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Friday, April 24, 2020 | History

2 edition of Cross-entropy minimization given fully-decomposable subset and aggregate constraints found in the catalog.

Cross-entropy minimization given fully-decomposable subset and aggregate constraints

John E. Shore

Cross-entropy minimization given fully-decomposable subset and aggregate constraints

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  • 35 Currently reading

Published by Naval Research Laboratory in Washington, D.C .
Written in English

    Subjects:
  • Entropy (Information theory),
  • Queuing theory.,
  • Computer networks.

  • Edition Notes

    StatementJohn E. Shore.
    SeriesNRL memorandum report -- 4430
    ContributionsNaval Research Laboratory (U.S.). Information Processing Systems Branch.
    The Physical Object
    Paginationiii, 22 p. ;
    Number of Pages22
    ID Numbers
    Open LibraryOL22442815M


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Cross-entropy minimization given fully-decomposable subset and aggregate constraints by John E. Shore Download PDF EPUB FB2

Get this from a library. Cross-entropy minimization given fully-decomposable subset and aggregate constraints. [John E Shore; Naval Research Laboratory (U.S.). Information Processing Systems Branch.]. John E. Shore has written: 'Cross-entropy minimization given fully-decomposable subset and aggregate constraints' -- subject(s): Computer networks, Entropy (Information theory), Queuing theory.

Cross-entropy minimization given fully decomposable subset and aggregate constraints (Corresp.) December IEEE Transactions on Information Theory John E.

Shore. Managing Derivative Exposure Article. April ; Source; Cross-entropy minimization given fully decomposable subset and aggregate constraints. New properties of minimum cross Author: Ulrich Kirchner.

[51] J. Shore, Cross-Entropy Minimization Given Fully Decomposable Subset of Aggregate Constraints, IEEE Transactions on Information The., IT(6), In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.

First, we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks.

Atrous convolution allows us to explicitly control the resolution at which Cited by: 1 DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen, George Papandreou, Senior Member, IEEE, Iasonas Kokkinos, Member, IEEE. Privacy-Preserving Data Mining Models and Algorithms ADVANCES IN DATABASE SYSTEMS Volume 34 Series EditorsAhmed K.

Cogsci17 Proceedings - Free ebook download as PDF File .pdf), Text File .txt) or read book online for free. Cognitive Science from In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally show.

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