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Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets.
On a measure of the information provided by an experiment
Dennis V Lindley · 1956
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Information-based objective functions for active data selection
David JC MacKay · 1992
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José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato, and Zoubin Ghahramani · 2014
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Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard E Turner · 2018
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Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2017
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Jakub M Tomczak and Max Welling · 2017
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Isabel Valera and Zoubin Ghahramani · 2017
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Automatic type inference with a nested latent variable model
Neil Dhir, Davide Zilli, Tomasz Rudny, and Alessandra Tosi
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Automatic type inferential general latent feature model
Neil Dhir, Davide Zilli, Tomasz Rudny, and Alessandra Tosi
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Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2018
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Icebreaker: Element-wise active information acquisition with bayesian deep latent gaussian model
Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, and José Miguel Hernández-Lobato · 2019
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Hm-vaes: a deep generative model for real-valued data with heterogeneous marginals
Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard Turner, Jose Miguel Hernandez-Lobato, and Cheng Zhang · 2019
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