ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure F Dangel, L Tatzel, P Hennig arXiv preprint arXiv:2106.02624, 2021 | 13 | 2021 |
Reparameterization invariance in approximate Bayesian inference H Roy, M Miani, CH Ek, P Hennig, M Pförtner, L Tatzel, S Hauberg arXiv preprint arXiv:2406.03334, 2024 | 3 | 2024 |
Late-Phase Second-Order Training L Tatzel, P Hennig, F Schneider Has it Trained Yet? NeurIPS 2022 Workshop, 2022 | 3 | 2022 |
Accelerating Generalized Linear Models by Trading off Computation for Uncertainty L Tatzel, J Wenger, F Schneider, P Hennig arXiv preprint arXiv:2310.20285, 2023 | 2 | 2023 |
Debiasing mini-batch quadratics for applications in deep learning L Tatzel, B Mucsányi, O Hackel, P Hennig arXiv preprint arXiv:2410.14325, 2024 | 1 | 2024 |
Position: Curvature Matrices Should Be Democratized via Linear Operators F Dangel, R Eschenhagen, W Ormaniec, A Fernandez, L Tatzel, ... arXiv preprint arXiv:2501.19183, 2025 | | 2025 |
Debiasing Mini-Batch Quadratics for Applications in Deep Learning P Hennig, O Hackel, B Mucsányi, L Tatzel arXiv, 2024 | | 2024 |
Reparameterization invariance in approximate Bayesian inference P Hennig, S Hauberg, L Tatzel, M Pförtner, CH Ek, M Miani, H Roy arXiv, 2024 | | 2024 |
Accelerating Generalized Linear Models by Trading off Computation for Uncertainty P Hennig, F Schneider, J Wenger, L Tatzel arXiv, 2024 | | 2024 |