43 citations · 80 across the 9 of their papers we have counts for
9 papers
Topology in high- QCD via staggered spectral projectors
Andreas Athenodorou, Claudio Bonanno, Claudio Bonati +7
We present preliminary lattice results for the topological susceptibility in high- QCD obtained discretizing this observable via spectral projectors on eigenmodes of t…
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…
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…
Repulsive Deep Ensembles are Bayesian
Francesco D'Angelo, Vincent Fortuin
Deep ensembles have recently gained popularity in the deep learning community for their conceptual simplicity and efficiency. However, maintaining functional diversity between ense…
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…
Learning the Ising Model with Generative Neural Networks
Francesco D'Angelo, Lucas Böttcher
Recent advances in deep learning and neural networks have led to an increased interest in the application of generative models in statistical and condensed matter physics. In parti…