Laplace Redux--Effortless Bayesian Deep Learning E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ... NeurIPS, 2021 | 252 | 2021 |
Continual deep learning by functional regularisation of memorable past P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan NeurIPS, 2020 | 133 | 2020 |
Improving predictions of Bayesian neural nets via local linearization A Immer, M Korzepa, M Bauer AISTATS, 2021 | 131 | 2021 |
Approximate inference turns deep networks into gaussian processes ME Khan, A Immer, E Abedi, M Korzepa NeurIPS, 2019 | 121 | 2019 |
Scalable marginal likelihood estimation for model selection in deep learning A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan ICML, 2021 | 104 | 2021 |
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 | 35 | 2022 |
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 | 33 | 2023 |
Probing as Quantifying the Inductive Bias of Pre-trained Representations A Immer*, LT Hennigen*, V Fortuin, R Cotterell ACL, 2022 | 23* | 2022 |
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels A Immer, TFA van der Ouderaa, M van der Wilk, G Rätsch, B Schölkopf ICML, 2023 | 12 | 2023 |
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 | 11* | 2024 |
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures R Eschenhagen, A Immer, RE Turner, F Schneider, P Hennig NeurIPS, 2023 | 10 | 2023 |
Pathologies in priors and inference for Bayesian transformers T Cinquin, A Immer, M Horn, V Fortuin AABI 2022, 2021 | 9 | 2021 |
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 | 9 | 2015 |
Sub-Matrix Factorization for Real-Time Vote Prediction A Immer*, V Kristof*, M Grossglauser, P Thiran KDD, 2020 | 7 | 2020 |
Learning Layer-wise Equivariances Automatically using Gradients TFA van der Ouderaa, A Immer, M van der Wilk NeurIPS, 2023 | 6 | 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 | 6 | 2023 |
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks A Immer, E Palumbo, A Marx, JE Vogt NeurIPS, 2023 | 5 | 2023 |
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion A Meterez, A Joudaki, F Orabona, A Immer, G Rätsch, H Daneshmand ICLR, 2024 | 3 | 2024 |
Improving Neural Additive Models with Bayesian Principles K Bouchiat, A Immer, H Yèche, G Rätsch, V Fortuin ICML, 2024 | 3* | 2024 |
Efficient learning of smooth probability functions from Bernoulli tests with guarantees P Rolland, A Kavis, A Immer, A Singla, V Cevher ICML, 2019 | 3 | 2019 |