{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:09:52Z","timestamp":1740100192876,"version":"3.37.3"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"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":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533997","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-9","source":"Crossref","is-referenced-by-count":3,"title":["Towards Consistent Predictive Confidence through Fitted Ensembles"],"prefix":"10.1109","author":[{"given":"Navid","family":"Kardan","sequence":"first","affiliation":[]},{"given":"Ankit","family":"Sharma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4503-0839","authenticated-orcid":false,"given":"Kenneth O.","family":"Stanley","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref32","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"ArXiv Preprint"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref31"},{"key":"ref30","article-title":"Fitted learning: Models with awareness of their limits","author":"kardan","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref37","article-title":"Delving into transferable adversarial examples and black-box attacks","author":"liu","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref36","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref35","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"0","journal-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref10","first-page":"1321","article-title":"On calibration of modern neural networks","volume":"70","author":"guo","year":"0","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref11","article-title":"Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning","author":"zhang","year":"0","journal-title":"Proceedings of the ICML International Conference on Machine Learning"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7965897"},{"key":"ref14","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref15","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","author":"liang","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref16","first-page":"7167","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","author":"lee","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref17","article-title":"Learning confidence for out-of-distribution detection in neural networks","author":"devries","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref18","article-title":"Deep anomaly detection with outlier exposure","author":"hendrycks","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_34"},{"key":"ref28","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"},{"key":"ref4","volume":"3","author":"duda","year":"1973","journal-title":"Pattern Classification and Scene Analysis"},{"key":"ref27","article-title":"Universum prescription: Regularization using unlabeled data","author":"zhang","year":"0","journal-title":"Thirty-First AAAI Conference on Artificial Intelligence"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017197"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"fisher","year":"1936","journal-title":"Annals of Eugenics"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1938.tb02189.x"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"journal-title":"Pattern Classification","year":"2000","author":"duda","key":"ref2"},{"key":"ref9","article-title":"The nature of statistical learning theory","author":"vapnik","year":"2013","journal-title":"Springer Science & Business Media"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470"},{"key":"ref20","first-page":"8491","article-title":"Detecting out-of-distribution examples with gram matrices","author":"sastry","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref22","article-title":"Energy-based out-of-distribution detection","author":"liu","year":"0","journal-title":"Conference on Neural Information Processing Systems NeurIPS"},{"key":"ref21","article-title":"Robust out-of-distribution detection in neural networks","author":"chen","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref24","article-title":"Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift","author":"snoek","year":"0","journal-title":"Conference on Neural Information Processing Systems NeurIPS"},{"journal-title":"Uncertainty in deep learning","year":"2016","author":"gal","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206810"},{"key":"ref25","article-title":"Posterior network: Uncertainty estimation without OOD samples via density-based pseudo-counts","author":"charpentier","year":"0","journal-title":"Annual Conference on Neural Information Processing Systems 2020 NeurIPS"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533997.pdf?arnumber=9533997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:45:55Z","timestamp":1652197555000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533997\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533997","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}