Erik A. Daxberger

20 papers A* 7Journal 13

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs. CoRR Erik A. Daxberger, Nina Wenzel, David Griffiths, Haiming Gang, Justin Lazarow, Gefen Kohavi, Kai Kang, Marcin Eichner, Yinfei Yang, Afshin Dehghan, Peter Grasch
2025	A*	`conf`	MMEgo: Towards Building Egocentric Multimodal LLMs for Video QA. ICLR 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
2024	J	`jnl`	MM-Ego: Towards Building Egocentric Multimodal LLMs. CoRR 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
2023	J	`jnl`	Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
2023	J	`jnl`	Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts. CoRR Erik A. Daxberger, Floris Weers, Bowen Zhang, Tom Gunter, Ruoming Pang, Marcin Eichner, Michael Emmersberger, Yinfei Yang, Alexander Toshev, Xianzhi Du
2022	A*	`conf`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2021	A*	`conf`	Bayesian Deep Learning via Subnetwork Inference. ICML Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	A*	`conf`	Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
2021	J	`jnl`	Laplace Redux - Effortless Bayesian Deep Learning. CoRR Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
2021	J	`jnl`	Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR Runa Eschenhagen, Erik A. Daxberger, Philipp Hennig, Agustinus Kristiadi
2020	J	`jnl`	Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2020	A*	`conf`	Mixed-Variable Bayesian Optimization. IJCAI Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause
2020	A*	`conf`	Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. NeurIPS Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
2020	J	`jnl`	Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. CoRR Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
2019	J	`jnl`	Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. CoRR Erik A. Daxberger, José Miguel Hernández-Lobato
2019	J	`jnl`	Embedding models for episodic knowledge graphs. J. Web Semant. Yunpu Ma, Volker Tresp, Erik A. Daxberger
2019	J	`jnl`	Mixed-Variable Bayesian Optimization. CoRR Erik A. Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause
2018	J	`jnl`	Embedding Models for Episodic Memory. CoRR Yunpu Ma, Volker Tresp, Erik A. Daxberger
2017	A*	`conf`	Distributed Batch Gaussian Process Optimization. ICML Erik A. Daxberger, Bryan Kian Hsiang Low