Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87524
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dc.contributor.authorLi, Guoqien
dc.contributor.authorDeng, Leien
dc.contributor.authorXu, Yien
dc.contributor.authorWen, Changyunen
dc.contributor.authorWang, Weien
dc.contributor.authorPei, Jingen
dc.contributor.authorShi, Lupingen
dc.date.accessioned2018-11-30T05:28:15Zen
dc.date.accessioned2019-12-06T16:43:43Z-
dc.date.available2018-11-30T05:28:15Zen
dc.date.available2019-12-06T16:43:43Z-
dc.date.issued2016en
dc.identifier.citationLi, G., Deng, L., Xu, Y., Wen, C., Wang, W., Pei, J., & Shi, L. (2016). Temperature based restricted boltzmann machines. Scientific Reports, 6, 19133-. doi:10.1038/srep19133en
dc.identifier.urihttps://hdl.handle.net/10356/87524-
dc.description.abstractRestricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key factor of the Boltzmann distribution that RBMs originate from. However, none of existing schemes have considered the impact of temperature in the graphical model of DBNs. In this work, we propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers. We theoretically prove that the effect of temperature can be adjusted by setting the parameter of the sharpness of the logistic function in the proposed TRBMs. The performance of RBMs can be improved by adjusting the temperature parameter of TRBMs. This work provides a comprehensive insights into the deep belief networks and deep learning architectures from a physical point of view.en
dc.format.extent12 p.en
dc.language.isoenen
dc.relation.ispartofseriesScientific Reportsen
dc.rights© 2016 The Authors (Nature Publishing Group). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/en
dc.subjectRestricted Boltzmann Machinesen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.subjectTemperatureen
dc.titleTemperature based restricted boltzmann machinesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1038/srep19133en
dc.description.versionPublished versionen
dc.identifier.pmid26758235-
item.grantfulltextopen-
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