Li, Qing

“Least squares generative adversarial networks,”

Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Generative Adversarial Networks and Image SynthesisDigital Media Forensic Detection

Cited by 5,437

A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., & Li, Q. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Topic ModelingInformation Retrieval and Search Behavior

Cited by 827

Recommender Systems in the Era of Large Language Models (LLMs)

Zhao, Z., Fan, W., Li, J., Liu, Y., Mei, X., Wang, Y., Wen, Z., Wang, F., Zhao, X., Tang, J., et al. (2024) · IEEE Transactions on Knowledge and Data Engineering

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 481

Contrastive Learning Models for Sentence Representations

Xu, L., Xie, H., Li, Z., Wang, F. L., Wang, W., & Li, Q. (2023) · ACM Transactions on Intelligent Systems and Technology

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 50

Knowledge-enhanced Black-box Attacks for Recommendations

Chen, J., Fan, W., Zhu, G., Zhao, X., Yuan, C., Li, Q., & Huang, Y. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Recommender Systems and TechniquesAdversarial Robustness in Machine Learning

Cited by 49

Adversarial Attacks for Black-Box Recommender Systems via Copying Transferable Cross-Domain User Profiles

Fan, W., Zhao, X., Li, Q., Derr, T., Ma, Y., Liu, H., Wang, J., & Tang, J. (2023) · IEEE Transactions on Knowledge and Data Engineering

Recommender Systems and TechniquesAdversarial Robustness in Machine Learning

Cited by 41

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

Ao, J., Wang, R., Zhou, L., Wang, C., Ren, S., Wu, Y., Liu, S., Ko, T., Li, Q., Zhang, Y., et al. (2021) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 30

A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models

Ning, L., Liang, Z., Jiang, Z., Qu, H., Ding, Y., Fan, W., Wei, X.-Y., Lin, S., Liu, H., Yu, P. S., et al. (2025) · arXiv

Web Data Mining and AnalysisInformation Retrieval and Search Behavior

Cited by 30

CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent

Ning, L.-B., Wang, S., Fan, W., Li, Q., Xu, X., Chen, H., & Huang, F. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Adversarial Robustness in Machine LearningRecommender Systems and Techniques

Cited by 22

ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

Ge, Z., Li, H., Wang, Y., Hu, N., Zhang, C. J., & Li, Q. (2026) · Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 2

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models

Zhang, X., Wei, X., Wu, J., Wu, J., Zhang, Z., Lei, Z., & Li, Q. (2025) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 0