Publications
- Qiang, Li., Mingkun, T., Dan, Z., Daoan, Z., Xun, Z., Porawit, K., Shengzhao, L., Lujun, Li., & S.H., C. (). How LLMs React to Industrial Spatio-Temporal Data? Assessing Hallucination with a Novel Traffic Incident Benchmark Dataset. in 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)
- Aishwarya, V., Michael, K., Andrea, B.-C., Mimoza, D., Ntambwe A S, M., Danijela, V., Daniel, L., Mingkun, T., Cedric, P., Tim, N., Martin, L., & Bánk, B. (). “UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models. GigaScience, 13. doi: 10.1093/gigascience/giae087.
- Mingkun, T., Daniel, L., & Tim W. , N. (). The Impact of Data Augmentations on Deep Learning-Based Marine Object Classification in Benthic Image Transects. Sensors, 22 (14), 5383. doi: 10.3390/s22145383 .
Mingkun Tan
Heisenbergstr. 2
48149 Münster