| Laplace Redux--Effortless Bayesian Deep Learning E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ... NeurIPS, 2021 | 517 | 2021 |
| Continual deep learning by functional regularisation of memorable past P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan NeurIPS, 2020 | 198 | 2020 |
| Improving predictions of Bayesian neural nets via local linearization A Immer, M Korzepa, M Bauer AISTATS, 2021 | 187 | 2021 |
| Scalable marginal likelihood estimation for model selection in deep learning A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan ICML, 2021 | 155 | 2021 |
| Approximate inference turns deep networks into gaussian processes ME Khan, A Immer, E Abedi, M Korzepa NeurIPS, 2019 | 153 | 2019 |
| Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ... ICML, 2024 | 101* | 2024 |
| On the Identifiability and Estimation of Causal Location-Scale Noise Models A Immer, C Schultheiss, JE Vogt, B Schölkopf, P Bühlmann, A Marx ICML, 2023 | 65 | 2023 |
| Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations A Immer*, TFA van der Ouderaa*, V Fortuin, G Rätsch, M van der Wilk NeurIPS, 2022 | 63 | 2022 |
| Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures R Eschenhagen, A Immer, RE Turner, F Schneider, P Hennig NeurIPS, 2023 | 48 | 2023 |
| Probing as Quantifying the Inductive Bias of Pre-trained Representations A Immer*, LT Hennigen*, V Fortuin, R Cotterell ACL, 2022 | 36* | 2022 |
| Effective Bayesian Heteroscedastic Regression with Deep Neural Networks A Immer, E Palumbo, A Marx, JE Vogt NeurIPS, 2023 | 29 | 2023 |
| Learning Layer-wise Equivariances Automatically using Gradients TFA van der Ouderaa, A Immer, M van der Wilk NeurIPS, 2023 | 23 | 2023 |
| Influence functions for scalable data attribution in diffusion models B Mlodozeniec, R Eschenhagen, J Bae, A Immer, D Krueger, R Turner arXiv preprint arXiv:2410.13850, 2024 | 22 | 2024 |
| Improving Neural Additive Models with Bayesian Principles K Bouchiat, A Immer, H Yèche, G Rätsch, V Fortuin ICML, 2024 | 19* | 2024 |
| Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels A Immer, TFA van der Ouderaa, M van der Wilk, G Rätsch, B Schölkopf ICML, 2023 | 19 | 2023 |
| Promises and pitfalls of the linearized Laplace in Bayesian optimization A Kristiadi, A Immer, R Eschenhagen, V Fortuin arXiv preprint arXiv:2304.08309, 2023 | 16 | 2023 |
| Pathologies in priors and inference for Bayesian transformers T Cinquin, A Immer, M Horn, V Fortuin AABI 2022, 2021 | 15 | 2021 |
| Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion A Meterez, A Joudaki, F Orabona, A Immer, G Rätsch, H Daneshmand ICLR, 2024 | 11 | 2024 |
| Optimizing routes of public transportation systems by analyzing the data of taxi rides K Richly, R Teusner, A Immer, F Windheuser, L Wolf Proceedings of the 1st international ACM SIGSPATIAL workshop on smart cities …, 2015 | 11 | 2015 |
| Shaving weights with Occam's razor: Bayesian sparsification for neural networks using the marginal likelihood R Dhahri, A Immer, B Charpentier, S Günnemann, V Fortuin Advances in Neural Information Processing Systems 37, 24959-24989, 2024 | 9 | 2024 |
Vincent FortuinPrincipal Investigator, Helmholtz AI & TU MunichVerified email at tum.de
