Scaling Down Annotation Needs: The Capacity of Self-Supervised Learning on Diatom Classification Mingkun, Tan; Daniel, Langenkämper; Michael, Kloster; Tim W. , Nattkemper. In: iScience. Research article (journal)
How LLMs React to Industrial Spatio-Temporal Data? Assessing Hallucination with a Novel Traffic Incident Benchmark Dataset Qiang, Li; Mingkun, Tan; Dan, Zhang; Daoan, Zhang; Xun, Zhao; Porawit, Kamnoedboon; Shengzhao, Lei; Lujun, Li; S.H., Chu. (2025) In: (eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL, Volume 6: Industry Track) . (kein Verlag angegeben). Research article in edited proceedings (conference)
“UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models Aishwarya, Venkataramanan; Michael, Kloster; Andrea, Burfeid-Castellanos; Mimoza, Dani; Ntambwe A S, Mayombo; Danijela, Vidakovic; Daniel, Langenkämper; Mingkun, Tan; Cedric, Pradalier; Tim, Nattkemper; Martin, Laviale; Bánk, Beszteri. (2024) In: GigaScience, 13. Research article (journal)
The Impact of Data Augmentations on Deep Learning-Based Marine Object Classification in Benthic Image Transects Mingkun, Tan; Daniel, Langenkämper; Tim W. , Nattkemper. (2022) In: Sensors, 22(14). Research article (journal)