{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T13:06:54Z","timestamp":1750770414608,"version":"3.28.0"},"reference-count":37,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1109\/icdcs.2018.00041","type":"proceedings-article","created":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T22:46:16Z","timestamp":1532385976000},"page":"334-343","source":"Crossref","is-referenced-by-count":19,"title":["ApDeepSense: Deep Learning Uncertainty Estimation without the Pain for IoT Applications"],"prefix":"10.1109","author":[{"given":"Shuochao","family":"Yao","sequence":"first","affiliation":[]},{"given":"Yiran","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Huajie","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Aston","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dongxin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shengzhong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Su","sequence":"additional","affiliation":[]},{"given":"Tarek","family":"Abdelzaher","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1791212.1791244"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2012.6224648"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2014.6856422"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/11736790_1","article-title":"Evaluating predictive uncertainty challenge","volume":"3944","author":"quinonero-candela","year":"2006","journal-title":"Lecture Notes in Computer Science"},{"key":"ref37","first-page":"1019","article-title":"A theoretically grounded application of dropout in recurrent neural networks","author":"gal","year":"2016","journal-title":"Advances in neural information processing systems"},{"journal-title":"Bayesian convolutional neural networks with bernoulli approximate variational inference","year":"2015","author":"gal","key":"ref36"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131675"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2682005"},{"key":"ref10","article-title":"Efficient and scalable runtime monitoring for cyber-physical system","author":"zheng","year":"2016","journal-title":"IEEE Systems Journal"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2509434"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2016.7460719"},{"key":"ref13","first-page":"361","article-title":"Regions, periods, activities:Uncovering urban dynamics via cross-modal representation learning","author":"zhang","year":"2017","journal-title":"International Conference on World Wide Web International World Wide Web Conferences Steering Committee"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098027"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2018.2381131"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804262"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052577"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2971461"},{"journal-title":"Towards deep learning models for psychological state prediction using smartphone data Challenges and opportunities","year":"2017","author":"mikelsons","key":"ref19"},{"key":"ref28","first-page":"441","author":"l\u00e4uter","year":"1982","journal-title":"Loéve m Probability theory i and ii (graduate texts in mathematics 45 u 46)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2013.92"},{"key":"ref27","first-page":"1","article-title":"Graphical models, exponential families, and variational inference","volume":"1","author":"wainwright","year":"2008","journal-title":"Foundations and Trends\ufffd in Machine Learning"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2396276.2396281"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2012.6920959"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1049\/ip-cds:19971587"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1869983.1870012"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2014.6846750"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2461381.2461393"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/BSN.2013.6575484"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1464420.1464424"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2010.2065210"},{"journal-title":"The mythos of model interpretability","year":"2016","author":"lipton","key":"ref20"},{"key":"ref22","first-page":"26","article-title":"Rdeepsense: Reliable deep mobile computing models with uncertainty estimations","volume":"1","author":"yao","year":"2017","journal-title":"Proc ACM Interact Mob Wearable Ubiquitous Technol"},{"key":"ref21","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"International Conference on Machine Learning"},{"key":"ref24","article-title":"Dropout: a simple way to prevent neural networks from overfitting","author":"srivastava","year":"2014","journal-title":"JMLR"},{"journal-title":"Intel edison compute module","year":"0","key":"ref23"},{"key":"ref26","volume":"118","author":"neal","year":"2012","journal-title":"Bayesian learning for neural networks"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2003.08.020"}],"event":{"name":"2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)","start":{"date-parts":[[2018,7,2]]},"location":"Vienna","end":{"date-parts":[[2018,7,6]]}},"container-title":["2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8415923\/8416263\/08416304.pdf?arnumber=8416304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T14:55:16Z","timestamp":1643208916000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8416304\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/icdcs.2018.00041","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}