Following is a list of papers that I frequently cite or found interesting to read during the course of my PhD.
SE4AI
Foundational papers that introduce the notion of bringing Software Engineering best practices to AI enabled systems.
vogelsang2019requirements
@InProceedings{ vogelsang2019requirements,
doi = {10.1109/rew.2019.00050},
url = {https://doi.org/10.1109/rew.2019.00050},
year = {2019},
month = sep,
publisher = {{IEEE}},
author = {Andreas Vogelsang and Markus Borg},
title = {Requirements Engineering for Machine Learning:
Perspectives from Data Scientists},
booktitle = {2019 {IEEE} 27th International Requirements Engineering
Conference Workshops ({REW})}
}breck2017ml
@InProceedings{ breck2017ml,
title = {The ML test score: A rubric for ML production readiness
and technical debt reduction},
url = {http://dx.doi.org/10.1109/BigData.2017.8258038},
doi = {10.1109/bigdata.2017.8258038},
booktitle = {2017 IEEE International Conference on Big Data (Big
Data)},
publisher = {IEEE},
author = {Breck, Eric and Cai, Shanqing and Nielsen, Eric and Salib,
Michael and Sculley, D.},
year = {2017},
month = dec
}sambasivan2021everyone
@InProceedings{ sambasivan2021everyone,
series = {CHI ’21},
title = {“Everyone wants to do the model work, not the data
work”: Data Cascades in High-Stakes AI},
url = {http://dx.doi.org/10.1145/3411764.3445518},
doi = {10.1145/3411764.3445518},
booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in
Computing Systems},
publisher = {ACM},
author = {Sambasivan, Nithya and Kapania, Shivani and Highfill,
Hannah and Akrong, Diana and Paritosh, Praveen and Aroyo,
Lora M},
year = {2021},
month = may,
collection = {CHI ’21}
}martinez-plumed2021crisp-dm
@Article{ martinez-plumed2021crisp-dm,
title = {CRISP-DM Twenty Years Later: From Data Mining Processes to
Data Science Trajectories},
volume = {33},
issn = {2326-3865},
url = {http://dx.doi.org/10.1109/TKDE.2019.2962680},
doi = {10.1109/tkde.2019.2962680},
number = {8},
journal = {IEEE Transactions on Knowledge and Data Engineering},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Martinez-Plumed, Fernando and Contreras-Ochando, Lidia and
Ferri, Cesar and Hernandez-Orallo, Jose and Kull, Meelis
and Lachiche, Nicolas and Ramirez-Quintana, Maria Jose and
Flach, Peter},
year = {2021},
month = aug,
pages = {3048–3061}
}hutchinson2021towards
@InProceedings{ hutchinson2021towards,
series = {FAccT ’21},
title = {Towards Accountability for Machine Learning Datasets:
Practices from Software Engineering and Infrastructure},
url = {http://dx.doi.org/10.1145/3442188.3445918},
doi = {10.1145/3442188.3445918},
booktitle = {Proceedings of the 2021 ACM Conference on Fairness,
Accountability, and Transparency},
publisher = {ACM},
author = {Hutchinson, Ben and Smart, Andrew and Hanna, Alex and
Denton, Emily and Greer, Christina and Kjartansson, Oddur
and Barnes, Parker and Mitchell, Margaret},
year = {2021},
month = mar,
collection = {FAccT ’21}
}haakman2021ai
@Article{ haakman2021ai,
title = {AI lifecycle models need to be revised: An exploratory
study in Fintech},
volume = {26},
issn = {1573-7616},
url = {http://dx.doi.org/10.1007/s10664-021-09993-1},
doi = {10.1007/s10664-021-09993-1},
number = {5},
journal = {Empirical Software Engineering},
publisher = {Springer Science and Business Media LLC},
author = {Haakman, Mark and Cruz, Luís and Huijgens, Hennie and van
Deursen, Arie},
year = {2021},
month = jul
}bosch2021engineering
@InBook{ bosch2021engineering,
title = {Engineering AI Systems: A Research Agenda},
issn = {2327-3461},
url = {http://dx.doi.org/10.4018/978-1-7998-5101-1.ch001},
doi = {10.4018/978-1-7998-5101-1.ch001},
booktitle = {Artificial Intelligence Paradigms for Smart Cyber-Physical
