{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:15:09Z","timestamp":1777547709611,"version":"3.51.4"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2013,7,3]],"date-time":"2013-07-03T00:00:00Z","timestamp":1372809600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2013,10]]},"DOI":"10.1007\/s10994-013-5390-3","type":"journal-article","created":{"date-parts":[[2013,7,2]],"date-time":"2013-07-02T14:52:58Z","timestamp":1372776778000},"page":"53-69","source":"Crossref","is-referenced-by-count":15,"title":["The flip-the-state transition operator for restricted Boltzmann machines"],"prefix":"10.1007","volume":"93","author":[{"given":"Kai","family":"Br\u00fcgge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asja","family":"Fischer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Igel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2013,7,3]]},"reference":[{"issue":"6","key":"5390_CR1","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1162\/neco.2008.11-07-647","volume":"21","author":"Y. Bengio","year":"2009","unstructured":"Bengio, Y., & Delalleau, O. (2009). Justifying and generalizing contrastive divergence. Neural Computation, 21(6), 1601\u20131621.","journal-title":"Neural Computation"},{"issue":"1","key":"5390_CR2","first-page":"552","volume":"28","author":"Y. Bengio","year":"2013","unstructured":"Bengio, Y., Mesnil, G., Dauphin, Y., & Rifai, S. (2013). Better mixing via deep representations. Journal of Machine Learning Research Workshop and Conference Proceedings, 28(1), 552\u2013560.","journal-title":"Journal of Machine Learning Research Workshop and Conference Proceedings"},{"key":"5390_CR3","series-title":"Monte Carlo simulation, and queues","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3124-8","volume-title":"Markov chains: Gibbs fields","author":"P. Br\u00e9maud","year":"1999","unstructured":"Br\u00e9maud, P. (1999). Markov chains: Gibbs fields, Monte Carlo simulation, and queues. Berlin: Springer."},{"issue":"8","key":"5390_CR4","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1162\/NECO_a_00158","volume":"23","author":"O. Breuleux","year":"2011","unstructured":"Breuleux, O., Bengio, Y., & Vincent, P. (2011). Quickly generating representative samples from an RBM-derived process. Neural Computation, 23(8), 2058\u20132073.","journal-title":"Neural Computation"},{"key":"5390_CR5","first-page":"3246","volume-title":"Proceedings of the international joint conference on neural networks (IJCNN 2010)","author":"K. Cho","year":"2010","unstructured":"Cho, K., Raiko, T., & Ilin, A. (2010). Parallel tempering is efficient for learning restricted Boltzmann machines. In Proceedings of the international joint conference on neural networks (IJCNN 2010) (pp.\u00a03246\u20133253). New York: IEEE Press."},{"issue":"AISTATS 2010","key":"5390_CR6","first-page":"145","volume":"9","author":"G. Desjardins","year":"2010","unstructured":"Desjardins, G., Courville, A., Bengio, Y., Vincent, P., & Dellaleau, O. (2010). Parallel tempering for training of restricted Boltzmann machines. Journal of Machine Learning Research Workshop and Conference Proceedings 9(AISTATS 2010), 145\u2013152.","journal-title":"Journal of Machine Learning Research Workshop and Conference Proceedings"},{"key":"5390_CR7","series-title":"LNCS","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/978-3-642-15825-4_26","volume-title":"International conference on artificial neural networks (ICANN 2010)","author":"A. Fischer","year":"2010","unstructured":"Fischer, A., & Igel, C. (2010). Empirical analysis of the divergence of Gibbs sampling based learning algorithms for Restricted Boltzmann Machines. In K. Diamantaras, W. Duch, & L. S. Iliadis (Eds.), LNCS: Vol.\u00a06354. International conference on artificial neural networks (ICANN 2010) (pp. 208\u2013217). Berlin: Springer."