13 citations · 20 across the 5 of their papers we have counts for
7 papers
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…
Neural networks with late-phase weights
Johannes von Oswald, Seijin Kobayashi, Alexander Meulemans +3
The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found…
Continual Learning in Recurrent Neural Networks
Benjamin Ehret, Christian Henning, Maria R. Cervera +3
While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing s…
Continual learning with hypernetworks
Johannes von Oswald, Christian Henning, Benjamin F. Grewe +1
Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. To overcome this problem, we present a novel approach based on…