Systems},
publisher = {IGI Global},
author = {Bosch, Jan and Olsson, Helena Holmström and Crnkovic,
Ivica},
year = {2021},
pages = {1–19}
}arpteg2018software
@InProceedings{ arpteg2018software,
title = {Software Engineering Challenges of Deep Learning},
url = {http://dx.doi.org/10.1109/SEAA.2018.00018},
doi = {10.1109/seaa.2018.00018},
booktitle = {2018 44th Euromicro Conference on Software Engineering and
Advanced Applications (SEAA)},
publisher = {IEEE},
author = {Arpteg, Anders and Brinne, Bjorn and Crnkovic-Friis, Luka
and Bosch, Jan},
year = {2018},
month = aug
}amershi2019software
@InProceedings{ amershi2019software,
doi = {10.1109/icse-seip.2019.00042},
url = {https://doi.org/10.1109/icse-seip.2019.00042},
year = 2019,
month = may,
publisher = {{IEEE}},
author = {Saleema Amershi and Andrew Begel and Christian Bird and
Robert DeLine and Harald Gall and Ece Kamar and Nachiappan
Nagappan and Besmira Nushi and Thomas Zimmermann},
title = {Software Engineering for Machine Learning: A Case Study},
booktitle = {2019 {IEEE}/{ACM} 41st International Conference on
Software Engineering: Software Engineering in Practice
({ICSE}-{SEIP})}
}LLMs
Papers on Large Language Models and Prompt Engineering.
liu2023pre-train
@Article{ liu2023pre-train,
doi = {10.1145/3560815},
url = {https://doi.org/10.1145/3560815},
year = {2023},
month = jan,
publisher = {Association for Computing Machinery ({ACM})},
volume = {55},
number = {9},
pages = {1--35},
author = {Pengfei Liu and Weizhe Yuan and Jinlan Fu and Zhengbao
Jiang and Hiroaki Hayashi and Graham Neubig},
title = {Pre-train, Prompt, and Predict: A Systematic Survey of
Prompting Methods in Natural Language Processing},
journal = {{ACM} Computing Surveys}
}Mining Software Repositories
Papers on large-scale repository mining.
quaranta2021kgtorrent
@InProceedings{ quaranta2021kgtorrent,
title = {KGTorrent: A Dataset of Python Jupyter Notebooks from
Kaggle},
url = {http://dx.doi.org/10.1109/MSR52588.2021.00072},
doi = {10.1109/msr52588.2021.00072},
booktitle = {2021 IEEE/ACM 18th International Conference on Mining
Software Repositories (MSR)},
publisher = {IEEE},
author = {Quaranta, Luigi and Calefato, Fabio and Lanubile,
Filippo},
year = {2021},
month = may
}psallidas2019data
@Misc{ psallidas2019data,
title = {Data Science through the looking glass and what we found
there},
author = {Fotis Psallidas and Yiwen Zhu and Bojan Karlas and Matteo
Interlandi and Avrilia Floratou and Konstantinos Karanasos
and Wentao Wu and Ce Zhang and Subru Krishnan and Carlo
Curino and Markus Weimer},
year = {2019},
eprint = {1912.09536},
archiveprefix = {arXiv},
primaryclass = {cs.LG}
}pimentel2019large-scale
@InProceedings{ pimentel2019large-scale,
title = {A Large-Scale Study About Quality and Reproducibility of
Jupyter Notebooks},
url = {http://dx.doi.org/10.1109/MSR.2019.00077},
doi = {10.1109/msr.2019.00077},
booktitle = {2019 IEEE/ACM 16th International Conference on Mining
Software Repositories (MSR)},
publisher = {IEEE},
author = {Pimentel, Joao Felipe and Murta, Leonardo and Braganholo,
Vanessa and Freire, Juliana},
year = {2019},
month = may
}bavishi2021vizsmith
@InProceedings{ bavishi2021vizsmith,
title = {VizSmith: Automated Visualization Synthesis by Mining
Data-Science Notebooks},
url = {http://dx.doi.org/10.1109/ASE51524.2021.9678696},
doi = {10.1109/ase51524.2021.9678696},
booktitle = {2021 36th IEEE/ACM International Conference on Automated
Software Engineering (ASE)},
publisher = {IEEE},
author = {Bavishi, Rohan and Laddad, Shadaj and Yoshida, Hiroaki and
Prasad, Mukul R. and Sen, Koushik},
year = {2021},
month = nov
}Computational Notebooks
Papers on the design and challenges of computational notebooks.
