| Understanding probabilistic sparse Gaussian process approximations M Bauer, M van der Wilk, CE Rasmussen Advances in neural information processing systems 29, 2016 | 271 | 2016 |
| Meta-learning probabilistic inference for prediction J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner arXiv preprint arXiv:1805.09921, 2018 | 266 | 2018 |
| Laplace redux-effortless bayesian deep learning E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, P Hennig Advances in Neural Information Processing Systems 34, 20089-20103, 2021 | 160 | 2021 |
| Resampled priors for variational autoencoders M Bauer, A Mnih The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 118 | 2019 |
| Improving predictions of Bayesian neural nets via local linearization A Immer, M Korzepa, M Bauer International conference on artificial intelligence and statistics, 703-711, 2021 | 96 | 2021 |
| Learning Invariances using the Marginal Likelihood M van der Wilk, M Bauer, ST John, J Hensman Advances in Neural Information Processing Systems, 9938-9948, 2018 | 84* | 2018 |
| Discriminative k-shot learning using probabilistic models M Bauer, M Rojas-Carulla, JB Swiatkowski, B Schölkopf, RE Turner 2nd Bayesian Deep Learning Workshop, NIPS 2017, 2017 | 81 | 2017 |
| Scalable marginal likelihood estimation for model selection in deep learning A Immer, M Bauer, V Fortuin, G Rätsch, KM Emtiyaz International Conference on Machine Learning, 4563-4573, 2021 | 79 | 2021 |
| Ecological feedback in quorum-sensing microbial populations can induce heterogeneous production of autoinducers M Bauer, J Knebel, M Lechner, P Pickl, E Frey Elife 6, e25773, 2017 | 43 | 2017 |
| Interpretable and differentially private predictions F Harder, M Bauer, M Park Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4083-4090, 2020 | 38 | 2020 |
| Automatic estimation of modulation transfer functions M Bauer, V Volchkov, M Hirsch, B Schcölkopf 2018 IEEE International Conference on Computational Photography (ICCP), 1-12, 2018 | 13 | 2018 |
| Versa: Versatile and efficient few-shot learning J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner Third workshop on Bayesian Deep Learning, 2018 | 12 | 2018 |
| Spatial functa: Scaling functa to imagenet classification and generation M Bauer, E Dupont, A Brock, D Rosenbaum, J Schwarz, H Kim arXiv preprint arXiv:2302.03130, 2023 | 8 | 2023 |
| Regularising for invariance to data augmentation improves supervised learning A Botev, M Bauer, S De arXiv preprint arXiv:2203.03304, 2022 | 6 | 2022 |
| Generalized doubly reparameterized gradient estimators M Bauer, A Mnih International Conference on Machine Learning, 738-747, 2021 | 6 | 2021 |
| Lindblad driving for nonequilibrium steady-state transport for noninteracting quantum impurity models M Bauer Bachelor Thesis, University of Munich, 2011 | 5 | 2011 |
| Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner Meta Learning Workshop, NeurIPS 2018, 2018 | 2 | 2018 |
| Automated Detection of Complex Tactical Patterns in Football MSP Bauer, S Ingbert | 1 | 2021 |
| C3: High-performance and low-complexity neural compression from a single image or video H Kim, M Bauer, L Theis, JR Schwarz, E Dupont arXiv preprint arXiv:2312.02753, 2023 | | 2023 |
| Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning M Bauer University of Cambridge, 2020 | | 2020 |
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
