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stat.ML2024
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…
stat.ML2023★ 2 cited
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…
stat.ML2016
Stratification of patient trajectories using covariate latent variable models
Kieran R. Campbell, Christopher Yau
Standard models assign disease progression to discrete categories or stages based on well-characterized clinical markers. However, such a system is potentially at odds with our und…