{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T14:29:57Z","timestamp":1730298597596,"version":"3.28.0"},"reference-count":16,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1109\/ssci.2018.8628706","type":"proceedings-article","created":{"date-parts":[[2019,2,28]],"date-time":"2019-02-28T23:15:32Z","timestamp":1551395732000},"page":"1374-1381","source":"Crossref","is-referenced-by-count":0,"title":["On the Use of Dropouts in Neural Networks for System Identification and Control"],"prefix":"10.1109","author":[{"given":"Shrikanth M.","family":"Yadav","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koshy","family":"George","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2514\/8.5282"},{"key":"ref11","article-title":"A gradient method for optimizing multi-stage allocation processes","author":"bryson","year":"0","journal-title":"Proceedings of the Harvard University Symposium on Digital Computers and Their Applications"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.2514\/3.25422"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2000.857892"},{"article-title":"Beyond regression: New tools for prediction and analysis in the behavioral sciences","year":"1974","author":"werbos","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.1999.830908"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr444"},{"key":"ref3","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"ref6","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"0","journal-title":"Proceedings of The 33rd International Conference on Machine Learning (ICML 16)"},{"article-title":"Uncertainty in deep learning","year":"2016","author":"gal","key":"ref5"},{"journal-title":"Stable Adaptive Systems","year":"1989","author":"narendra","key":"ref8"},{"key":"ref7","first-page":"1027","article-title":"A theoretically grounded application of dropout in recurrent neural networks","year":"0","journal-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS 16)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/72.80202"},{"journal-title":"System Identification Theory for the User","year":"1999","author":"ljung","key":"ref1"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/72.80336"}],"event":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","start":{"date-parts":[[2018,11,18]]},"location":"Bangalore, India","end":{"date-parts":[[2018,11,21]]}},"container-title":["2018 IEEE Symposium Series on Computational Intelligence (SSCI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8610062\/8628618\/08628706.pdf?arnumber=8628706","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T01:17:19Z","timestamp":1598231839000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8628706\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1109\/ssci.2018.8628706","relation":{},"subject":[],"published":{"date-parts":[[2018,11]]}}}