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  How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset

Mackie, I., Dalton, J., & Yates, A. (2021). How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset. In F. Diaz, C. Shah, T. Suel, P. Castells, R. Jones, T. Sakai, et al. (Eds.), SIGIR '21 (pp. 2335-2341). New York, NY: ACM. doi:10.1145/3404835.3463262.

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Genre: Conference Paper
Latex : How Deep is your Learning: the {DL}-{HARD} Annotated Deep Learning Dataset

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 Creators:
Mackie, Iain1, Author
Dalton, Jeffrey1, Author
Yates, Andrew2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Language(s): eng - English
 Dates: 20212021
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Mackie_SIGIR21
DOI: 10.1145/3404835.3463262
 Degree: -

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Title: 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of Event: Virtual Event, Canada
Start-/End Date: 2021-07-11 - 2021-07-15

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Title: SIGIR '21
  Abbreviation : SIGIR 2021
  Subtitle : Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Source Genre: Proceedings
 Creator(s):
Diaz, Fernando1, Editor
Shah, Chirag1, Editor
Suel, Torsten1, Editor
Castells, Pablo1, Editor
Jones, Rosie1, Editor
Sakai, Tetsuya1, Editor
Bellogín, Alejandro1, Editor
Yushioka, Massaharu1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2335 - 2341 Identifier: ISBN: 978-1-4503-8037-9