Erik A. Daxberger

According to our database, Erik A. Daxberger authored at least 14 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

MM-Ego: Towards Building Egocentric Multimodal LLMs.

Hanrong Ye, Haotian Zhang, Erik A. Daxberger, Lin Chen, Zongyu Lin, Yanghao Li, Bowen Zhang, Haoxuan You, Dan Xu, Zhe Gan, Jiasen Lu, Yinfei Yang

CoRR, 2024

2023

Improving Continual Learning by Accurate Gradient Reconstructions of the Past.

Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan

Trans. Mach. Learn. Res., 2023

Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts.

Erik A. Daxberger, Floris Weers, Bowen Zhang, Tom Gunter, Ruoming Pang, Marcin Eichner, Michael Emmersberger, Yinfei Yang, Alexander Toshev, Xianzhi Du

CoRR, 2023

2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.

Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2022

2021

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning.

Runa Eschenhagen, Erik A. Daxberger, Philipp Hennig, Agustinus Kristiadi

CoRR, 2021

Laplace Redux - Effortless Bayesian Deep Learning.

Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2020

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining.

Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Mixed-Variable Bayesian Optimization.

Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019

Embedding models for episodic knowledge graphs.

Yunpu Ma, Volker Tresp, Erik A. Daxberger

J. Web Semant., 2019

Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection.

Erik A. Daxberger, José Miguel Hernández-Lobato

CoRR, 2019

2018

Embedding Models for Episodic Memory.

Yunpu Ma, Volker Tresp, Erik A. Daxberger

CoRR, 2018

2017

Distributed Batch Gaussian Process Optimization.

Erik A. Daxberger, Bryan Kian Hsiang Low

Proceedings of the 34th International Conference on Machine Learning, 2017