Data and code underlying the publication: Metric-DST: Mitigating Selection Bias Through Diversity-guided Semi Supervised Metric Learning

DOI:10.4121/206ef581-11a1-4a01-9595-576e7ec8b7c1.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/206ef581-11a1-4a01-9595-576e7ec8b7c1
Datacite citation style:
Tepeli, Yasin; de Wolf, Mathijs; Gonçalves, Joana (2024): Data and code underlying the publication: Metric-DST: Mitigating Selection Bias Through Diversity-guided Semi Supervised Metric Learning. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/206ef581-11a1-4a01-9595-576e7ec8b7c1.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This repository consists of Data/Code to reproduce the results of the paper "Metric-DST: Mitigating Selection Bias Through Diversity-guided Semi Supervised Metric Learning".

The data is shared at: https://doi.org/10.6084/m9.figshare.27720726.v1

The code is shared at: https://github.com/joanagoncalveslab/Metric-DST

History

  • 2024-11-25 first online, published, posted

Publisher

4TU.ResearchData

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft Bioinformatics Lab