281 citations · 421 across the 8 of their papers we have counts for
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cs.LG2019
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text
Bohan Li, Junxian He, Graham Neubig +2
When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework. In practice, however, VAEs are tra…
cs.LG2019★ 128 cited
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig +1
The variational autoencoder (VAE) is a popular combination of deep latent variable model and accompanying variational learning technique. By using a neural inference network to app…