Papers by Prince Zizhuang Wang
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling (N19-1)
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| Challenge: | Empirical experiments show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse. |
| Approach: | They propose a Variational Wasserstein Autoencoder with Riemannian Normalizing Flow to solve this problem by transforming a latent variable into a space that respects the geometric characteristics of input space. |
| Outcome: | Empirical results show that the proposed model avoids KLvanishing and has better performance in language modeling, likelihood approximation, and text generation tasks. |
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. |
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond (2020.acl-main)
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| Challenge: | Existing work has shown that the optimization of variational autoencoders suffers from the posterior collapse problem. |
| Approach: | They propose a variational autoencoder that couples a VAE model with a deterministic autoencoding model and improves the parameters via weight sharing and decoder signal matching. |
| Outcome: | The proposed model improves on benchmark datasets and improves diversity of dialogue generation. |