaris","year":"2018","journal-title":"Proc 6th Int Conf Learning Representations ICLR 2018"},{"key":"ref17","first-page":"7105","article-title":"NAS-bench-101: Towards reproducible neural architecture search","volume":"97","author":"ying","year":"2019","journal-title":"Proc 36th Int Conf Machine Learning ICML 2019"},{"key":"ref18","article-title":"Graph structure of neural networks","author":"you","year":"2020","journal-title":"Proc 37th Int Conf Machine Learning ICML 2020"},{"key":"ref19","article-title":"NAS-Bench-201: Extending the scope of reproducible neural architecture search","author":"dong","year":"2020","journal-title":"Proc 8th Int Conf Learning Representations ICLR 2020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i06.6554"},{"key":"ref3","author":"white","year":"2019","journal-title":"Bananas Bayesian optimization with neural architectures for neural architecture search"},{"key":"ref6","author":"wei","year":"2020","journal-title":"NPENAS Neural predictor guided evolution for neural architecture search"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_12"},{"key":"ref8","article-title":"Does unsupervised architecture representation learning help neural architecture search?","author":"yan","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst 33 Annu Conf Neural Inf Process Syst 2020"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_39"},{"key":"ref49","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"loshchilov","year":"2017","journal-title":"Proc 5th Int Conf Learning Representations ICLR 2017"},{"key":"ref9","author":"chen","year":"2020","journal-title":"A simple framework for contrastive learning of visual representations"},{"key":"ref46","author":"krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref45","author":"wei","year":"2020","journal-title":"Code for self-supervised representation learning for evolutionary neural architecture search"},{"key":"ref48","article-title":"Adam: A method for stochastic optimization","volume":"abs 1412 6980","author":"kingma","year":"2014","journal-title":"CoRR"},{"key":"ref47","first-page":"1106","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Information Process Syst 25 26th Annu Conf Neural Information Process Syst 2012 Proc Meeting"},{"key":"ref42","author":"caron","year":"2020","journal-title":"Unsupervised learning of visual features by contrasting cluster assignments"},{"key":"ref41","article-title":"How powerful are graph neural networks?","author":"xu","year":"2019","journal-title":"Proc 7th Int Conf Learning Representations ICLR 2019"},{"key":"ref44","article-title":"Fast graph representation learning with PyTorch Geometric","author":"fey","year":"0","journal-title":"Proc Int Conf Learning Representations Workshop Representation Learning Graphs and Manifolds"},{"key":"ref43","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Information Processing Syst 32 Annu Conf Neural Information Process Syst 2019 NeurIPS 2019"}],"container-title":["IEEE Computational Intelligence Magazine"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10207\/9491851\/09492145.pdf?arnumber=9492145","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:52:20Z","timestamp":1652194340000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9492145\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":51,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/mci.2021.3084415","relation":{},"ISSN":["1556-603X","1556-6048"],"issn-type":[{"value":"1556-603X","type":"print"},{"value":"1556-6048","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8]]}}}