Tang, Jie

P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks

Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., & Tang, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 810

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., & Shi, W. (2019) · Proceedings of the IEEE

IoT and Edge/Fog ComputingAutonomous Vehicle Technology and Safety

Cited by 778

GLM-130B: An Open Bilingual Pre-trained Model

Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., Yang, Z., Xu, Y., Zheng, W., Xia, X., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 281

CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Hong, W., Ding, M., Zheng, W., Liu, X., & Tang, J. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 148

OpenAI o1 System Card

OpenAI, :, Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 50

KoLA: Carefully Benchmarking World Knowledge of Large Language Models

Yu, J., Wang, X., Tu, S., Cao, S., Zhang-Li, D., Lv, X., Peng, H., Yao, Z., Zhang, X., Li, H., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

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