Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Immer, Alexander; Bauer, Matthias; Fortuin, Vincent; Rätsch, Gunnar; Khan, Mohammad Emtiyaz

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

MPS-Authors

/persons/resource/persons220009

Bauer, Matthias
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
External Organizations;

External Resource

https://proceedings.mlr.press/v139/immer21a.html
(Publisher version)

https://doi.org/10.48550/arXiv.2104.04975
(Preprint)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Immer, A., Bauer, M., Fortuin, V., Rätsch, G., & Khan, M. E. (2021). Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. In M. Meila, & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning (ICML 2021) (pp. 4563-4573). PMLR.

Cite as: https://hdl.handle.net/21.11116/0000-0010-2E7E-F

Abstract

There is no abstract available