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Gunnar Rätsch

Improving Neural Additive Models with Bayesian Principles
Bayesian neural network priors revisited
Invariance learning in deep neural networks with differentiable Laplace approximations
On Disentanglement in Gaussian Process Variational Autoencoders
Scalable Gaussian process variational autoencoders
Scalable marginal likelihood estimation for model selection in deep learning
Sparse Gaussian processes on discrete domains
T-DPSOM: An interpretable clustering method for unsupervised learning of patient health states
GP-VAE: Deep probabilistic time series imputation
DPSOM: Deep probabilistic clustering with self-organizing maps
Meta-learning mean functions for Gaussian processes
META$^2$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning
SOM-VAE: Interpretable discrete representation learning on time series

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