5 citations · 5 across the 4 of their papers we have counts for
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Disentangling shared and private latent factors in multimodal Variational Autoencoders
Kaspar Märtens, Christopher Yau
Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or share…
Neural Decomposition: Functional ANOVA with Variational Autoencoders
Kaspar Märtens, Christopher Yau
Variational Autoencoders (VAEs) have become a popular approach for dimensionality reduction. However, despite their ability to identify latent low-dimensional structures embedded w…
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders
Kaspar Märtens, Christopher Yau
Variational Autoencoders (VAEs) provide a flexible and scalable framework for non-linear dimensionality reduction. However, in application domains such as genomics where data sets…
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar Märtens, Kieran R. Campbell, Christopher Yau
The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. Howeve…