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Bayesian neural network priors revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W Ober, Florian Wenzel, Gunnar Rätsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison
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Bayesian neural network priors revisited
Priors in Bayesian deep learning: A review
Vincent Fortuin
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Priors in Bayesian deep learning: A review
PACOH: Bayes-optimal meta-learning with PAC-guarantees
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause
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PACOH: Bayes-optimal meta-learning with PAC-guarantees
Repulsive deep ensembles are Bayesian
Francesco D'Angelo, Vincent Fortuin
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Repulsive deep ensembles are Bayesian
Scalable marginal likelihood estimation for model selection in deep learning
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
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Scalable marginal likelihood estimation for model selection in deep learning
GP-VAE: Deep probabilistic time series imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
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GP-VAE: Deep probabilistic time series imputation
SOM-VAE: Interpretable discrete representation learning on time series
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
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SOM-VAE: Interpretable discrete representation learning on time series