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Erik Daxberger
Erik Daxberger
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Title
Cited by
Cited by
Year
Laplace Redux--Effortless Bayesian Deep Learning
E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ...
NeurIPS 2021, 2021
2472021
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
A Tripp*, E Daxberger*, JM Hernández-Lobato
NeurIPS 2020, 2020
1302020
Embedding Models for Episodic Knowledge Graphs
Y Ma, V Tresp, EA Daxberger
Journal of Web Semantics, 2018
1092018
Bayesian Deep Learning via Subnetwork Inference
E Daxberger, E Nalisnick, JU Allingham, J Antorán, ...
ICML 2021, 2021
100*2021
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
E Daxberger, JM Hernández-Lobato
Bayesian Deep Learning Workshop, NeurIPS 2019, 2019
562019
Distributed Batch Gaussian Process Optimization
EA Daxberger, BKH Low
ICML 2017, 2017
552017
Mixed-Variable Bayesian Optimization
E Daxberger*, A Makarova*, M Turchetta, A Krause
IJCAI 2020, 2020
492020
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
202021
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
32023
Improving Continual Learning by Accurate Gradient Reconstructions of the Past
E Daxberger, S Swaroop, K Osawa, R Yokota, RE Turner, ...
TMLR 2023, 2023
12023
Advances in Probabilistic Deep Learning and Their Applications
EA Daxberger
University of Cambridge, 2023
2023
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Articles 1–12