9 citations · 16 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
On Stein Variational Neural Network Ensembles
Francesco D'Angelo, Vincent Fortuin, Florian Wenzel
Ensembles of deep neural networks have achieved great success recently, but they do not offer a proper Bayesian justification. Moreover, while they allow for averaging of predictio…
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…
Annealed Stein Variational Gradient Descent
Francesco D'Angelo, Vincent Fortuin
Particle based optimization algorithms have recently been developed as sampling methods that iteratively update a set of particles to approximate a target distribution. In particul…