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dc.contributor.author Li, Qing -
dc.contributor.author Chen, Yang -
dc.contributor.author Kim, Yongjune -
dc.date.accessioned 2020-11-11T02:32:49Z -
dc.date.available 2020-11-11T02:32:49Z -
dc.date.created 2020-10-08 -
dc.date.issued 2020-12 -
dc.identifier.issn 0090-6778 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12484 -
dc.description.abstract We answer two questions in this work: what Deep Boltzmann Machines (DBMs) can do for compression and vise versa. We show that (1) DBMs can be applied to learn the rate distortion approaching posterior as in the Blahut-Arimoto (BA) algorithm, and to construct a lossy source compression scheme based on the Deep AutoEncoder; (2) compression can improve DBMs' training performances via compression-based denoising algorithms. The implementation of the BA algorithm in the form of DBMs is the foundation of the two applications. © 1972-2012 IEEE. -
dc.language English -
dc.publisher Institute of Electrical and Electronics Engineers Inc. -
dc.title Compression By and For Deep Boltzmann Machines -
dc.type Article -
dc.identifier.doi 10.1109/tcomm.2020.3020796 -
dc.identifier.scopusid 2-s2.0-85097976578 -
dc.identifier.bibliographicCitation IEEE Transactions on Communications, v.68, no.12, pp.7498 - 7510 -
dc.description.isOpenAccess FALSE -
dc.subject.keywordAuthor Image coding -
dc.subject.keywordAuthor Rate-distortion -
dc.subject.keywordAuthor Noise reduction -
dc.subject.keywordAuthor Machine learning -
dc.subject.keywordAuthor Training -
dc.subject.keywordAuthor Distortion -
dc.subject.keywordAuthor Source coding -
dc.subject.keywordAuthor Rate distortion -
dc.subject.keywordAuthor lossy source coding -
dc.subject.keywordAuthor deep Boltzmann machines -
dc.subject.keywordAuthor deep auto-encoder -
dc.subject.keywordAuthor Blahut-Arimoto algorithm -
dc.subject.keywordAuthor denoising -
dc.subject.keywordPlus CAPACITY -
dc.subject.keywordPlus CODES -
dc.subject.keywordPlus REPRESENTATIONS -
dc.subject.keywordPlus INFORMATION -
dc.citation.endPage 7510 -
dc.citation.number 12 -
dc.citation.startPage 7498 -
dc.citation.title IEEE Transactions on Communications -
dc.citation.volume 68 -
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Department of Electrical Engineering and Computer Science Information, Computing, and Intelligence Laboratory 1. Journal Articles

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