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Erik Daxberger
Erik Daxberger
Apple
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Leto
Laplace Redux--Effortless Bayesian Deep Learning
E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ...
NeurIPS 2021, 2021
3642021
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
A Tripp*, E Daxberger*, JM Hernández-Lobato
NeurIPS 2020, 2020
1672020
Bayesian Deep Learning via Subnetwork Inference
E Daxberger, E Nalisnick, JU Allingham, J Antorán, ...
ICML 2021, 2021
132*2021
Embedding Models for Episodic Knowledge Graphs
Y Ma, V Tresp, EA Daxberger
Journal of Web Semantics, 2018
1222018
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
E Daxberger, JM Hernández-Lobato
Bayesian Deep Learning Workshop, NeurIPS 2019, 2019
782019
Mixed-Variable Bayesian Optimization
E Daxberger*, A Makarova*, M Turchetta, A Krause
IJCAI 2020, 2020
622020
Distributed Batch Gaussian Process Optimization
EA Daxberger, BKH Low
ICML 2017, 2017
582017
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
39*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
272021
MM-Ego: Towards Building Egocentric Multimodal LLMs
H Ye, H Zhang, E Daxberger, L Chen, Z Lin, Y Li, B Zhang, H You, D Xu, ...
ICLR 2025, 2024
62024
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
52023
Improving Continual Learning by Accurate Gradient Reconstructions of the Past
E Daxberger, S Swaroop, K Osawa, R Yokota, RE Turner, ...
TMLR 2023, 2023
32023
Advances in Probabilistic Deep Learning and Their Applications
EA Daxberger
University of Cambridge, 2023
2023
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