Dynamical Variational Autoencoders

Dynamical Variational Autoencoders

A Comprehensive Review

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Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In this monograph the authors introduce and discuss a general class of models, called dynamical variational autoencoders (DVAEs), which extend VAEs to model temporal vector sequences. In doing so the authors provide:· a formal definition of the general class of DVAEs· a detailed and complete technical description of seven DVAE models· a rapid overview of other DVAE models presented in the recent l...