| Repulsive Deep Ensembles are Bayesian F D'Angelo, V Fortuin Advances in Neural Information Processing Systems 34, 2021 | 82 | 2021 |
| Posterior meta-replay for continual learning C Henning, M Cervera, F D'Angelo, J Von Oswald, R Traber, B Ehret, ... Advances in Neural Information Processing Systems 34, 2021 | 47 | 2021 |
| Learning the Ising model with generative neural networks F D'Angelo, L Böttcher Physical Review Research 2 (2), 023266, 2020 | 40 | 2020 |
| On Stein Variational Neural Network Ensembles F D'Angelo, V Fortuin, F Wenzel Workshop on Uncertainty & Robustness in Deep Learning (ICML), 2021 | 25 | 2021 |
| Annealed Stein Variational Gradient Descent F D’Angelo, V Fortuin 3rd Symposium on Advances in Approximate Bayesian Inference, 2020, 2020 | 18 | 2020 |
| On out-of-distribution detection with Bayesian neural networks F D'Angelo, C Henning arXiv. org, 2021 | 12* | 2021 |
| Are Bayesian neural networks intrinsically good at out-of-distribution detection? C Henning, F D'Angelo, BF Grewe ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning., 2021 | 9 | 2021 |
| Why Do We Need Weight Decay in Modern Deep Learning? M Andriushchenko, F D'Angelo, A Varre, N Flammarion arXiv preprint arXiv:2310.04415, 2023 | 6 | 2023 |
| Uncertainty estimation under model misspecification in neural network regression MR Cervera, R Dätwyler, F D'Angelo, H Keurti, BF Grewe, C Henning NeurIPS 2021 Workshop Your Model Is Wrong: Robustness and Misspecification …, 2021 | 5 | 2021 |
Christian HenningEthonAI AGDirección de correo verificada de ethz.ch
