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arXiv Machine Learning By Andrew A. Stirn, Abhejit Rajagopal, David A. Knowles, Grace Huynh

VampPrior Unleashed

Read the original on arXiv Machine Learning →

The paper introduces improvements to the VampPrior, a prior used in deep latent variable models. By decoupling prior and posterior covariances and replacing fixed uniform mixture weights with a Dirichlet process, the authors achieve higher model evidence and better non‑parametric clustering. Applying the enhanced prior to the single‑cell Variational Inference framework improves integration and clustering across multiple atlases while maintaining a continuous latent space.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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