{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:10:00Z","timestamp":1760609400788,"version":"3.37.3"},"reference-count":45,"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"}],"funder":[{"DOI":"10.13039\/100012774","name":"Innovation Fund Denmark","doi-asserted-by":"publisher","award":["8053-00073B"],"award-info":[{"award-number":["8053-00073B"]}],"id":[{"id":"10.13039\/100012774","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9534258","type":"proceedings-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T20:32:37Z","timestamp":1632342757000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation"],"prefix":"10.1109","author":[{"given":"Mathias","family":"Lowe","sequence":"first","affiliation":[]},{"given":"Per","family":"Lunnemann","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Risi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","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"},{"year":"2015","author":"nielsen","journal-title":"Neural Networks and Deep Learning","key":"ref38"},{"year":"2017","author":"jia","journal-title":"Using deep neural network approximate Bayesian network","key":"ref33"},{"key":"ref32","first-page":"2218","article-title":"Multiplicative normalizing flows for variational bayesian neural networks","author":"louizos","year":"2017","journal-title":"International Conference on Machine Learning"},{"key":"ref31","first-page":"1530","article-title":"Variational inference with normalizing flows","author":"rezende","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref30","first-page":"1","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"papamakarios","year":"2021","journal-title":"Journal of Machine Learning Research"},{"key":"ref37","first-page":"55","author":"prechelt","year":"1998","journal-title":"Early Stopping - But When?"},{"year":"2018","author":"bradbury","journal-title":"JAX Composable transformations of Python+NumPy programs","key":"ref36"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1109\/TPAMI.2016.2577031"},{"doi-asserted-by":"publisher","key":"ref34","DOI":"10.1088\/1361-6587\/ab1d26"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1201\/b16018"},{"key":"ref40","article-title":"Layer normalization","volume":"abs 1607 6450","author":"ba","year":"2016","journal-title":"CoRR"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1016\/j.eswa.2008.09.024"},{"key":"ref12","first-page":"1","author":"wilson","year":"2020","journal-title":"The case for Bayesian deep learning"},{"key":"ref13","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"},{"year":"2020","author":"wilson","journal-title":"Bayesian Deep Learning and a Probabilistic Perspective of Generalization","key":"ref14"},{"key":"ref15","first-page":"2798","article-title":"A Bayesian data augmentation approach for learning deep models","author":"tran","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref16","first-page":"234","article-title":"Uncertainty in neural networks: Approximately Bayesian ensembling","volume":"108","author":"pearce","year":"2020","journal-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics"},{"key":"ref17","article-title":"Posterior distribution analysis for Bayesian inference in neural networks","author":"myshkov","year":"0","journal-title":"Workshop on Bayesian Deep Learning NIPS"},{"year":"1995","author":"bishop","journal-title":"Bayesian methods for neural networks","key":"ref18"},{"key":"ref19","volume":"118","author":"neal","year":"2012","journal-title":"Bayesian learning for neural networks"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref4","volume":"1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref27","first-page":"69","article-title":"Vc dimension of neural networks","volume":"168","author":"sontag","year":"1998","journal-title":"NATO ASI Series F Computer and Systems Sciences"},{"key":"ref3","doi-asserted-by":"crossref","DOI":"10.1201\/9780429029608","author":"mcelreath","year":"2020","journal-title":"Statistical rethinking A Bayesian course with examples in R and Stan"},{"year":"2003","author":"mackay","journal-title":"Information Theory Inference and Learning Algorithms","key":"ref6"},{"key":"ref29","article-title":"Auto-Encoding Variational Bayes","author":"kingma","year":"0","journal-title":"International Conference on Learning Representations"},{"year":"2007","author":"robert","journal-title":"The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation","key":"ref5"},{"key":"ref8","article-title":"Predictive inference based on Markov Chain Monte Carlo output","author":"kr\u00fcger","year":"2020","journal-title":"International Statistical Review"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1002\/wics.1314"},{"year":"2006","author":"bishop","journal-title":"Pattern Recognition and Machine Learning","key":"ref2"},{"year":"2014","author":"kruschke","journal-title":"Doing Bayesian Data Analysis A Tutorial with R JAGS and Stan","key":"ref9"},{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.1017\/9781108679930","author":"deisenroth","year":"2020","journal-title":"Machine Learning M"},{"year":"2015","author":"hern\u00e1ndez-lobato","journal-title":"Probabilistic back-propagation for scalable learning of Bayesian neural networks","key":"ref20"},{"doi-asserted-by":"publisher","key":"ref45","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1162\/neco.1992.4.3.415"},{"key":"ref21","first-page":"2348","article-title":"Practical variational inference for neural networks","volume":"24","author":"graves","year":"2011","journal-title":"Advances in neural information processing systems"},{"key":"ref42","first-page":"1593","article-title":"The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo","volume":"15","author":"hoffman","year":"2014","journal-title":"J Mach Learn Res"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1016\/j.acha.2015.12.005"},{"doi-asserted-by":"publisher","key":"ref41","DOI":"10.7717\/peerj-cs.55"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1016\/0893-6080(91)90009-T"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.1007\/978-3-030-11027-7_24"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/72.788640"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1007\/BF01206525"},{"key":"ref25","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"brown","year":"2020","journal-title":"Advances in neural information processing systems"}],"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\/09534258.pdf?arnumber=9534258","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:46:02Z","timestamp":1652197562000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9534258\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9534258","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}