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On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) Under Class-Prior Shift

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Reproducible Research in Pattern Recognition (RRPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14068))

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Abstract

This paper focuses on the implementation details of the baseline methods and a recent lightweight conditional model extrapolation algorithm LIMES [5] for streaming data under class-prior shift. LIMES achieves superior performance over the baseline methods, especially concerning the minimum-across-day accuracy, which is important for the users of the system. In this work, the key measures to facilitate reproducibility and enhance the credibility of the results are described.

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Notes

  1. 1.

    https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data.

  2. 2.

    https://cvml.ist.ac.at/geo-tweets/.

  3. 3.

    https://twarc-project.readthedocs.io/en/latest/.

  4. 4.

    https://github.com/ptomaszewska/LIMES.

  5. 5.

    https://slurm.schedmd.com/documentation.html.

  6. 6.

    https://github.com/datasets/geo-countries/blob/master/data/countries.geojson.

  7. 7.

    https://github.com/UKPLab/sentence-transformers.

References

  1. Baker, M.: 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016). https://doi.org/10.1038/533452a

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  2. Liu, C., Gao, C., Xia, X., Lo, D., Grundy, J., Yang, X.: On the reproducibility and replicability of deep learning in software engineering. ACM Trans. Softw. Eng. Methodol. 31(1), 1–46 (2022). https://doi.org/10.1145/3477535

    Article  Google Scholar 

  3. Pineau, J., et al.: Improving reproducibility in machine learning research (a report from the NeurIPS 2019 reproducibility program). J. Mach. Learn. Res. 22(164), 1–20 (2021). http://jmlr.org/papers/v22/20-303.html

  4. Tatman, R., Vanderplas, J., Dane, S.: A practical taxonomy of reproducibility for machine learning research. In: Reproducibility in Machine Learning - Workshop at ICML (2018)

    Google Scholar 

  5. Tomaszewska, P., Lampert, C.H.: Lightweight conditional model extrapolation for streaming data under class-prior shift. In: 26th International Conference on Pattern Recognition (2022)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Warsaw University of Technology, Faculty of Mathematics and Information Science, Warsaw, Poland

    Paulina Tomaszewska

  2. Institute of Science and Technology Austria (ISTA), Machine Learning and Computer Vision Group, Klosterneuburg, Austria

    Christoph H. Lampert

Authors
  1. Paulina Tomaszewska
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  2. Christoph H. Lampert
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Corresponding author

Correspondence to Paulina Tomaszewska .

Editor information

Editors and Affiliations

  1. Lumière University Lyon 2, Bron, France

    Bertrand Kerautret

  2. Ecole Normale Superieure Paris, Gif-sur-Yvette, France

    Miguel Colom

  3. University of Stasbourg, Illkirch Cedex, France

    Adrien Krähenbühl

  4. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  5. Ecole des Ponts Paris Tech, Marne- la- Vallée Cedex 2, France

    Pascal Monasse

  6. ESIEE Paris, Université Gustave Eiffel, Noisy le Grand Cedex, France

    Benjamin Perret

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Tomaszewska, P., Lampert, C.H. (2023). On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) Under Class-Prior Shift. In: Kerautret, B., Colom, M., Krähenbühl, A., Lopresti, D., Monasse, P., Perret, B. (eds) Reproducible Research in Pattern Recognition. RRPR 2022. Lecture Notes in Computer Science, vol 14068. Springer, Cham. https://doi.org/10.1007/978-3-031-40773-4_6

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  • DOI: https://doi.org/10.1007/978-3-031-40773-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40772-7

  • Online ISBN: 978-3-031-40773-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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