Laplace Redux--Effortless Bayesian Deep Learning E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ... NeurIPS 2021, 2021 | 244 | 2021 |
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining A Tripp*, E Daxberger*, JM Hernández-Lobato NeurIPS 2020, 2020 | 130 | 2020 |
Embedding Models for Episodic Knowledge Graphs Y Ma, V Tresp, EA Daxberger Journal of Web Semantics, 2018 | 108 | 2018 |
Bayesian Deep Learning via Subnetwork Inference E Daxberger, E Nalisnick, JU Allingham, J Antorán, ... ICML 2021, 2021 | 98* | 2021 |
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection E Daxberger, JM Hernández-Lobato Bayesian Deep Learning Workshop, NeurIPS 2019, 2019 | 56 | 2019 |
Distributed Batch Gaussian Process Optimization EA Daxberger, BKH Low ICML 2017, 2017 | 55 | 2017 |
Mixed-Variable Bayesian Optimization E Daxberger*, A Makarova*, M Turchetta, A Krause IJCAI 2020, 2020 | 49 | 2020 |
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning J Antorán, D Janz, JU Allingham, E Daxberger, R Barbano, E Nalisnick, ... ICML 2022, 2022 | 29* | 2022 |
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 | 20 | 2021 |
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts E Daxberger, F Weers, B Zhang, T Gunter, R Pang, M Eichner, ... arXiv 2023, 2023 | 3 | 2023 |
Improving Continual Learning by Accurate Gradient Reconstructions of the Past E Daxberger, S Swaroop, K Osawa, R Yokota, RE Turner, ... TMLR 2023, 2023 | 1 | 2023 |
Advances in Probabilistic Deep Learning and Their Applications EA Daxberger University of Cambridge, 2023 | | 2023 |