yang2021subtle
@InProceedings{ yang2021subtle,
title = {Subtle Bugs Everywhere: Generating Documentation for Data
Wrangling Code},
url = {http://dx.doi.org/10.1109/ASE51524.2021.9678520},
doi = {10.1109/ase51524.2021.9678520},
booktitle = {2021 36th IEEE/ACM International Conference on Automated
Software Engineering (ASE)},
publisher = {IEEE},
author = {Yang, Chenyang and Zhou, Shurui and Guo, Jin L.C. and
Kastner, Christian},
year = {2021},
month = nov
}wang2020assessing
@InProceedings{ wang2020assessing,
series = {ASE ’20},
title = {Assessing and restoring reproducibility of Jupyter
notebooks},
url = {http://dx.doi.org/10.1145/3324884.3416585},
doi = {10.1145/3324884.3416585},
booktitle = {Proceedings of the 35th IEEE/ACM International Conference
on Automated Software Engineering},
publisher = {ACM},
author = {Wang, Jiawei and Kuo, Tzu-yang and Li, Li and Zeller,
Andreas},
year = {2020},
month = dec,
collection = {ASE ’20}
}rule2018exploration
@InProceedings{ rule2018exploration,
series = {CHI ’18},
title = {Exploration and Explanation in Computational Notebooks},
url = {http://dx.doi.org/10.1145/3173574.3173606},
doi = {10.1145/3173574.3173606},
booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems},
publisher = {ACM},
author = {Rule, Adam and Tabard, Aurélien and Hollan, James D.},
year = {2018},
month = apr,
collection = {CHI ’18}
}kery2018story
@InProceedings{ kery2018story,
series = {CHI ’18},
title = {The Story in the Notebook: Exploratory Data Science using
a Literate Programming Tool},
url = {http://dx.doi.org/10.1145/3173574.3173748},
doi = {10.1145/3173574.3173748},
booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems},
publisher = {ACM},
author = {Kery, Mary Beth and Radensky, Marissa and Arya, Mahima and
John, Bonnie E. and Myers, Brad A.},
year = {2018},
month = apr,
collection = {CHI ’18}
}head2019managing
@InProceedings{ head2019managing,
series = {CHI ’19},
title = {Managing Messes in Computational Notebooks},
url = {http://dx.doi.org/10.1145/3290605.3300500},
doi = {10.1145/3290605.3300500},
booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems},
publisher = {ACM},
author = {Head, Andrew and Hohman, Fred and Barik, Titus and
Drucker, Steven M. and DeLine, Robert},
year = {2019},
month = may,
collection = {CHI ’19}
}chattopadhyay2020what
@InProceedings{ chattopadhyay2020what’s,
series = {CHI ’20},
title = {What’s Wrong with Computational Notebooks? Pain Points,
Needs, and Design Opportunities},
url = {http://dx.doi.org/10.1145/3313831.3376729},
doi = {10.1145/3313831.3376729},
booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in
Computing Systems},
publisher = {ACM},
author = {Chattopadhyay, Souti and Prasad, Ishita and Henley, Austin
Z. and Sarma, Anita and Barik, Titus},
year = {2020},
month = apr,
collection = {CHI ’20}
}Visual Analytics
Papers on visual analytics tools for ML.
zeiler2014visualizing
This is a very nice paper, which I believe kicked-off the trend of visual analytics in Deep Learning? I have seen the visualisations shown in the paper before (probably during the DL course I took during Msc).
The visualisation techniques shown inspect the feature maps inside the model. I think this helps be align my work to the visualisations used before and after the model is trained. This is also in-line with our narrative of making decisions at a more holistic level, looking at the entire ML pipeline.
