34 citations · 108 across the 30 of their papers we have counts for
3 papers · 1 filter
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
Runa Eschenhagen, Erik Daxberger, Philipp Hennig +1
Deep neural networks are prone to overconfident predictions on outliers. Bayesian neural networks and deep ensembles have both been shown to mitigate this problem to some extent. I…
Laplace Redux -- Effortless Bayesian Deep Learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer +3
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty qu…
Being a Bit Frequentist Improves Bayesian Neural Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Despite their compelling theoretical properties, Bayesian neural networks (BNNs) tend to perform worse than frequentist methods in classification-based uncertainty quantification (…