13 citations · 20 across the 5 of their papers we have counts for
4 papers · 1 filter
Uncertainty estimation under model misspecification in neural network regression
Maria R. Cervera, Rafael Dätwyler, Francesco D'Angelo +3
Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameter…
On out-of-distribution detection with Bayesian neural networks
Francesco D'Angelo, Christian Henning
The question whether inputs are valid for the problem a neural network is trying to solve has sparked interest in out-of-distribution (OOD) detection. It is widely assumed that Bay…
Are Bayesian neural networks intrinsically good at out-of-distribution detection?
Christian Henning, Francesco D'Angelo, Benjamin F. Grewe
The need to avoid confident predictions on unfamiliar data has sparked interest in out-of-distribution (OOD) detection. It is widely assumed that Bayesian neural networks (BNN) are…
Posterior Meta-Replay for Continual Learning
Christian Henning, Maria R. Cervera, Francesco D'Angelo +6
Learning a sequence of tasks without access to i.i.d. observations is a widely studied form of continual learning (CL) that remains challenging. In principle, Bayesian learning dir…