@InBook{ zeiler2014visualizing,
title = {Visualizing and Understanding Convolutional Networks},
isbn = {9783319105901},
issn = {1611-3349},
url = {http://dx.doi.org/10.1007/978-3-319-10590-1_53},
doi = {10.1007/978-3-319-10590-1_53},
booktitle = {Lecture Notes in Computer Science},
publisher = {Springer International Publishing},
author = {Zeiler, Matthew D. and Fergus, Rob},
year = {2014},
pages = {818–833}
}wexler2019what-if
@Article{ wexler2019what-if,
title = {The What-If Tool: Interactive Probing of Machine Learning
Models},
issn = {2160-9306},
url = {http://dx.doi.org/10.1109/TVCG.2019.2934619},
doi = {10.1109/tvcg.2019.2934619},
journal = {IEEE Transactions on Visualization and Computer Graphics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Wexler, James and Pushkarna, Mahima and Bolukbasi, Tolga
and Wattenberg, Martin and Viegas, Fernanda and Wilson,
Jimbo},
year = {2019},
pages = {1–1}
}kandel2012enterprise
@Article{ kandel2012enterprise,
title = {Enterprise Data Analysis and Visualization: An Interview
Study},
volume = {18},
issn = {1077-2626},
url = {http://dx.doi.org/10.1109/TVCG.2012.219},
doi = {10.1109/tvcg.2012.219},
number = {12},
journal = {IEEE Transactions on Visualization and Computer Graphics},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Kandel, Sean and Paepcke, Andreas and Hellerstein, Joseph
M. and Heer, Jeffrey},
year = {2012},
month = dec,
pages = {2917–2926}
}hohman2019visual
@Article{ hohman2019visual,
doi = {10.1109/tvcg.2018.2843369},
url = {https://doi.org/10.1109/tvcg.2018.2843369},
year = {2019},
month = aug,
publisher = {Institute of Electrical and Electronics Engineers
({IEEE})},
volume = {25},
number = {8},
pages = {2674--2693},
author = {Fred Hohman and Minsuk Kahng and Robert Pienta and Duen
Horng Chau },
title = {Visual Analytics in Deep Learning: An Interrogative Survey
for the Next Frontiers},
journal = {{IEEE} Transactions on Visualization and Computer
Graphics}
}amershi2015modeltracker
@InProceedings{ amershi2015modeltracker,
series = {CHI ’15},
title = {ModelTracker: Redesigning Performance Analysis Tools for
Machine Learning},
url = {http://dx.doi.org/10.1145/2702123.2702509},
doi = {10.1145/2702123.2702509},
booktitle = {Proceedings of the 33rd Annual ACM Conference on Human
Factors in Computing Systems},
publisher = {ACM},
author = {Amershi, Saleema and Chickering, Max and Drucker, Steven
M. and Lee, Bongshin and Simard, Patrice and Suh, Jina},
year = {2015},
month = apr,
collection = {CHI ’15}
}Testing
Papers that touch upon testing AI systems. This is a vast topic of research, but the major themes include where to test, what to test and how to test AI systems.
riccio2020testing
@Article{ riccio2020testing,
title = {Testing machine learning based systems: a systematic
mapping},
volume = {25},
issn = {1573-7616},
url = {http://dx.doi.org/10.1007/s10664-020-09881-0},
doi = {10.1007/s10664-020-09881-0},
number = {6},
journal = {Empirical Software Engineering},
publisher = {Springer Science and Business Media LLC},
author = {Riccio, Vincenzo and Jahangirova, Gunel and Stocco, Andrea
and Humbatova, Nargiz and Weiss, Michael and Tonella,
Paolo},
year = {2020},
month = sep,
pages = {5193–5254}
}xiao2021self-checking
@InProceedings{ xiao2021self-checking,
title = {Self-Checking Deep Neural Networks in Deployment},
url = {http://dx.doi.org/10.1109/ICSE43902.2021.00044},
doi = {10.1109/icse43902.2021.00044},
booktitle = {2021 IEEE/ACM 43rd International Conference on Software
Engineering (ICSE)},
publisher = {IEEE},
author = {Xiao, Yan and Beschastnikh, Ivan and Rosenblum, David S.
