{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-vampprior-mixture-model","title":"The VampPrior Mixture Model","arxiv_id":"2402.04412","date":"2024-02-06","proceeding":null,"authors":["Andrew Stirn","David A. Knowles"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2402.04412v2","url_pdf":"https://arxiv.org/pdf/2402.04412v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-vampprior-mixture-model","repo_url":"https://github.com/astirn/vampprior-mixture-model","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-clustering","task_name":"Image Clustering"},{"task_slug":"unsupervised-image-classification","task_name":"Unsupervised Image Classification"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"variational-inference","method_name":"Variational Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-clustering-on-fashion-mnist","task":"Image Clustering","dataset":"Fashion-MNIST","model":"VMM","rank_in_archive_order":2,"of":13,"metrics":{"Accuracy":"0.716","NMI":"0.710"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-mnist-full","task":"Image Clustering","dataset":"MNIST-full","model":"VMM","rank_in_archive_order":12,"of":16,"metrics":{"Accuracy":"0.967","NMI":"0.920"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-image-classification-on-mnist","task":"Unsupervised Image Classification","dataset":"MNIST","model":"VMM","rank_in_archive_order":5,"of":10,"metrics":{"Accuracy":"96.74"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}