Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach Y Yu, S Zuo, H Jiang, W Ren, T Zhao, C Zhang arXiv preprint arXiv:2010.07835, 2020 | 113 | 2020 |
A stochastic model of cascading failure dynamics in communication networks W Ren, J Wu, X Zhang, R Lai, L Chen IEEE Transactions on Circuits and Systems II: Express Briefs 65 (5), 632-636, 2018 | 98 | 2018 |
Denoising multi-source weak supervision for neural text classification W Ren, Y Li, H Su, D Kartchner, C Mitchell, C Zhang arXiv preprint arXiv:2010.04582, 2020 | 69 | 2020 |
Sequential restorations of complex networks after cascading failures Y Huang, J Wu, W Ren, KT Chi, Z Zheng IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (1), 400-411, 2018 | 60 | 2018 |
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision D Kartchner, W Ren, D Nakajima An, C Zhang, CS Mitchell Advances in neural information processing systems, 2020 | 5 | 2020 |
Rule-enhanced active learning for semi-automated weak supervision D Kartchner, D Nakajima An, W Ren, C Zhang, CS Mitchell AI 3 (1), 211-228, 2022 | 3 | 2022 |
Amortized Network Intervention to Steer the Excitatory Point Processes Z Song, W Ren, S Li arXiv preprint arXiv:2310.04159, 2023 | | 2023 |
Rule-Enhanced Active Learning for Semi-Automated Weak Supervision. AI 2022, 3, 211–228 D Kartchner, D Nakajima An, W Ren, C Zhang, CS Mitchell s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2022 | | 2022 |