{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T11:27:04Z","timestamp":1763724424440,"version":"3.28.0"},"reference-count":19,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"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":[[2019,5]]},"DOI":"10.1109\/icra.2019.8794189","type":"proceedings-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T01:26:12Z","timestamp":1565659572000},"page":"8543-8549","source":"Crossref","is-referenced-by-count":6,"title":["Early Failure Detection of Deep End-to-End Control Policy by Reinforcement Learning"],"prefix":"10.1109","author":[{"given":"Keuntaek","family":"Lee","sequence":"first","affiliation":[]},{"given":"Kamil","family":"Saigol","sequence":"additional","affiliation":[]},{"given":"Evangelos A.","family":"Theodorou","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"journal-title":"What uncertainties do we need in bayesian deep learning for computer vision?","year":"2017","author":"kendall","key":"ref10"},{"journal-title":"Bayesian learning for neural networks","year":"1995","author":"neal","key":"ref11"},{"key":"ref12","first-page":"1613","article-title":"Weight uncertainty in neural network","volume":"37","author":"blundell","year":"2015","journal-title":"Proceedings of the 32nd International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"journal-title":"Improving Neural Networks by Preventing Co-adaptation of Feature Detectors","year":"2012","author":"hinton","key":"ref13"},{"key":"ref14","article-title":"Concrete dropout","author":"gal","year":"2017","journal-title":"Advances in Neural Information Processing Systems I (NIPS)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-005-5724-z"},{"journal-title":"TensorFlow Large-Scale Machine Learning on Heterogeneous Systems","year":"2015","author":"abadi","key":"ref16"},{"key":"ref17","article-title":"Adam: A Method for Stochastic Optimization","volume":"abs 1412 6980","author":"kingma","year":"2014","journal-title":"Proc of the Int Conf on Learning Representations (ICLR)"},{"journal-title":"OpenAI Gym","year":"2016","author":"brockman","key":"ref18"},{"key":"ref19","article-title":"AutoRally An open platform for aggressive autonomous driving","author":"goldfain","year":"2018","journal-title":"ArXiv Preprint"},{"journal-title":"Explaining how a deep neural network trained with end-to-end learning steers a car","year":"2017","author":"bojarski","key":"ref4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2018.XIV.056"},{"journal-title":"Dropoutdagger A bayesian approach to safe imitation learning","year":"2017","author":"menda","key":"ref6"},{"key":"ref5","article-title":"Deep predictive models for collision risk assessment in autonomous driving","volume":"abs 1711 10453","author":"strickland","year":"2017","journal-title":"CoRR"},{"key":"ref8","article-title":"Very deep convolutional networks for large-scale image recognition","volume":"abs 1409 1556","author":"simonyan","year":"2014","journal-title":"CoRR"},{"key":"ref7","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","volume":"48","author":"gal","year":"2016","journal-title":"Proceedings of The 33rd International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref2","article-title":"A reduction of imitation learning and structured prediction to no-regret online learning","volume":"15","author":"ross","year":"2011","journal-title":"Proceedings of the 14th International Conference on Artificial Intelligence and Statistics ser JMLR"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref1"},{"key":"ref9","first-page":"1465","article-title":"Receding horizon differential dynamic programming","author":"tassa","year":"2008","journal-title":"Advances in Neural Information Processing Systems 20"}],"event":{"name":"2019 International Conference on Robotics and Automation (ICRA)","start":{"date-parts":[[2019,5,20]]},"location":"Montreal, QC, Canada","end":{"date-parts":[[2019,5,24]]}},"container-title":["2019 International Conference on Robotics and Automation (ICRA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8780387\/8793254\/08794189.pdf?arnumber=8794189","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T03:15:11Z","timestamp":1657854911000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8794189\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5]]},"references-count":19,"URL":"https:\/\/doi.org\/10.1109\/icra.2019.8794189","relation":{},"subject":[],"published":{"date-parts":[[2019,5]]}}}