{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T16:02:41Z","timestamp":1742400161821,"version":"3.37.3"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100004356","name":"Nokia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004356","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012338","name":"Alan Turing Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,23]]},"DOI":"10.1109\/icassp43922.2022.9746330","type":"proceedings-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T19:50:34Z","timestamp":1651089034000},"page":"3998-4002","source":"Crossref","is-referenced-by-count":2,"title":["Robust and Efficient Uncertainty Aware Biosignal Classification via Early Exit Ensembles"],"prefix":"10.1109","author":[{"given":"Alexander","family":"Campbell","sequence":"first","affiliation":[{"name":"University of Cambridge"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorena","family":"Qendro","sequence":"additional","affiliation":[{"name":"University of Cambridge"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Lio","sequence":"additional","affiliation":[{"name":"University of Cambridge"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cecilia","family":"Mascolo","sequence":"additional","affiliation":[{"name":"University of Cambridge"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"181","article-title":"Early exit ensembles for uncertainty quantification","author":"qendro","year":"2021","journal-title":"Machine Learning for Health"},{"article-title":"High frequency eeg artifact detection with uncertainty via early exit paradigm","year":"2021","author":"qendro","key":"ref11"},{"key":"ref12","first-page":"3301","article-title":"Shallow-deep networks: Understanding and mitigating network overthinking","author":"kaya","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911747"},{"article-title":"Uci machine learning repository","year":"2017","author":"dua","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/SPMB50085.2020.9353647"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Very deep convolutional networks for large-scale image recognition","year":"2014","author":"simonyan","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8856620"},{"article-title":"Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift","year":"2019","author":"ovadia","key":"ref4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101714"},{"key":"ref6","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"lakshminarayanan","year":"2017","journal-title":"Advances in neural information processing systems"},{"article-title":"Bayesian convolutional neural networks with bernoulli approximate variational inference","year":"2015","author":"gal","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"article-title":"Hyperparameter ensembles for robustness and uncertainty quantification","year":"2020","author":"wenzel","key":"ref7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107147"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682307"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430782"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5599615"},{"key":"ref22","article-title":"When networks disagree: Ensemble methods for hybrid neural networks","author":"perrone","year":"1992","journal-title":"Tech Rep"},{"article-title":"Adam: A method for stochastic optimization","year":"2014","author":"kingma","key":"ref21"},{"article-title":"Deep ensembles: A loss landscape perspective","year":"2019","author":"fort","key":"ref23"}],"event":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2022,5,23]]},"location":"Singapore, Singapore","end":{"date-parts":[[2022,5,27]]}},"container-title":["ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9745891\/9746004\/09746330.pdf?arnumber=9746330","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T20:11:21Z","timestamp":1660594281000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9746330\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icassp43922.2022.9746330","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]}}}