{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:22:15Z","timestamp":1780590135339,"version":"3.54.1"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100006192","name":"Advanced Scientific Computing Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006192","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,21]]},"DOI":"10.1109\/icpr56361.2022.9956231","type":"proceedings-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T19:34:13Z","timestamp":1669750453000},"page":"1908-1914","source":"Crossref","is-referenced-by-count":26,"title":["AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification"],"prefix":"10.1109","author":[{"given":"Romain","family":"Egele","sequence":"first","affiliation":[{"name":"Argonne National Laboratory,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romit","family":"Maulik","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory,Lemont,Illinois,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishnan","family":"Raghavan","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory,Lemont,Illinois,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bethany","family":"Lusch","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory,Lemont,Illinois,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabelle","family":"Guyon","sequence":"additional","affiliation":[{"name":"Université Paris-Saclay,Paris,France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prasanna","family":"Balaprakash","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory,Lemont,Illinois,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Bayesian deep learning and a probabilistic perspective of generalization","author":"wilson","year":"2020"},{"key":"ref11","article-title":"Hyperparameter ensembles for robustness and uncertainty quantification","author":"wenzel","year":"2021"},{"key":"ref12","article-title":"Neural ensemble search for uncertainty estimation and dataset shift","author":"zaidi","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1994.374138"},{"key":"ref14","article-title":"Learning in implicit generative models","author":"mohamed","year":"2016"},{"key":"ref15","article-title":"Mixture density networks","author":"bishop","year":"1994"},{"key":"ref16","article-title":"On feature collapse and deep kernel learning for single forward pass uncertainty","author":"van amersfoort","year":"2021"},{"key":"ref17","first-page":"4075","article-title":"High-quality prediction intervals for deep learning: A distribution-free, ensembled approach","author":"pearce","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref18","first-page":"13153","article-title":"A simple baseline for Bayesian uncertainty in deep learning","volume":"32","author":"maddox","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2006.76"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/34.58871"},{"key":"ref4","article-title":"Stochastic variational inference","volume":"14","author":"hoffman","year":"2013","journal-title":"Journal of Machine Learning Research"},{"key":"ref27","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016"},{"key":"ref3","volume":"118","author":"neal","year":"2012","journal-title":"Bayesian learning for neural networks"},{"key":"ref6","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref5","article-title":"Probabilistic backpropagation for scalable learning of Bayesian neural networks","author":"hern\u00e3\u00a1ndez-lobato","year":"2015"},{"key":"ref8","article-title":"Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift","author":"ovadia","year":"2019"},{"key":"ref7","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"lakshminarayanan","year":"2017"},{"key":"ref2","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":"ref9","article-title":"Pitfalls of indomain uncertainty estimation and ensembling in deep learning","author":"ashukha","year":"2020"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-05946-3"},{"key":"ref20","first-page":"1613","article-title":"Weight uncertainty in neural network","author":"blundell","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref22","author":"rudary","year":"2009","journal-title":"On predictive linear Gaussian models"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3086757"},{"key":"ref24","article-title":"Regularized evolution for image classifier architecture search","author":"real","year":"2018"},{"key":"ref23","first-page":"1","article-title":"AgEBO-Tabular: Joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data","author":"egele","year":"2021","journal-title":"SC14 International Conference for High Performance Computing Networking Storage and Analysis SC"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015432"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/978-3-642-10701-6_6","article-title":"Kriging is well-suited to parallelize optimization","volume":"2","author":"ginsbourger","year":"2010","journal-title":"Computational Intelligence in Expensive Optimization Problems"}],"event":{"name":"2022 26th International Conference on Pattern Recognition (ICPR)","location":"Montreal, QC, Canada","start":{"date-parts":[[2022,8,21]]},"end":{"date-parts":[[2022,8,25]]}},"container-title":["2022 26th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9956007\/9955631\/09956231.pdf?arnumber=9956231","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T20:04:00Z","timestamp":1671480240000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9956231\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/icpr56361.2022.9956231","relation":{},"subject":[],"published":{"date-parts":[[2022,8,21]]}}}