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Nonparametric Variational Auto-encoders for Hierarchical Representation Learning

ICCV 2017 • Prasoon Goyal • Zhiting Hu • Xiaodan Liang • Chenyu Wang • Eric Xing

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... (read more)

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