{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:30:57Z","timestamp":1760596257000,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2016,8,31]],"date-time":"2016-08-31T00:00:00Z","timestamp":1472601600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61379101"],"award-info":[{"award-number":["61379101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1007\/s12559-016-9429-1","type":"journal-article","created":{"date-parts":[[2016,9,2]],"date-time":"2016-09-02T21:09:16Z","timestamp":1472850556000},"page":"1064-1073","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Weight Uncertainty in Boltzmann Machine"],"prefix":"10.1007","volume":"8","author":[{"given":"Jian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1391-2717","authenticated-orcid":false,"given":"Shifei","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,8,31]]},"reference":[{"issue":"3","key":"9429_CR1","first-page":"625","volume":"11","author":"D Erhan","year":"2010","unstructured":"Erhan D, Vincent P, Bengio Y. Why does unsupervised pre-training help deep learning. J Mach Learn Res. 2010;11(3):625\u201360.","journal-title":"J Mach Learn Res"},{"issue":"8","key":"9429_CR2","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1162\/089976602760128018","volume":"14","author":"G Hinton","year":"2002","unstructured":"Hinton G. Training products of experts by minimizing contrastive divergence. Neural Comput. 2002;14(8):1771\u2013800.","journal-title":"Neural Comput"},{"issue":"6","key":"9429_CR3","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1162\/neco.2008.04-07-510","volume":"20","author":"N Roux","year":"2008","unstructured":"Roux N, Bengio Y. Representational power of restricted Boltzmann machines and deep belief networks. Neural Comput. 2008;20(6):1631\u201349.","journal-title":"Neural Comput"},{"issue":"7","key":"9429_CR4","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"G Hinton","year":"2006","unstructured":"Hinton G, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527\u201354.","journal-title":"Neural Comput"},{"issue":"5786","key":"9429_CR5","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"G Hinton","year":"2006","unstructured":"Hinton G, Salakhutdinov R. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504\u20137.","journal-title":"Science"},{"key":"9429_CR6","first-page":"1096","volume":"22","author":"H Lee","year":"2009","unstructured":"Lee H, Pham P, Yan L, et al. Unsupervised feature learning for audio classification using convolutional deep belief networks. Adv Neural Inf Process Syst. 2009;22:1096\u20131104.","journal-title":"Adv Neural Inf Process Syst"},{"key":"9429_CR7","first-page":"2735","volume":"1\u20134","author":"M Norouzi","year":"2009","unstructured":"Norouzi M, Ranjbar M, Mori G. Stacks of convolutional restricted Boltzmann machines for shift-invariant feature learning. IEEE Conf Comput Vis Pattern Recognit. 2009;1\u20134:2735\u20132742.","journal-title":"IEEE Conf Comput Vis Pattern Recognit"},{"issue":"8","key":"9429_CR8","first-page":"693","volume":"9","author":"R Salakhutdinov","year":"2010","unstructured":"Salakhutdinov R, Larochelle H. Efficient learning of deep Boltzmann machines. J Mach Learn Res. 2010;9(8):693\u2013700.","journal-title":"J Mach Learn Res"},{"issue":"8","key":"9429_CR9","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1162\/NECO_a_00311","volume":"24","author":"R Salakhutdinov","year":"2012","unstructured":"Salakhutdinov R, Hinton G. An efficient learning procedure for deep Boltzmann machines. Neural Comput. 2012;24(8):1967\u20132006.","journal-title":"Neural Comput"},{"key":"9429_CR10","doi-asserted-by":"crossref","unstructured":"Bengio Y, Boulanger-Lewandowski N, Pascanu R. Advances in optimizing recurrent networks. IEEE International Conference on Acoustics, 2012: 8624\u20138628.","DOI":"10.1109\/ICASSP.2013.6639349"},{"issue":"1","key":"9429_CR11","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s13042-015-0419-5","volume":"7","author":"J Zhang","year":"2016","unstructured":"Zhang J, Ding S, Zhang N, et al. Incremental extreme learning machine based on deep feature embedded. Int J Mach Learn Cybernet. 2016;7(1):111\u201320.","journal-title":"Int J Mach Learn Cybernet"},{"key":"9429_CR12","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.neucom.2015.07.058","volume":"171","author":"N Zhang","year":"2016","unstructured":"Zhang N, Ding S, Shi Z. Denoising Laplacian multi-layer extreme learning machine. Neurocomputing. 2016;171:1066\u201374.","journal-title":"Neurocomputing"},{"key":"9429_CR13","first-page":"1","volume":"2015","author":"S Ding","year":"2015","unstructured":"Ding S, Zhang N, Xu X, et al. Deep Extreme learning machine and its application in EEG classification. Math Probl Eng. 2015;2015:1\u201311.","journal-title":"Math Probl Eng"},{"issue":"3","key":"9429_CR14","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TIFS.2014.2381872","volume":"10","author":"J Li","year":"2015","unstructured":"Li J, Li X, Yang B, et al. Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur. 2015;10(3):507\u201318.","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"2","key":"9429_CR15","doi-asserted-by":"crossref","first-page":"961","DOI":"10.3233\/IFS-141378","volume":"28","author":"Y Zheng","year":"2015","unstructured":"Zheng Y, Jeon B, Xu D, et al. Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst. 2015;28(2):961\u201373.","journal-title":"J Intell Fuzzy Syst"},{"issue":"7","key":"9429_CR16","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1109\/TNNLS.2014.2342533","volume":"26","author":"B Gu","year":"2015","unstructured":"Gu B, Sheng V, Tay K, et al. Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst. 2015;26(7):1403\u201316.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"9429_CR17","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929\u201358.","journal-title":"J Mach Learn Res"},{"key":"9429_CR18","unstructured":"Blundell C, Cornebise J, Kavukcuoglu K. Weight uncertainty in neural networks. International Conference on Machine Learning, France, 2015."},{"key":"9429_CR19","first-page":"1121","volume":"20","author":"S Osindero","year":"2008","unstructured":"Osindero S, Hinton G. Modeling image patches with a directed hierarchy of markov random fields. Adv Neural Inf Process Syst. 2008;20:1121\u20131128.","journal-title":"Adv Neural Inf Process Syst"},{"key":"9429_CR20","unstructured":"Lee T, Yoon S. Boosted categorical restricted Boltzmann machine for computational prediction of splice junctions. International Conference on Machine Learning, France, 2015."},{"issue":"1","key":"9429_CR21","first-page":"926","volume":"9","author":"G Hinton","year":"2010","unstructured":"Hinton G. A practical guide to training restricted Boltzmann machines. Momentum. 2010;9(1):926.","journal-title":"Momentum"},{"key":"9429_CR22","unstructured":"Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images. Technical report U. Toronto, 2009."},{"key":"9429_CR23","unstructured":"Salakhutdinov R. Learning and evaluating Boltzmann machines, Technical Report U. Toronto, 2008"},{"issue":"92","key":"9429_CR24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0004-3702(92)90065-6","volume":"56","author":"R Neal","year":"1992","unstructured":"Neal R. Connectionist learning of belief networks. Artif Intell. 1992;56(92):71\u2013113.","journal-title":"Artif Intell"},{"issue":"4598","key":"9429_CR25","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick S, Jr G, Vecchi M. Optimization by simulated annealing. Science. 1983;220(4598):671\u201380.","journal-title":"Science"},{"key":"9429_CR26","first-page":"1598","volume":"22","author":"R Salakhutdinov","year":"2009","unstructured":"Salakhutdinov R. Learning in Markov random fields using tempered transitions. Adv Neural Inf Process Syst. 2009;22:1598\u20131606.","journal-title":"Adv Neural Inf Process Syst"},{"key":"9429_CR27","unstructured":"Desjardins G, Courville A, Bengio Y, et al. Tempered markov chain monte carlo for training of restricted Boltzmann machines. International Conference on Artificial Intelligence and Statistics, Italy, 2010: 145\u2013152."},{"key":"9429_CR28","doi-asserted-by":"crossref","unstructured":"Tieleman T. Training restricted Boltzmann machines using approximations to the likelihood gradient. International Conference on Machine Learning, Finland, 2008: 1064\u20131071.","DOI":"10.1145\/1390156.1390290"},{"key":"9429_CR29","doi-asserted-by":"crossref","unstructured":"Tieleman T, Hinton G. Using fast weights to improve persistent contrastive divergence. International Conference on Machine Learning, Canada, 2009: 1033\u20131040.","DOI":"10.1145\/1553374.1553506"},{"issue":"2","key":"9429_CR30","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1023\/A:1007665907178","volume":"37","author":"M Jordan","year":"1999","unstructured":"Jordan M, Ghahramani Z, Jaakkola T, et al. An introduction to variational methods for graphical models. Mach Learn. 1999;37(2):183\u2013233.","journal-title":"Mach Learn"},{"key":"9429_CR31","first-page":"995","volume":"1","author":"H Ackley","year":"1987","unstructured":"Ackley H, Hinton G, Sejnowski J, et al. a mean field theory learning algorithm for neural network. Complex Syst. 1987;1:995\u20131019.","journal-title":"Complex Syst"},{"key":"9429_CR32","first-page":"2447","volume":"3","author":"G Hinton","year":"2012","unstructured":"Hinton G, Salakhutdinov R. A better way to pretrain deep Boltzmann machines. Adv Neural Inf Process Syst. 2012;3:2447\u20132455.","journal-title":"Adv Neural Inf Process Syst"},{"key":"9429_CR33","first-page":"621","volume":"9","author":"M Ranzato","year":"2010","unstructured":"Ranzato M, Krizhevsky A, Hinton G. Factored 3-way restricted Boltzmann machines for modeling natural images. J Mach Learn Res. 2010;9:621\u20138.","journal-title":"J Mach Learn Res"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-016-9429-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12559-016-9429-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-016-9429-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T19:02:01Z","timestamp":1601060521000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12559-016-9429-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,31]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2016,12]]}},"alternative-id":["9429"],"URL":"https:\/\/doi.org\/10.1007\/s12559-016-9429-1","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2016,8,31]]}}}