},{"key":"5390_CR8","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1162\/NECO_a_00085","volume":"23","author":"A. Fischer","year":"2011","unstructured":"Fischer, A., & Igel, C. (2011). Bounding the bias of contrastive divergence learning. Neural Computation, 23, 664\u2013673.","journal-title":"Neural Computation"},{"key":"5390_CR9","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1162\/089976602760128018","volume":"14","author":"G. E. Hinton","year":"2002","unstructured":"Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14, 1771\u20131800.","journal-title":"Neural Computation"},{"issue":"5786","key":"5390_CR10","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"G. E. Hinton","year":"2006","unstructured":"Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504\u2013507.","journal-title":"Science"},{"key":"5390_CR11","first-page":"993","volume":"9","author":"C. Igel","year":"2008","unstructured":"Igel, C., Glasmachers, T., & Heidrich-Meisner, V. (2008). Shark. Journal of Machine Learning Research, 9, 993\u2013996.","journal-title":"Journal of Machine Learning Research"},{"key":"5390_CR12","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/BF00162521","volume":"6","author":"J. S. Liu","year":"1996","unstructured":"Liu, J. S. (1996). Metropolized independent sampling with comparisons to rejection sampling and importance sampling. Statistics and Computing, 6, 113\u2013119.","journal-title":"Statistics and Computing"},{"key":"5390_CR13","volume-title":"Information theory, inference & learning algorithms","author":"D. J. C. MacKay","year":"2002","unstructured":"MacKay, D. J. C. (2002). Information theory, inference & learning algorithms. Cambridge: Cambridge University Press."},{"key":"5390_CR14","unstructured":"Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto."},{"issue":"3","key":"5390_CR15","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1093\/biomet\/60.3.607","volume":"60","author":"P. H. Peskun","year":"1973","unstructured":"Peskun, P. H. (1973). Optimum Monte-Carlo sampling using Markov chains. Biometrika, 60(3), 607\u2013612.","journal-title":"Biometrika"},{"key":"5390_CR16","first-page":"1598","volume-title":"Advances in neural information processing systems","author":"R. Salakhutdinov","year":"2010","unstructured":"Salakhutdinov, R. (2010). Learning in Markov random fields using tempered transitions. In Y. Bengio, D.\u00a0Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in neural information processing systems (Vol.\u00a022, pp. 1598\u20131606)."},{"key":"5390_CR17","first-page":"194","volume-title":"Parallel distributed processing: explorations in the microstructure of cognition, Vol. 1: foundations","author":"P. Smolensky","year":"1986","unstructured":"Smolensky, P. (1986). Information processing in dynamical systems: foundations of harmony theory. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: explorations in the microstructure of cognition, Vol. 1: foundations (pp. 194\u2013281). Cambridge: MIT Press."},{"key":"5390_CR18","unstructured":"Thompson, M. B. (2010). A comparison of methods for computing autocorrelation time. Tech. Rep. 1007, Department of Statistics, University of Toronto."},{"issue":"12","key":"5390_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v043.i12","volume":"43","author":"M. B. Thompson","year":"2011","unstructured":"Thompson, M. B. (2011). Introduction to SamplerCompare. Journal of Statistical Software, 43(12), 1\u201310.","journal-title":"Journal of Statistical Software"},{"key":"5390_CR20","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1145\/1390156.1390290","volume-title":"International conference on machine learning (ICML)","author":"T. Tieleman","year":"2008","unstructured":"Tieleman, T. (2008). Training restricted Boltzmann machines using approximations to the likelihood gradient. In W. W. Cohen, A. McCallum, & S. T. Roweis (Eds.), International conference on machine learning (ICML) (pp. 1064\u20131071). New York: ACM."},{"key":"5390_CR21","first-page":"1033","volume-title":"International conference on machine learning (ICML)","author":"T. Tieleman","year":"2009","unstructured":"Tieleman, T., & Hinton, G. E. (2009). Using fast weights to improve persistent contrastive divergence. In A. Pohoreckyj Danyluk, L. Bottou, & M. L. Littman (Eds.), International conference on machine learning (ICML) (pp. 1033\u20131040). New York: ACM."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-013-5390-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-013-5390-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-013-5390-3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T01:27:22Z","timestamp":1645925242000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-013-5390-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,7,3]]},"references-count":21,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2013,10]]}},"alternative-id":["5390"],"URL":"https:\/\/doi.org\/10.1007\/s10994-013-5390-3","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,7,3]]}}}