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  Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders

Bozkurt Varolgunes, Y., Bereau, T., & Rudzinski, J. F. (2020). Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders. Machine Learning: Science and Technology, 1(1): 015012. doi:10.1088/2632-2153/ab80b7.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-69D7-F Version Permalink: https://hdl.handle.net/21.11116/0000-0009-69D8-E
Genre: Journal Article

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Bozkurt_Varolgüneş_2020_Mach._Learn. _Sci._Technol._1_015012.pdf (Publisher version), 13MB
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Bozkurt_Varolgüneş_2020_Mach._Learn. _Sci._Technol._1_015012.pdf
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2020
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 Creators:
Bozkurt Varolgunes, Yasemin1, 2, Author           
Bereau, Tristan1, Author           
Rudzinski, Joseph F.1, Author           
Affiliations:
1Dept. Kremer: Polymer Theory, MPI for Polymer Research, Max Planck Society, ou_1800287              
2Koc Univ, Dept Elect & Elect Engn, Istanbul, Turkey, ou_persistent22              

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Language(s): eng - English
 Dates: 2020-04-27
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1088/2632-2153/ab80b7
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Title: Machine Learning: Science and Technology
  Abbreviation : Mach. Learn.: Sci. Technol.
Source Genre: Journal
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Publ. Info: Bristol, UK : IOP Publishing
Pages: - Volume / Issue: 1 (1) Sequence Number: 015012 Start / End Page: - Identifier: ISSN: 2632-2153
CoNE: https://pure.mpg.de/cone/journals/resource/2632-2153