Bibliography#

[CAB+19]

The Turing Way Community, Becky Arnold, Louise Bowler, Sarah Gibson, Patricia Herterich, Rosie Higman, Anna Krystalli, Alexander Morley, Martin O'Reilly, and Kirstie Whitaker. The turing way: a handbook for reproducible data science. 2019. URL: https://zenodo.org/record/3233986, doi:10.5281/ZENODO.3233986.

[DDS+09]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, June 2009. URL: https://doi.org/10.1109/cvpr.2009.5206848, doi:10.1109/cvpr.2009.5206848.

[Dra21]

Jesper Sören Dramsch. Make Machine Learning work in the Real World. Self-published, 2021. URL: https://dramsch.net/projects/book/make-ml-work-in-the-real-world/.

[DSC+22]

Peter D. Dueben, Martin G. Schultz, Matthew Chantry, David John Gagne, David Matthew Hall, and Amy McGovern. Challenges and benchmark datasets for machine learning in the atmospheric sciences: definition, status, and outlook. Artificial Intelligence for the Earth Systems, July 2022. URL: https://doi.org/10.1175/aies-d-21-0002.1, doi:10.1175/aies-d-21-0002.1.

[HMH+18]

Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. Rainbow: combining improvements in deep reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, April 2018. URL: https://doi.org/10.1609/aaai.v32i1.11796, doi:10.1609/aaai.v32i1.11796.

[HHG20]

Allison Marie Horst, Alison Presmanes Hill, and Kristen B Gorman. palmerpenguins: Palmer Archipelago (Antarctica) penguin data. 2020. R package version 0.1.0. URL: https://allisonhorst.github.io/palmerpenguins/, doi:10.5281/zenodo.3960218.

[KLVC21]

Janis Klaise, Arnaud¬†Van Looveren, Giovanni Vacanti, and Alexandru Coca. Alibi explain: algorithms for explaining machine learning models. Journal of Machine Learning Research, 22(181):1‚Äď7, 2021. URL: http://jmlr.org/papers/v22/21-0017.html.

[KMM+20]

Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. Captum: a unified and generic model interpretability library for pytorch. 2020. arXiv:2009.07896.

[Lon21]

Michael A. Lones. How to avoid machine learning pitfalls: a guide for academic researchers. 2021. URL: https://arxiv.org/abs/2108.02497, doi:10.48550/ARXIV.2108.02497.

[LL17]

Scott Lundberg and Su-In Lee. A unified approach to interpreting model predictions. 2017. URL: https://arxiv.org/abs/1705.07874, doi:10.48550/ARXIV.1705.07874.

[MLdPM19]

Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau, and Tobias Meisen. Ablation studies in artificial neural networks. 2019. URL: https://arxiv.org/abs/1901.08644, doi:10.48550/ARXIV.1901.08644.

[Moh22]

Goku Mohandas. Home - made with ml. https://madewithml.com/, 2022.

[Mol22]

Christoph Molnar. Interpretable Machine Learning. Leanpub, 2 edition, 2022. URL: https://christophm.github.io/interpretable-ml-book.

[PVG+11]

F.¬†Pedregosa, G.¬†Varoquaux, A.¬†Gramfort, V.¬†Michel, B.¬†Thirion, O.¬†Grisel, M.¬†Blondel, P.¬†Prettenhofer, R.¬†Weiss, V.¬†Dubourg, J.¬†Vanderplas, A.¬†Passos, D.¬†Cournapeau, M.¬†Brucher, M.¬†Perrot, and E.¬†Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825‚Äď2830, 2011.

[PVLS+20]

Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily Fox, and Hugo Larochelle. Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program). 2020. URL: https://arxiv.org/abs/2003.12206, doi:10.48550/ARXIV.2003.12206.

[Ras18]

Sebastian Raschka. Model evaluation, model selection, and algorithm selection in machine learning. 2018. URL: https://arxiv.org/abs/1811.12808, doi:10.48550/ARXIV.1811.12808.

[RDS+20]

Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, and Nils Thuerey. WeatherBench: a benchmark data set for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems, November 2020. URL: https://doi.org/10.1029/2020ms002203, doi:10.1029/2020ms002203.

[WPL+17]

Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers. ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, July 2017. URL: https://doi.org/10.1109/cvpr.2017.369, doi:10.1109/cvpr.2017.369.

[YCN+15]

Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. Understanding neural networks through deep visualization. In Deep Learning Workshop, International Conference on Machine Learning (ICML). 2015.