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Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin
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Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
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Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
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Meta-learning richer priors for VAEs
Marcello Massimo Negri, Vincent Fortuin, Jan Stuehmer
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Neural Variational Gradient Descent
Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann
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On Disentanglement in Gaussian Process Variational Autoencoders
Simon Bing, Vincent Fortuin, Gunnar Rätsch
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Pathologies in priors and inference for Bayesian transformers
Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin
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Quantum Bayesian Neural Networks
Noah Berner, Vincent Fortuin, Jonas Landman
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A Bayesian Approach to Invariant Deep Neural Networks
Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin
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Annealed Stein Variational Gradient Descent
Francesco D'Angelo, Vincent Fortuin
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