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  DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning

Harder, F., Köhler, J., Welling, M., & Park, M. (2018). DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning. In Workshop on Privacy Preserving Machine Learning. Retrieved from https://ppml-workshop.github.io/ppml/ppml18/index.html#papers.

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Genre: Conference Paper

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 Creators:
Harder, Frederik1, Author           
Köhler, Jonas1, Author           
Welling, Max2, Author
Park, Mijung1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2External Organizations, ou_persistent22              

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Free keywords: Abt. Schölkopf
 Abstract: -

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Language(s): eng - English
 Dates: 2018
 Publication Status: Published online
 Pages: 5
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: HarderKoehlerWellingPark18
URI: https://ppml-workshop.github.io/ppml/ppml18/index.html#papers
 Degree: -

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Title: Privacy Preserving Machine Learning (PPML 2018)
Place of Event: Montreal
Start-/End Date: 2018-12-08 - 2018-12-08

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Title: Workshop on Privacy Preserving Machine Learning
  Other : Privacy Preserving Machine Learning: NeurIPS 2018 Workshop
Source Genre: Proceedings
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: URI: https://ppml-workshop.github.io/ppml/ppml18/index.html#papers