Papers › The VampPrior Mixture Model

The VampPrior Mixture Model

6 Feb 2024arXiv:2402.04412archive 2025-07-28

Andrew Stirn, David A. Knowles

Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations. Addressing these deficiencies could greatly benefit deep learning-based scRNA-seq analysis by performing integration and clustering simultaneously. We adapt the VampPrior (Tomczak & Welling, 2018) into a Dirichlet process Gaussian mixture model, resulting in the VampPrior Mixture Model (VMM), a novel prior for DLVMs. We propose an inference procedure that alternates between variational inference and Empirical Bayes to cleanly distinguish variational and prior parameters. Using the VMM in a Variational Autoencoder attains highly competitive clustering performance on benchmark datasets. Augmenting scVI (Lopez et al., 2018), a popular scRNA-seq integration method, with the VMM significantly improves its performance and automatically arranges cells into biologically meaningful clusters.

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Tasks

Clustering Image Clustering Unsupervised Image Classification Variational Inference model

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Clustering	Fashion-MNIST	VMM	Accuracy	0.716	#2 of 13	Archive leaderboard	report
Image Clustering	Fashion-MNIST	VMM	NMI	0.710	#2 of 13	Archive leaderboard	report
Image Clustering	MNIST-full	VMM	Accuracy	0.967	#12 of 16	Archive leaderboard	report
Image Clustering	MNIST-full	VMM	NMI	0.920	#12 of 16	Archive leaderboard	report
Unsupervised Image Classification	MNIST	VMM	Accuracy	96.74	#5 of 10	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Variational Inference

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