Papers with von Mises-Fisher
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation (D19-1)
Copied to clipboard
| Challenge: | Existing methods favor uninformative and non replier-specific responses due to lack of relevant information guidance. |
| Approach: | They propose to use a semi-supervised variable network to generate replier-specific responses . they use vMF as latent space to obtain stable KL performance . |
| Outcome: | The proposed model outperforms baseline models on two large conversation datasets and generates diverse and replier-specific responses. |
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent Neural Topic Models (NTMs) have limited applications in the real world due to the challenge of incorporating human knowledge. |
| Approach: | They propose a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher variational autoencoders and optimal transport. |
| Outcome: | The proposed method outperforms existing semi-supervised topic modeling methods on multiple aspects. |
Spherical Latent Spaces for Stable Variational Autoencoders (D18-1)
Copied to clipboard
| Challenge: | Variational autoencoders use a multivariate Gaussian latent variable to capture latent structure in data. |
| Approach: | They propose a variational autoencoder which uses a latent distribution instead of Gaussian . they find that the variational posterior averts the KL collapse by a fixed hyperparameter . |
| Outcome: | The von Mises-Fisher distribution averts the KL collapse and gives better likelihoods than Gaussian models across a range of modeling conditions. |