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Pathologies in Priors and Inference for Bayesian Transformers
Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin
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Probing as quantifying inductive bias
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
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Quantum Bayesian Neural Networks
Noah Berner, Vincent Fortuin, Jonas Landman
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Annealed Stein Variational Gradient Descent
Francesco D'Angelo, Vincent Fortuin
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Exact Langevin Dynamics with Stochastic Gradients
Adrià Garriga-Alonso, Vincent Fortuin
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Factorized Gaussian Process Variational Autoencoders
Metod Jazbec, Michael Arthur Leopold Pearce, Vincent Fortuin
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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
PCA Subspaces Are Not Always Optimal for Bayesian Learning
Alexandre Bense, Amir Joudaki, Tim GJ Rudner, Vincent Fortuin
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Scalable Gaussian process variational autoencoders
Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
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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