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Invariance learning in deep neural networks with differentiable Laplace approximations

Alexander Immer, Tycho van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk

Priors in Bayesian deep learning: A review

Vincent Fortuin

James Urquhart Allingham, Florian Wenzel, Zelda E Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton

A Bayesian Approach to Invariant Deep Neural Networks

Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin

BNNpriors: A library for Bayesian neural network inference with different prior distributions

Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Margherita Rosnati, Vincent Fortuin

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data

Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen

On Stein variational neural network ensembles

Francesco D'Angelo, Vincent Fortuin, Florian Wenzel

Repulsive deep ensembles are Bayesian

Francesco D'Angelo, Vincent Fortuin

Sparse Gaussian processes on discrete domains

Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch