If you're writing a paper, it's important to acknowledge the software engineers who make your scientific contributions possible.
For software and packages, I've found it much harder to find BibTeX citations I can simply copy-paste into my references.bib than it is for papers.
Hopefully this repository will be the first step towards making that easier.
I'm using this page to collect the most official citations I can find for common (machine learning) Python packages. If you spot a mistake or know of another citation that you think should be on this page, please open an issue or PR!
Bit of discussion around this online (see this StackOverflow thread). The following is based on this email by Steven D'Aprano.
@manual{python,
title={{Python: A dynamic, open source programming language}},
author={{Python Core Team}},
organization={{Python Software Foundation}},
year={2019},
url={https://www.python.org/},
}It may also be a good idea to add the specific version of Python, for example for 3.7:
@manual{python37,
title={{Python: A dynamic, open source programming language}},
author={{Python Core Team}},
organization={{Python Software Foundation}},
year={2019},
url={https://www.python.org/},
note={Python version 3.7}
}Packages are listed alphabetically. If this list grows too long, maybe we can add categories.
@article{caffe,
Author={Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal={arXiv preprint arXiv:1408.5093},
Title={Caffe: Convolutional Architecture for Fast Feature Embedding},
Year={2014}
}(source)
@InProceedings{corenlp,
author={Manning, Christopher D. and Surdeanu, Mihai and Bauer, John and Finkel, Jenny and Bethard, Steven J. and McClosky, David},
title={The {Stanford} {CoreNLP} Natural Language Processing Toolkit},
booktitle={Association for Computational Linguistics (ACL) System Demonstrations},
year={2014},
pages={55--60},
url={http://www.aclweb.org/anthology/P/P14/P14-5010}
}(source)
@misc{fastai,
author={
Howard, Jeremy
and Gugger, Sylvain
},
title={fastai: A layered API for deep learning},
journal={Information (Special Issue)}
url={fast.ai},
year={2019}
}(source)
@inproceedings{rehurek_lrec,
title={{Software Framework for Topic Modelling with Large Corpora}},
author={Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka},
booktitle={{Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks}},
pages={45--50},
year=2010,
month=May,
day=22,
publisher={ELRA},
address={Valletta, Malta},
note={\url{http://is.muni.cz/publication/884893/en}},
language={English}
}(source)
@misc{keras,
title={Keras},
author={Chollet, Fran\c{c}ois and others},
year={2015},
howpublished={\url{https://keras.io}},
}(source)
@article{larq,
doi={10.21105/joss.01746},
url={https://doi.org/10.21105/joss.01746},
year={2020},
month=jan,
publisher={The Open Journal},
volume={5},
number={45},
pages={1746},
author={Lukas Geiger and Plumerai Team},
title={Larq: An Open-Source Library for Training Binarized Neural Networks},
journal={Journal of Open Source Software}
}(source)
@article{matplotlib,
title={Matplotlib: A 2D graphics environment},
author={Hunter, John D},
journal={Computing in science \& engineering},
volume={9},
number={3},
pages={90},
year={2007},
publisher={IEEE Computer Society}
}(source)
See also: (DOIs per version of Matplotlib)
@article{mlxtend,
author={Sebastian Raschka},
title={MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack},
journal={The Journal of Open Source Software},
volume={3},
number={24},
month=apr,
year=2018,
publisher={The Open Journal},
doi={10.21105/joss.00638},
url={http://joss.theoj.org/papers/10.21105/joss.00638}
}(source)
@book{nltk,
title={Natural Language Processing with Python},
author={Bird, Steven, Edward Loper and Ewan Klein},
year={2009},
publisher={O'Reilly Media Inc.}
}(source)
@article{numpy,
author = {Harris, Charles R and Millman, K Jarrod and van der Walt, St{\'{e}}fan J and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H and Brett, Matthew and Haldane, Allan and del R{\'{i}}o, Jaime Fern{\'{a}}ndez and Wiebe, Mark and Peterson, Pearu and G{\'{e}}rard-Marchant, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E},
doi = {10.1038/s41586-020-2649-2},
issn = {1476-4687},
journal = {Nature},
number = {7825},
pages = {357--362},
title = {{Array programming with NumPy}},
url = {https://doi.org/10.1038/s41586-020-2649-2},
volume = {585},
year = {2020}
}(source)
@article{opencv,
author={Bradski, G.},
citeulike-article-id={2236121},
journal={Dr. Dobb's Journal of Software Tools},
keywords={bibtex-import},
posted-at={2008-01-15 19:21:54},
priority={4},
title={{The OpenCV Library}},
year={2000}
}(source)
@inproceedings{pandas,
author={Wes McKinney},
title= {Data Structures for Statistical Computing in Python},
booktitle={Proceedings of the 9th Python in Science Conference},
pages={51 - 56},
year={2010},
editor={St\'efan van der Walt and Jarrod Millman}
}(source)
@inproceedings{pytorch,
title={Automatic Differentiation in {PyTorch}},
author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
booktitle={NIPS Autodiff Workshop},
year={2017}
}(source)
@article{scikit-learn,
title={{Scikit-learn: Machine Learning in Python}},
author={
Pedregosa, F. and
Varoquaux, G. and
Gramfort, A. and
Michel, V. and
Thirion, B. and
Grisel, O. and
Blondel, M. and
Prettenhofer, P. and
Weiss, R. and
Dubourg, V. and
Vanderplas, J. and
Passos, A. and
Cournapeau, D. and
Brucher, M. and
Perrot, M. and
Duchesnay, E.
},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}(source)
@article{scipy,
author={
{Virtanen}, Pauli and
{Gommers}, Ralf and
{Oliphant}, Travis E. and
{Haberland}, Matt and
{Reddy}, Tyler and
{Cournapeau}, David and
{Burovski}, Evgeni and
{Peterson}, Pearu and
{Weckesser}, Warren and
{Bright}, Jonathan and
{van der Walt}, St{\'e}fan J. and
{Brett}, Matthew and
{Wilson}, Joshua and
{Jarrod Millman}, K. and
{Mayorov}, Nikolay and
{Nelson}, Andrew R.~J. and
{Jones}, Eric and
{Kern}, Robert and
{Larson}, Eric and
{Carey}, CJ and
{Polat}, {\.I}lhan and
{Feng}, Yu and
{Moore}, Eric W. and
{VanderPlas}, Jake and
{Laxalde}, Denis and
{Perktold}, Josef and
{Cimrman}, Robert and
{Henriksen}, Ian and
{Quintero}, E.~A. and
{Harris}, Charles R and
{Archibald}, Anne M. and
{Ribeiro}, Ant{\^o}nio H. and
{Pedregosa}, Fabian and
{van Mulbregt}, Paul and
{Contributors}, SciPy 1.0
},
title="{SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python}",
journal={Nature Methods},
year="2020",
adsurl={https://rdcu.be/b08Wh},
doi={https://doi.org/10.1038/s41592-019-0686-2},
}(source)
@misc{tensorflow,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
Mart\'{\i}n~Abadi and
Ashish~Agarwal and
Paul~Barham and
Eugene~Brevdo and
Zhifeng~Chen and
Craig~Citro and
Greg~S.~Corrado and
Andy~Davis and
Jeffrey~Dean and
Matthieu~Devin and
Sanjay~Ghemawat and
Ian~Goodfellow and
Andrew~Harp and
Geoffrey~Irving and
Michael~Isard and
Yangqing Jia and
Rafal~Jozefowicz and
Lukasz~Kaiser and
Manjunath~Kudlur and
Josh~Levenberg and
Dandelion~Man\'{e} and
Rajat~Monga and
Sherry~Moore and
Derek~Murray and
Chris~Olah and
Mike~Schuster and
Jonathon~Shlens and
Benoit~Steiner and
Ilya~Sutskever and
Kunal~Talwar and
Paul~Tucker and
Vincent~Vanhoucke and
Vijay~Vasudevan and
Fernanda~Vi\'{e}gas and
Oriol~Vinyals and
Pete~Warden and
Martin~Wattenberg and
Martin~Wicke and
Yuan~Yu and
Xiaoqiang~Zheng
},
year={2015},
}(source)
@article{theano,
author={{Theano Development Team}},
title="{Theano: A {Python} framework for fast computation of mathematical expressions}",
journal={arXiv e-prints},
volume={abs/1605.02688},
primaryClass="cs.SC",
keywords={Computer Science - Symbolic Computation, Computer Science - Learning, Computer Science - Mathematical Software},
year=2016,
month=may,
url={http://arxiv.org/abs/1605.02688},
}(source)
@article{torchbearer,
author={Ethan Harris and Matthew Painter and Jonathon Hare},
title={Torchbearer: A Model Fitting Library for PyTorch},
journal={arXiv preprint arXiv:1809.03363},
year={2018}
}(source)
Many citations come from the SciPy citing guide (which doesn't have BibTeX entries for most citations). Thanks to Lynn (Tristan) Pepin for adding lots of citations to this page!
If this resource was useful to you, please consider checking the following as well:
- Dynamically Typed: My thoughts and links on productized artificial intelligence, machine learning technology, and the tech/startup industry. Delivered to your inbox every second Sunday.
- Machine Learning Resources: Machine learning resources featured in the Quick ML Resource Links section of Dynamically Typed. Updated with new resources every second Sunday!
(Random additional tip: to find the documentation PDF of any LaTeX package, go to texdoc.net/pkg/{package-name}.)