207 citations · 303 across the 20 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…
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
Fabian Falck, Christopher Williams, Dominic Danks +5
U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formu…
Multi-Facet Clustering Variational Autoencoders
Fabian Falck, Haoting Zhang, Matthew Willetts +3
Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one co…
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…
Bayesian Nonparametric Boolean Factor Models
Tammo Rukat, Christopher Yau
We build upon probabilistic models for Boolean Matrix and Boolean Tensor factorisation that have recently been shown to solve these problems with unprecedented accuracy and to enab…