| Laplace Redux--Effortless Bayesian Deep Learning E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ... NeurIPS 2021, 2021 | 332 | 2021 |
| Practical deep learning with Bayesian principles K Osawa, S Swaroop, A Jain, R Eschenhagen, RE Turner, R Yokota, ... NeurIPS 2019, 2019 | 294 | 2019 |
| Continual deep learning by functional regularisation of memorable past P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan NeurIPS 2020, 2020 | 152 | 2020 |
| Benchmarking neural network training algorithms GE Dahl, F Schneider, Z Nado, N Agarwal, CS Sastry, P Hennig, ... arXiv preprint arXiv:2306.07179, 2023 | 25 | 2023 |
| Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning R Eschenhagen, E Daxberger, P Hennig, A Kristiadi Bayesian Deep Learning Workshop, NeurIPS 2021, 2021 | 25 | 2021 |
| Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures R Eschenhagen, A Immer, RE Turner, F Schneider, P Hennig NeurIPS 2023, 2023 | 19 | 2023 |
| Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks A Kristiadi, R Eschenhagen, P Hennig NeurIPS 2022, 2022 | 12 | 2022 |
| Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization A Kristiadi, A Immer, R Eschenhagen, V Fortuin AABI 2023, 2023 | 8 | 2023 |
| Approximate Bayesian neural operators: Uncertainty quantification for parametric PDEs E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig arXiv preprint arXiv:2208.01565, 2022 | 8 | 2022 |
| Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC W Lin, F Dangel, R Eschenhagen, K Neklyudov, A Kristiadi, RE Turner, ... ICML 2024, 0 | 8* | |
| Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective W Lin, F Dangel, R Eschenhagen, J Bae, RE Turner, A Makhzani ICML 2024, 2024 | 7 | 2024 |
| Influence Functions for Scalable Data Attribution in Diffusion Models B Mlodozeniec, R Eschenhagen, J Bae, A Immer, D Krueger, R Turner ICLR 2025, 2024 | 1 | 2024 |
| Natural Gradient Variational Inference for Continual Learning in Deep Neural Networks R Eschenhagen University of Osnabrück, 2019 | 1 | 2019 |
| Spectral-factorized Positive-definite Curvature Learning for NN Training W Lin, F Dangel, R Eschenhagen, J Bae, RE Turner, RB Grosse arXiv preprint arXiv:2502.06268, 2025 | | 2025 |
| 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 |
| Fast Fractional Natural Gradient Descent using Learnable Spectral Factorizations W Lin, F Dangel, R Eschenhagen, J Bae, RE Turner, RB Grosse | | |
Richard E TurnerProfessor, University of Cambridgecam.ac.uk의 이메일 확인됨