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Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
[article]
2017
arXiv
pre-print
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors
arXiv:1703.07027v2
fatcat:fllrzupobrfhjpjeejjgzgmve4