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  General Latent Feature Models for Heterogeneous Datasets

Valera, I., Pradier, M. F., Lomeli, M., & Ghahramani, Z. (2020). General Latent Feature Models for Heterogeneous Datasets. Journal of Machine Learning Research, 21: 100. Retrieved from http://jmlr.org/papers/v21/17-328.html.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-DE04-6 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-DE0A-0
Genre: Journal Article

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Locator:
http://jmlr.org/papers/v21/17-328.html (Publisher version)
Description:
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OA-Status:
Gold
Locator:
https://dl.acm.org/doi/10.5555/3455716.3455816 (Publisher version)
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OA-Status:
Gold

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 Creators:
Valera, Isabel1, 2, Author                 
Pradier, Melanie F.1, Author
Lomeli, Maria1, Author
Ghahramani, Zoubin1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Max Planck Research Group Probabilistic Learning, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_3320952              

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Free keywords: Forschungsgruppe Valera
 Abstract: -

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Language(s): eng - English
 Dates: 2020-04
 Publication Status: Published online
 Pages: 49
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: ValPraLomGha20
URI: http://jmlr.org/papers/v21/17-328.html
URI: https://dl.acm.org/doi/10.5555/3455716.3455816
 Degree: -

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Title: Journal of Machine Learning Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Brookline, MA : Microtome Publishing
Pages: - Volume / Issue: 21 Sequence Number: 100 Start / End Page: - Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020