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Learnable Explicit Density for Continuous Latent Space and Variational Inference
[article]
2017
arXiv
pre-print
In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample generation and inference. Second, we analyze the family of inverse autoregressive flows (inverse AF) and show that with further improvement, inverse AF could be used as universal approximation to any
arXiv:1710.02248v1
fatcat:caospxxwezcrfd2h5td7tigooe