Radial and Directional Posteriors for Bayesian Deep Learning

Oh, Changyong; Adamczewski, Kamil; Park, Mijung

doi:10.1609/aaai.v34i04.5976

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Radial and Directional Posteriors for Bayesian Deep Learning

MPS-Authors

/persons/resource/persons286492

Oh, Changyong
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons286495

Adamczewski, Kamil
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
External Organizations;

/persons/resource/persons214725

Park, Mijung
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
External Organizations;

External Resource

https://doi.org/10.1609/aaai.v34i04.5976
(Publisher version)

https://doi.org/10.48550/arXiv.1902.02603
(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

Oh, C., Adamczewski, K., & Park, M. (2020). Radial and Directional Posteriors for Bayesian Deep Learning. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, the Thirty-Second Conference on Innovative Applications of Artificial Intelligence, the Tenth Symposium on Educational Advances in Artificial Intelligence (pp. 5298-5305). Palo Alto, CA: AAAI Press. doi:10.1609/aaai.v34i04.5976.

Cite as: https://hdl.handle.net/21.11116/0000-000C-8F03-0

Abstract

There is no abstract available