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Found 5 papers, 4 papers with code
Disentangling shared and private latent factors in multimodal Variational Autoencoders
Common or shared factors could be important for explaining variation across modalities whereas other factors may be private and important only for the explanation of a single modality.
Neural Decomposition: Functional ANOVA with Variational Autoencoders
Our goal is to provide a feature-level variance decomposition, i. e. to decompose variation in the data by separating out the marginal additive effects of latent variables z and fixed inputs c from their non-linear interactions.
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders
Variational Autoencoders (VAEs) provide a flexible and scalable framework for non-linear dimensionality reduction.
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations.
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models
Bayesian inference for factorial hidden Markov models is challenging due to the exponentially sized latent variable space.
