VampPrior Unleashed
Deep latent variable models (DLVMs) underpin many modern analysis pipelines, for example in single-cell genomics, yet their prior distributions have received markedly little attention. The VampPrior approximates the optimal prior (the aggregate posterior) and often attains high model evidence. Here, we identify two key limitations of the VampPrior: it ties prior and posterior uncertainty to a single amortized covariance and clusters poorly due to its fixed uniform mixture weights. We unleash the VampPrior by decoupling its prior and posterior covariances, resolving the conflict of generating high prior and low posterior entropy with the same shared parameters and substantially improving m...
| Authors | Andrew A. Stirn, Abhejit Rajagopal, David A. Knowles et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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