Learning Hierarchical Variational Autoencoders With Mutual Information Maximization for Autoregressive Sequence Modeling

Variational autoencoders (VAEs) are a class of effective deep generative models, with the objective to approximate the true, but unknown data distribution. VAEs make use of latent variables to capture high-level semantics so as to reconstruct the data well with the help of informative latent variabl...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 45; H. 2; S. 1949 - 1962
Hauptverfasser:	Qian, Dong, Cheung, William K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Autoregressive models Computational modeling Data models Decoding hierarchical variational autoencoders (HVAEs) Modelling Mutual information mutual information neural estimation neural autoregressive sequence modeling Neural networks Predictive models Semantics Sequences Task analysis Training Variational autoencoders (VAEs)
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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