Information Bottleneck: Theory and Applications in Deep Learning

Bernhard C. Geiger; Gernot Kubin

doi:10.3390/e22121408

Information Bottleneck: Theory and Applications in Deep Learning

^*Corresponding author for this work

Research output: Contribution to journal › Editorial

Original language	English
Article number	1408
Pages (from-to)	1-4
Number of pages	4
Journal	Entropy
Volume	22
Issue number	12
DOIs	https://doi.org/10.3390/e22121408
Publication status	Published - 1 Dec 2020

Keywords

Deep learning
Information bottleneck
Neural networks

ASJC Scopus subject areas

Physics and Astronomy(all)

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10.3390/e22121408Licence: CC BY 4.0

Cite this

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title = "Information Bottleneck: Theory and Applications in Deep Learning",

keywords = "Deep learning, Information bottleneck, Neural networks",

author = "Geiger, {Bernhard C.} and Gernot Kubin",

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year = "2020",

month = dec,

day = "1",

doi = "10.3390/e22121408",

language = "English",

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pages = "1--4",

journal = "Entropy",

issn = "1099-4300",

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AU - Geiger, Bernhard C.

AU - Kubin, Gernot

N1 - guest editorial for special issue

PY - 2020/12/1

Y1 - 2020/12/1

KW - Deep learning

KW - Information bottleneck

KW - Neural networks

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M3 - Editorial

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EP - 4

JO - Entropy

JF - Entropy

IS - 12

M1 - 1408

ER -

Information Bottleneck: Theory and Applications in Deep Learning

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this