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Research map / Probabilistic and scientific modeling / Variational and generative sampling

Unsupervised Learning with Contrastive Latent Variable Models

AAAI 2019 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Hierarchical VAEs and posterior collapse · 43 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use robust cLVM instead of cPCA with mean imputationMice protein expression · Mice protein expression, natural missingness and artificially increased missingnesssubgroup discoveryHelped
Use robust cLVM instead of standard cLVM (ablation)Synthetic data with outliers · Synthetic data with outliersrecovery of target latent representationHelped
Add ARD for automatic selection of the shared latent dimension (ablation)cLVM · Single-cell RNA-Seqsubgroup discoveryHelped
Add ARD for automatic selection of the shared latent dimension (ablation)cLVM · Synthetic data with cluster-specific target latent meansshared-dimension selectionHelped
Add group sparsity regularization to the target factor loading (ablation)cLVM · mHealth sensor datafeature selectionHelped
Use a horseshoe prior for the target factor loading (ablation)cLVM · mHealth sensor datafeature selectionHelped
Use cVAE instead of a standard VAE with the same combined latent dimensionalityVAE · MNIST digits on grassdigit clustering in latent spaceHelped
Use a two-dimensional target latent space and an eight-dimensional shared space to separate target and background structure (ablation)cVAE · MNIST digits on grassselective generation of target and backgroundHelped
Use cLVM instead of PCAcLVM · Single-cell RNA-Seqsubgroup discoveryHelped
Use cLVM instead of PCAcLVM · Mice protein expressionsubgroup discoveryHelped
Set the shared latent dimension to zero (ablation)cLVM · Synthetic datacontrastive latent structureMixed
Use a Student's t likelihood instead of a Gaussian likelihood (ablation)cLVM · Synthetic data with outliersrecovery of target latent representationHelped

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