{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:20:34Z","timestamp":1740169234130,"version":"3.37.3"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3062747","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T20:48:46Z","timestamp":1614372526000},"page":"36241-36251","source":"Crossref","is-referenced-by-count":2,"title":["Incremental Kernel Principal Components Subspace Inference With Nystr\u00f6m Approximation for Bayesian Deep Learning"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0470-9178","authenticated-orcid":false,"given":"Yongguang","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7785-9698","authenticated-orcid":false,"given":"Shuzhen","family":"Yao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6108-7494","authenticated-orcid":false,"given":"Tian","family":"Xu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"633","article-title":"Sparse kernel principal component analysis","author":"tipping","year":"2001","journal-title":"Proc NIPS"},{"key":"ref38","first-page":"598","article-title":"Automatic choice of dimensionality for PCA","author":"minka","year":"2000","journal-title":"Proc NIPS"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref32","first-page":"1657","article-title":"Less is more: Computational regularization","author":"rudi","year":"2015","journal-title":"Proc NIPS"},{"key":"ref31","article-title":"Subspace learning for large-scale SVMs","author":"li","year":"2020","journal-title":"arXiv 2002 08937"},{"key":"ref30","first-page":"476","article-title":"Method vs random Fourier features: A theoretical and empirical comparison","author":"yang","year":"2012","journal-title":"Proc NIPS"},{"key":"ref37","first-page":"8803","article-title":"Loss surfaces, mode connectivity, and fast ensembling of DNNs","author":"garipov","year":"2018","journal-title":"Proc NIPS"},{"key":"ref36","first-page":"1","article-title":"Deep Bayesian bandits showdown","author":"riquelme","year":"2017","journal-title":"Proc ICLR"},{"key":"ref35","first-page":"1472","article-title":"Deep Gaussian processes for regression using approximate expectation propagation","volume":"48","author":"bui","year":"2016","journal-title":"Proc ICML"},{"key":"ref34","first-page":"370","article-title":"Deep kernel learning","volume":"51","author":"wilson","year":"2016","journal-title":"Proc ICML"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.181"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.5244\/C.20.96"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.896668"},{"key":"ref14","article-title":"Incremental kernel PCA and the Nyström method","author":"hallgren","year":"2018","journal-title":"arXiv 1802 00043"},{"key":"ref15","first-page":"682","article-title":"Using the Nyström method to speed up kernel machines","author":"williams","year":"2001","journal-title":"Proc NIPS"},{"key":"ref16","first-page":"13132","article-title":"A simple baseline for Bayesian uncertainty in deep learning","author":"maddox","year":"2019","journal-title":"Proc NIPS"},{"key":"ref17","first-page":"12","article-title":"Stochastic gradient learning in neural networks","volume":"91","author":"l\u00e9on","year":"1991","journal-title":"Proc Neuro-Nimes"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TNN.1997.557706","article-title":"Bayesian learning for neural networks","volume":"8","author":"neal","year":"1997","journal-title":"IEEE Trans Neural Netw"},{"key":"ref19","first-page":"1321","article-title":"Stochastic gradient Hamiltonian Monte Carlo","volume":"70","author":"chen","year":"2014","journal-title":"Proc ICML"},{"key":"ref28","first-page":"3738","article-title":"Online structured Laplace approximations for overcoming catastrophic forgetting","volume":"31","author":"ritter","year":"2018","journal-title":"Proc NIPS"},{"journal-title":"Information Theory Inference and Learning Algorithms","year":"2002","author":"mackay","key":"ref4"},{"key":"ref27","first-page":"8789","article-title":"Loss surfaces, mode connectivity, and fast ensembiling of DNNs","volume":"31","author":"garipov","year":"2018","journal-title":"Proc NIPS"},{"key":"ref3","first-page":"1613","article-title":"Weight uncertainty in neural network","volume":"37","author":"blundell","year":"2015","journal-title":"Proc ICML"},{"key":"ref6","first-page":"1169","article-title":"Subspace inference for Bayesian deep learning","author":"izmailov","year":"2019","journal-title":"Proc ICML"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/090771806"},{"key":"ref5","first-page":"2575","article-title":"Variational dropout and the local reparameterization trick","volume":"2","author":"kingma","year":"2015","journal-title":"Proc NIPS"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1938.tb02189.x"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/34.908974"},{"key":"ref2","first-page":"5580","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"kendall","year":"2017","journal-title":"Proc NIPS"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"wold","year":"1986","journal-title":"Chemometrics Intell Lab Syst"},{"key":"ref1","first-page":"1321","article-title":"On calibration of modern neural networks","volume":"70","author":"guo","year":"2017","journal-title":"Proc ICML"},{"key":"ref20","first-page":"681","article-title":"Bayesian learning via stochastic gradient Langevin dynamics","author":"welling","year":"2011","journal-title":"Proc ICML"},{"key":"ref22","first-page":"541","article-title":"Elliptical slice sampling","volume":"9","author":"murray","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref21","first-page":"1","article-title":"Stochastic gradient descent as approximate Bayesian inference","volume":"18","author":"mandt","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref24","first-page":"1","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc ICLR"},{"key":"ref23","first-page":"2348","article-title":"Practical variational inference for neural networks","author":"graves","year":"2011","journal-title":"Proc 24th Int Conf Neural Inf Process Syst"},{"key":"ref26","first-page":"876","article-title":"Averaging weights leads to wider optima and better generalization","author":"izmailov","year":"2018","journal-title":"Proc UAI"},{"key":"ref25","first-page":"2218","article-title":"Multiplicative normalizing flows for variational Bayesian neural networks","volume":"70","author":"louizos","year":"2017","journal-title":"Proc ICML"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09364973.pdf?arnumber=9364973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:57:06Z","timestamp":1639771026000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9364973\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3062747","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}