{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T22:07:37Z","timestamp":1755036457898,"version":"3.37.3"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"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":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1109\/tnnls.2020.3042525","type":"journal-article","created":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T20:28:40Z","timestamp":1608755320000},"page":"1492-1506","source":"Crossref","is-referenced-by-count":7,"title":["Accelerating Monte Carlo Bayesian Prediction via Approximating Predictive Uncertainty Over the Simplex"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9663-0079","authenticated-orcid":false,"given":"Yufei","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7017-0388","authenticated-orcid":false,"given":"Wuguannan","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Mathematics, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4579-4268","authenticated-orcid":false,"given":"Qiao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2886-2513","authenticated-orcid":false,"given":"Antoni B.","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6431-9868","authenticated-orcid":false,"given":"Chun Jason","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","article-title":"Wasserstein auto-encoders","author":"tolstikhin","year":"2017","journal-title":"arXiv 1711 01558"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1177\/0278364918770733"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102457"},{"key":"ref30","first-page":"5281","article-title":"Direct uncertainty prediction for medical second opinions","volume":"97","author":"raghu","year":"2019","journal-title":"Mach Learn Res"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref35","first-page":"681","article-title":"Bayesian dlearning via stochastic gradient Langevin dynamics","author":"welling","year":"2011","journal-title":"Proc 28th Int Conf Mach Learn"},{"key":"ref34","volume":"338","author":"villani","year":"2008","journal-title":"Optimal Transport Old and New"},{"key":"ref10","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00355"},{"key":"ref12","first-page":"5767","article-title":"Improved training of Wasserstein GANs","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"han","year":"2015","journal-title":"arXiv 1510 00149 [cs]"},{"key":"ref14","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref15","article-title":"Deep anomaly detection with outlier exposure","author":"hendrycks","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref16","first-page":"1648","article-title":"MCMC for variationally sparse Gaussian processes","author":"hensman","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","first-page":"351","article-title":"Scalable variational Gaussian process classification","author":"hensman","year":"2015","journal-title":"Proc 18th Int Conf Artif Intell Statist"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref19","first-page":"4107","article-title":"Binarized neural networks","author":"hubara","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1561\/2200000073"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref4"},{"key":"ref27","first-page":"489","article-title":"Reparameterization gradients through acceptance-rejection sampling algorithms","author":"naesseth","year":"2017","journal-title":"Proc Artif Intell Statist"},{"key":"ref3","first-page":"3438","article-title":"Bayesian dark knowledge","author":"balan","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.10.042"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2013.829001"},{"key":"ref5","first-page":"99","article-title":"Dropout distillation","author":"bul\u00f2","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref8","first-page":"441","article-title":"Implicit reparameterization gradients","author":"figurnov","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.05.082"},{"key":"ref2","article-title":"Wasserstein GAN","author":"arjovsky","year":"2017","journal-title":"arXiv 1701 07875"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5388-x"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2307\/2532069"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107514"},{"key":"ref22","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","author":"liang","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref21","first-page":"5574","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision","author":"kendall","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","article-title":"The concrete distribution: A continuous relaxation of discrete random variables","author":"maddison","year":"2016","journal-title":"arXiv 1611 00712"},{"key":"ref23","first-page":"345","article-title":"Towards accurate binary convolutional neural network","author":"lin","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","article-title":"Discovering discrete latent topics with neural variational inference","author":"miao","year":"2017","journal-title":"arXiv 1706 00359"},{"key":"ref25","first-page":"7047","article-title":"Predictive uncertainty estimation via prior networks","author":"malinin","year":"2018","journal-title":"Advances in neural information processing systems"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9749160\/09305977.pdf?arnumber=9305977","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T20:30:44Z","timestamp":1652733044000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9305977\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":36,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2020.3042525","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2022,4]]}}}