Papers with variational posterior
Neural Gaussian Copula for Variational Autoencoder (D19-1)
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| Challenge: | Variational language models assume the posterior of latent variables to be factorized even when the true posterior is not. |
| Approach: | They propose a Gaussian Copula Variational Autoencoder to avert a typical training problem called posterior collapse observed in all other variational language models. |
| Outcome: | The proposed model achieves great success over a huge number of tasks, such as transfer learning, unsupervised learning and unsupervised training. |