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ORCIDErik A. Daxberger
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2020 – today
- 2023
[j2]Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan:
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023)- [i9]Erik A. Daxberger, Floris Weers, Bowen Zhang, Tom Gunter, Ruoming Pang, Marcin Eichner, Michael Emmersberger, Yinfei Yang, Alexander Toshev, Xianzhi Du:
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts. CoRR abs/2309.04354 (2023) - 2022
- [c6]Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML 2022: 796-821 - [i8]Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR abs/2206.08900 (2022) - 2021
- [c5]Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Bayesian Deep Learning via Subnetwork Inference. ICML 2021: 2510-2521 - [c4]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS 2021: 20089-20103 - [i7]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. CoRR abs/2106.14806 (2021) - [i6]Runa Eschenhagen, Erik A. Daxberger, Philipp Hennig, Agustinus Kristiadi:
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR abs/2111.03577 (2021) - 2020
- [c3]Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause:
Mixed-Variable Bayesian Optimization. IJCAI 2020: 2633-2639 - [c2]Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato:
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. NeurIPS 2020 - [i5]Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato:
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. CoRR abs/2006.09191 (2020) - [i4]Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR abs/2010.14689 (2020)
2010 – 2019
- 2019
- [j1]Yunpu Ma, Volker Tresp, Erik A. Daxberger:
Embedding models for episodic knowledge graphs. J. Web Semant. 59 (2019) - [i3]Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause:
Mixed-Variable Bayesian Optimization. CoRR abs/1907.01329 (2019) - [i2]Erik A. Daxberger, José Miguel Hernández-Lobato:
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. CoRR abs/1912.05651 (2019) - 2018
- [i1]Yunpu Ma, Volker Tresp, Erik A. Daxberger:
Embedding Models for Episodic Memory. CoRR abs/1807.00228 (2018) - 2017
- [c1]Erik A. Daxberger, Bryan Kian Hsiang Low:
Distributed Batch Gaussian Process Optimization. ICML 2017: 951-960
Coauthor Index
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