Handling Incomplete Heterogeneous Data using VAEs

10 Jul 2018Alfredo NazabalPablo M. OlmosZoubin GhahramaniIsabel Valera

Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications... (read more)

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