5 citations · 5 across the 1 of their papers we have counts for
3 papers
stat.ML2020★ 5 cited
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…
stat.ML2020
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…
stat.ML2018
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…