Chen, Yulin
National University of Singapore
7 papers on CoChatORCID 0000-0001-5679-4055
Most cited papers
- Verified
The sheep genome illuminates biology of the rumen and lipid metabolism
Jiang, Y., Xie, M., Chen, W., Talbot, R., Maddox, J. F., Faraut, T., Wu, C., Muzny, D. M., Li, Y., Zhang, W., et al. (2014) · Science
Genetic and phenotypic traits in livestockRuminant Nutrition and Digestive Physiology
Cited by 514
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The origin of domestication genes in goats
Zheng, Z., Wang, X., Li, M., Li, Y., Yang, Z., Wang, X., Pan, X., Gong, M., Zhang, Y., Guo, Y., et al. (2020) · Science Advances
Genetic and phenotypic traits in livestockGenetic diversity and population structure
Cited by 184
- Verified
Genetics of the phenotypic evolution in sheep: a molecular look at diversity-driving genes
Kalds, P., Zhou, S., Gao, Y., Cai, B., Huang, S., Chen, Y., & Wang, X. (2022) · Genetics Selection Evolution
Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals
Cited by 85
- Verified
OpenPrompt: An Open-source Framework for Prompt-learning
Ding, N., Hu, S., Zhao, W., Chen, Y., Liu, Z., Zheng, H.-T., & Sun, M. (2021) · arXiv
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 60
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Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2022) · arXiv
Domain Adaptation and Few-Shot LearningTopic Modeling
Cited by 20
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Defense against prompt injection attack by leveraging attack techniques
Chen, Y., Li, H., Zheng, Z., Wu, D., Song, Y., & Hooi, B. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Adversarial Robustness in Machine LearningSecurity and Verification in Computing
Cited by 19
- Verified
Backdoor-powered prompt injection attacks nullify defense methods
Chen, Y., Li, H., Sui, Y., Song, Y., & Hooi, B. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025
Adversarial Robustness in Machine LearningTopic Modeling
Cited by 1
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