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  Adapting the Linearised Laplace Model Evidence for Modern Deep Learning

Antoran, J., Janz, D., Allingham, J. U., Daxberger, E., Barbano, R., Nalisnick, E., et al. (2022). Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. In K. Chaudhuri (Ed.), Proceedings of the 39th International Conference on Machine Learning (ICML 2022) (pp. 796-821). PMLR. Retrieved from https://proceedings.mlr.press/v162/antoran22a.html.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0010-2F7A-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0010-2F7B-1
Genre: Conference Paper

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Locator:
https://proceedings.mlr.press/v162/antoran22a.html (Publisher version)
Description:
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OA-Status:
Miscellaneous
Locator:
https://doi.org/10.48550/arXiv.2205.02293 (Preprint)
Description:
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OA-Status:
Green

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 Creators:
Antoran, Javier 1, Author
Janz, David 1, Author
Allingham, James Urquhart1, Author
Daxberger, Erik1, 2, Author           
Barbano, Riccardo 1, Author
Nalisnick, Eric 1, Author
Hernandez-Lobato, José Miguel1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Free keywords: Abt. Schölkopf
 Abstract: -

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Language(s): eng - English
 Dates: 2022-07
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Antoranetal22
URI: https://proceedings.mlr.press/v162/antoran22a.html
arXiv: 2205.02293
 Degree: -

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Title: 39th International Conference on Machine Learning (ICML 2022)
Place of Event: Baltimore, MD
Start-/End Date: 2022-07-17 - 2022-07-23

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Source 1

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Title: Proceedings of the 39th International Conference on Machine Learning (ICML 2022)
Source Genre: Proceedings
 Creator(s):
Chaudhuri, Kamalika1, Editor
Jegelka, Stefanie1, Author
Song, Le1, Author
Szepesvari, Csaba1, Author
Niu, Gang1, Author
Sabato, Sivan1, Author
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: PMLR
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 796 - 821 Identifier: URI: https://proceedings.mlr.press/v162

Source 2

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Title: Proceedings of Machine Learning Research
  Abbreviation : PMLR
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: PMLR
Pages: - Volume / Issue: 162 Sequence Number: - Start / End Page: - Identifier: URI: https://proceedings.mlr.press
ISSN: 2640-3498