{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T12:18:58Z","timestamp":1730204338531,"version":"3.28.0"},"reference-count":35,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"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":[],"published-print":{"date-parts":[[2021,1,27]]},"DOI":"10.1109\/ccwc51732.2021.9376167","type":"proceedings-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T16:17:39Z","timestamp":1615997859000},"page":"1541-1550","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Evolution of Variational Autoencoders"],"prefix":"10.1109","author":[{"given":"Jeff","family":"Hajewski","sequence":"first","affiliation":[]},{"given":"Suely","family":"Oliveira","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"year":"2017","author":"xiao","journal-title":"Fashion-mnist a novel image dataset for benchmarking machine learning algorithms","key":"ref33"},{"year":"2010","author":"lecun","journal-title":"MNIST Handwritten Digit Database","key":"ref32"},{"key":"ref31","first-page":"7","article-title":"Distributed smsvm ensemble learning","author":"hajewski","year":"2019","journal-title":"Recent Advances in Big Data and Deep Learning Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL 2019 held at Sestri Levante"},{"key":"ref30","article-title":"Hierarchical representations for efficient architecture search","author":"liu","year":"2017","journal-title":"CoRR vol abs\/1711 00436"},{"key":"ref35","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"0","journal-title":"NIPS Autodiff Workshop"},{"year":"2018","author":"clanuwat","journal-title":"Deep learning for classical Japanese literature","key":"ref34"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1145\/3065386"},{"key":"ref12","article-title":"Dawnbench: An end-to-end deep learning benchmark and competition","author":"coleman","year":"0","journal-title":"NIPS ML Systems Workshop"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1145\/3352020.3352024"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1145\/3200947.3208068"},{"key":"ref15","first-page":"4095","article-title":"Efficient neural architecture search via parameters sharing","volume":"80","author":"pham","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref16","article-title":"Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning","author":"such","year":"2017","journal-title":"CoRR vol abs\/1712 06567"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1145\/3140659.3080246"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1007\/3-540-49430-8_3"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1007\/s00365-006-0663-2"},{"key":"ref28","article-title":"Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation","author":"liu","year":"1901","journal-title":"CoRR"},{"key":"ref4","article-title":"Auto-encoding variational bayes","author":"kingma","year":"0","journal-title":"2nd International Conference on Learning Representations ICLR 2014 Banff AB Canada April 14&#x2013;16 2014 Conference Track Proceedings"},{"key":"ref27","article-title":"Differentiable neural network architecture search","author":"shin","year":"0","journal-title":"6th International Conference on Learning Representations ICLR 2018 Vancouver BC Canada April 30 - May 3 2018 Workshop Track Proceedings"},{"key":"ref3","article-title":"Evolving deep neural networks","author":"miikkulainen","year":"2017","journal-title":"CoRR vol abs\/1703 00548"},{"key":"ref6","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"3rd International Conference on Learning Representations ICLR 2015"},{"key":"ref29","first-page":"2902","article-title":"Large-scale evolution of image classifiers","volume":"70","author":"real","year":"0","journal-title":"Proceedings of the 34th International Conference on Machine Learning ICML 2017 Sydney NSW Australia 6&#x2013;11 August 2017 ser Proceedings of Machine Learning Research"},{"key":"ref5","article-title":"Empirical evaluation of rectified activations in convolutional network","author":"xu","year":"2015","journal-title":"CoRR"},{"key":"ref8","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"0","journal-title":"Advances in Neural Information Processing Systems 27"},{"key":"ref7","first-page":"2017","article-title":"Spatial transformer networks","author":"jaderberg","year":"0","journal-title":"Advances in Neural IInformation Processing Systems"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1145\/2463372.2463509"},{"key":"ref9","article-title":"Rethinking the inception architecture for computer vision","author":"szegedy","year":"2015","journal-title":"CoRR vol abs\/1512 00567"},{"year":"2017","author":"zoph","journal-title":"Neural architecture search with reinforcement learning","key":"ref1"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1145\/1390156.1390294"},{"key":"ref22","first-page":"340","article-title":"Letter to the editor: The kullback-leibler distance","volume":"41","author":"kullback","year":"1987","journal-title":"The American Statistician"},{"key":"ref21","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"vincent","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref24","article-title":"SMASH: one-shot model architecture search through hypernet-works","author":"brock","year":"2017","journal-title":"CoRR vol abs\/1708 05344"},{"key":"ref23","article-title":"Hypernetworks","author":"ha","year":"2016","journal-title":"CoRR vol abs\/1609 09106"},{"key":"ref26","article-title":"DARTS: differentiable architecture search","author":"liu","year":"2018","journal-title":"CoRR vol abs\/1806 09055"},{"key":"ref25","article-title":"Graph hyper-networks for neural architecture search","author":"zhang","year":"2018","journal-title":"CoRR vol abs\/1810 05749"}],"event":{"name":"2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)","start":{"date-parts":[[2021,1,27]]},"location":"NV, USA","end":{"date-parts":[[2021,1,30]]}},"container-title":["2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9375825\/9375905\/09376167.pdf?arnumber=9376167","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:43:48Z","timestamp":1652183028000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9376167\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,27]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/ccwc51732.2021.9376167","relation":{},"subject":[],"published":{"date-parts":[[2021,1,27]]}}}