date: 2021-10-27T12:45:29Z
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pdf:docinfo:title: Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders
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subject: Machine Learning: Science and Technology, 1 (2020) 015012 doi: 10.1088/2632-2153/ab80b7
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dc:title: Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders
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cp:subject: Machine Learning: Science and Technology, 1 (2020) 015012 doi: 10.1088/2632-2153/ab80b7
pdf:docinfo:subject: Machine Learning: Science and Technology, 1 (2020) 015012 doi: 10.1088/2632-2153/ab80b7
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Author: Yasemin Bozkurt Varolgünesto¸ ,Tristan Bereau ,Joseph F Rudzinski
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dc:description: Machine Learning: Science and Technology, 1 (2020) 015012 doi: 10.1088/2632-2153/ab80b7
Keywords: variational autoencoders,dimensionality reduction,clustering,Markov state models,molecular dynamics simulations
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dc:creator: Yasemin Bozkurt Varolgünesto¸ ,Tristan Bereau ,Joseph F Rudzinski
description: Machine Learning: Science and Technology, 1 (2020) 015012 doi: 10.1088/2632-2153/ab80b7
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title: Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders
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creator: Yasemin Bozkurt Varolgünesto¸ ,Tristan Bereau ,Joseph F Rudzinski
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dc:subject: variational autoencoders,dimensionality reduction,clustering,Markov state models,molecular dynamics simulations
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