and Sun, Changsheng and Elbaum, Sebastian and Lin, Yun and
Dong, Jin Song},
year = {2021},
month = may
}pei2017deepxplore
@InProceedings{ pei2017deepxplore,
series = {SOSP ’17},
title = {DeepXplore: Automated Whitebox Testing of Deep Learning
Systems},
url = {http://dx.doi.org/10.1145/3132747.3132785},
doi = {10.1145/3132747.3132785},
booktitle = {Proceedings of the 26th Symposium on Operating Systems
Principles},
publisher = {ACM},
author = {Pei, Kexin and Cao, Yinzhi and Yang, Junfeng and Jana,
Suman},
year = {2017},
month = oct,
collection = {SOSP ’17}
}zhang2021ignorance
@InProceedings{ zhang2021ignorance,
title = {“Ignorance and Prejudice” in Software Fairness},
url = {http://dx.doi.org/10.1109/ICSE43902.2021.00129},
doi = {10.1109/icse43902.2021.00129},
booktitle = {2021 IEEE/ACM 43rd International Conference on Software
Engineering (ICSE)},
publisher = {IEEE},
author = {Zhang, Jie M. and Harman, Mark},
year = {2021},
month = may
}mehrabi2021survey
@Article{ mehrabi2021survey,
title = {A Survey on Bias and Fairness in Machine Learning},
volume = {54},
issn = {1557-7341},
url = {http://dx.doi.org/10.1145/3457607},
doi = {10.1145/3457607},
number = {6},
journal = {ACM Computing Surveys},
publisher = {Association for Computing Machinery (ACM)},
author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta
and Lerman, Kristina and Galstyan, Aram},
year = {2021},
month = jul,
pages = {1–35}
}chen2023fairness
@Misc{ chen2023fairness,
title = {Fairness Testing: A Comprehensive Survey and Analysis of
Trends},
author = {Zhenpeng Chen and Jie M. Zhang and Max Hort and Federica
Sarro and Mark Harman},
year = {2023},
eprint = {2207.10223},
archiveprefix = {arXiv},
primaryclass = {cs.SE}
}biswas2021fair
@InProceedings{ biswas2021fair,
series = {ESEC/FSE ’21},
title = {Fair preprocessing: towards understanding compositional
fairness of data transformers in machine learning
pipeline},
url = {http://dx.doi.org/10.1145/3468264.3468536},
doi = {10.1145/3468264.3468536},
booktitle = {Proceedings of the 29th ACM Joint Meeting on European
Software Engineering Conference and Symposium on the
Foundations of Software Engineering},
publisher = {ACM},
author = {Biswas, Sumon and Rajan, Hridesh},
year = {2021},
month = aug,
collection = {ESEC/FSE ’21}
}biswas2020do
@InProceedings{ biswas2020do,
series = {ESEC/FSE ’20},
title = {Do the machine learning models on a crowd sourced platform
exhibit bias? an empirical study on model fairness},
url = {http://dx.doi.org/10.1145/3368089.3409704},
doi = {10.1145/3368089.3409704},
booktitle = {Proceedings of the 28th ACM Joint Meeting on European
Software Engineering Conference and Symposium on the
Foundations of Software Engineering},
publisher = {ACM},
author = {Biswas, Sumon and Rajan, Hridesh},
year = {2020},
month = nov,
collection = {ESEC/FSE ’20}
}zhang2022machine
@Article{ zhang2022machine,
title = {Machine Learning Testing: Survey, Landscapes and
Horizons},
volume = {48},
issn = {2326-3881},
url = {http://dx.doi.org/10.1109/TSE.2019.2962027},
doi = {10.1109/tse.2019.2962027},
number = {1},
journal = {IEEE Transactions on Software Engineering},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
author = {Zhang, Jie M. and Harman, Mark and Ma, Lei and Liu, Yang},
year = {2022},
month = jan,
pages = {1–36}
}schelter2018automating
@Article{ schelter2018automating,
title = {Automating large-scale data quality verification},
volume = {11},
issn = {2150-8097},
url = {http://dx.doi.org/10.14778/3229863.3229867},
doi = {10.14778/3229863.3229867},
number = {12},
journal = {Proceedings of the VLDB Endowment},
publisher = {Association for Computing Machinery (ACM)},
author = {Schelter, Sebastian and Lange, Dustin and Schmidt, Philipp
