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Information Bottleneck: Theory and Applications in Deep Learning

Detalles Bibliográficos
Autores principales: Geiger, Bernhard C., Kubin, Gernot
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764901/
https://www.ncbi.nlm.nih.gov/pubmed/33327417
http://dx.doi.org/10.3390/e22121408
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author Geiger, Bernhard C.
Kubin, Gernot
author_facet Geiger, Bernhard C.
Kubin, Gernot
author_sort Geiger, Bernhard C.
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spelling pubmed-77649012021-02-24 Information Bottleneck: Theory and Applications in Deep Learning Geiger, Bernhard C. Kubin, Gernot Entropy (Basel) Editorial MDPI 2020-12-14 /pmc/articles/PMC7764901/ /pubmed/33327417 http://dx.doi.org/10.3390/e22121408 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Editorial
Geiger, Bernhard C.
Kubin, Gernot
Information Bottleneck: Theory and Applications in Deep Learning
title Information Bottleneck: Theory and Applications in Deep Learning
title_full Information Bottleneck: Theory and Applications in Deep Learning
title_fullStr Information Bottleneck: Theory and Applications in Deep Learning
title_full_unstemmed Information Bottleneck: Theory and Applications in Deep Learning
title_short Information Bottleneck: Theory and Applications in Deep Learning
title_sort information bottleneck: theory and applications in deep learning
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764901/
https://www.ncbi.nlm.nih.gov/pubmed/33327417
http://dx.doi.org/10.3390/e22121408
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