Fixed: #1754 Add Citation.cff

[Anselmoo]

Add cff

Which issue(s) does this Pull Request fix? fixed: #1754

Original bibtex entry:

@inproceedings{10.1145/3292500.3330648,
author = {Jin, Haifeng and Song, Qingquan and Hu, Xia},
title = {Auto-Keras: An Efficient Neural Architecture Search System},
year = {2019},
isbn = {9781450362016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3292500.3330648},
doi = {10.1145/3292500.3330648},
abstract = {Neural architecture search (NAS) has been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually suffer from expensive computational cost. Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling more efficient training during the search. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. The framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm to efficiently explores the search space. Extensive experiments on real-world benchmark datasets have been done to demonstrate the superior performance of the developed framework over the state-of-the-art methods. Moreover, we build an open-source AutoML system based on our method, namely Auto-Keras. The code and documentation are available at https://autokeras.com. The system runs in parallel on CPU and GPU, with an adaptive search strategy for different GPU memory limits.},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {1946–1956},
numpages = {11},
keywords = {neural architecture search, automated machine learning, bayesian optimization, network morphis, automl},
location = {Anchorage, AK, USA},
series = {KDD '19}
}

CITATION.cff generated bibtex entry:

@inproceedings{Jin_Auto-Keras_An_Efficient_2019,
author = {Jin, Haifeng and Song, Qingquan and Xu, Xia},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
doi = {10.1145/3292500.3330648},
month = {8},
pages = {1946--1956},
publisher = {Association for Computing Machinery},
series = {KDD '19},
title = {{Auto-Keras: An Efficient Neural Architecture Search System}},
url = {https://doi.org/10.1145/3292500.3330648},
year = {2019}
}

Will add this feature to the repo:

[codecov[bot]]

Codecov Report

Merging #1755 (9acea69) into master (af9168f) will not change coverage. The diff coverage is n/a.

@@            Coverage Diff            @@
##            master     #1755   +/-   ##
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  Coverage   100.00%   100.00%           
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  Files           51        51           
  Lines         3411      3411           
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  Hits          3411      3411           

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[Anselmoo]

Thanks for the PR! However, we prefer to keep the current simple bibtex for now until the cff files are widely adopted in popular repos.

It might be an idea to add the DOI https://doi.org/10.1145/3292500.3330648 because it will help reference managers like Endnote or Zotero keep tracking the reference-updates.