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  Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., et al. (2019). Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. In 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019) (pp. 460-477). Red Hook, NY: Curran Associates, Inc. Retrieved from http://auai.org/uai2019/accepted.php.

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 Creators:
Peharz, R.1, Author
Vergari, A.2, Author           
Stelzner, K.1, Author
Molina, A.1, Author
Shao, X.1, Author
Trapp, M.1, Author
Kersting, K.1, Author
Ghahramani, Z.1, 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: 20192019-10
 Publication Status: Issued
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

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Title: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019)
Place of Event: Tel Aviv
Start-/End Date: 2019-07-22 - 2019-07-25

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Title: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Red Hook, NY : Curran Associates, Inc.
Pages: - Volume / Issue: 1 Sequence Number: 124 Start / End Page: 460 - 477 Identifier: URI: http://auai.org/uai2019/accepted.php
ISBN: 978-1-5108-9156-2

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Title: Proceedings of the 35th Uncertainty in Artificial Intelligence Conference
Source Genre: Proceedings
 Creator(s):
Adams, Ryan P.1, Editor
Gogate, Vibhav1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: PMLR
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 334 - 344 Identifier: URI: https://proceedings.mlr.press/v115/

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Title: Proceedings of Machine Learning Research (PMLR)
Source Genre: Series
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Publ. Info: PMLR
Pages: - Volume / Issue: 115 Sequence Number: - Start / End Page: - Identifier: ISSN: 2640-3498