and Celikel, Meltem and Biessmann, Felix and Grafberger,
Andreas},
year = {2018},
month = aug,
pages = {1781–1794}
}lwakatare2021on
@InProceedings{ lwakatare2021on,
title = {On the Experiences of Adopting Automated Data Validation
in an Industrial Machine Learning Project},
url = {http://dx.doi.org/10.1109/ICSE-SEIP52600.2021.00034},
doi = {10.1109/icse-seip52600.2021.00034},
booktitle = {2021 IEEE/ACM 43rd International Conference on Software
Engineering: Software Engineering in Practice (ICSE-SEIP)},
publisher = {IEEE},
author = {Lwakatare, Lucy Ellen and Rånge, Ellinor and Crnkovic,
Ivica and Bosch, Jan},
year = {2021},
month = may
}biessmann2021automated
@Article{ biessmann2021automated,
author = {Felix Biessmann and Jacek Golebiowski and Tammo Rukat and
Dustin Lange and Philipp Schmidt},
title = {Automated data validation in machine learning systems},
year = {2021},
url = {https://www.amazon.science/publications/automated-data-validation-in-machine-learning-systems},
journal = {IEEE Data Engineering Bulletin}
}Meta
Following are books and papers that I found helpful when…
shaw2003writing
@InProceedings{ shaw2003writing,
title = {Writing good software engineering research papers},
url = {http://dx.doi.org/10.1109/ICSE.2003.1201262},
doi = {10.1109/icse.2003.1201262},
booktitle = {25th International Conference on Software Engineering,
2003. Proceedings.},
publisher = {IEEE},
author = {Shaw, M.},
year = {2003}
}shaw2002what
@Article{ shaw2002what,
title = {What makes good research in Software Engineering?},
volume = {4},
issn = {1433-2779},
url = {http://dx.doi.org/10.1007/s10009-002-0083-4},
doi = {10.1007/s10009-002-0083-4},
number = {1},
journal = {International Journal on Software Tools for Technology
Transfer},
publisher = {Springer Science and Business Media LLC},
author = {Shaw, Mary},
year = {2002},
month = oct,
pages = {1–7}
}wohlin2012experimentation
@Book{ wohlin2012experimentation,
title = {Experimentation in Software Engineering},
isbn = {9783642290442},
url = {http://dx.doi.org/10.1007/978-3-642-29044-2},
doi = {10.1007/978-3-642-29044-2},
publisher = {Springer Berlin Heidelberg},
author = {Wohlin, Claes and Runeson, Per and Höst, Martin and
Ohlsson, Magnus C. and Regnell, Björn and Wesslén,
Anders},
year = {2012}
}rugg2004unwritten
@Book{ rugg2004unwritten,
title = {The Unwritten rules of phd research},
url = {https://postgrado.bio.uc.cl/wp-content/uploads/2014/11/Unwritten-Rules-of-PhD-Research.pdf},
publisher = {Open University Press},
author = {Rugg, Gordon and Petre, Marian},
year = {2004}
}ML Software
ML software libraries that I often use and cite. Mostly Python.
baylor2017tfx
@InProceedings{ baylor2017tfx,
doi = {10.1145/3097983.3098021},
url = {https://doi.org/10.1145/3097983.3098021},
year = {2017},
month = aug,
publisher = {{ACM}},
author = {Denis Baylor and Eric Breck and Heng-Tze Cheng and Noah
Fiedel and Chuan Yu Foo and Zakaria Haque and Salem Haykal
and Mustafa Ispir and Vihan Jain and Levent Koc and Chiu
Yuen Koo and Lukasz Lew and Clemens Mewald and Akshay
Naresh Modi and Neoklis Polyzotis and Sukriti Ramesh and
Sudip Roy and Steven Euijong Whang and Martin Wicke and
Jarek Wilkiewicz and Xin Zhang and Martin Zinkevich},
title = {{TFX}},
booktitle = {Proceedings of the 23rd {ACM} {SIGKDD} International
Conference on Knowledge Discovery and Data Mining}
}virtanen2020scipy
@Article{ virtanen2020scipy,
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 Millman, K.
Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and
Jones, Eric and Kern, Robert and Larson, Eric and Carey, C
J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
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