| GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training J Qiu, Q Chen, Y Dong, J Zhang, H Yang, M Ding, K Wang, J Tang KDD 2020, 2020 | 529 | 2020 |
| GPT understands, too X Liu, Y Zheng, Z Du, M Ding, Y Qian, Z Yang, J Tang arXiv preprint arXiv:2103.10385, 2021 | 500* | 2021 |
| CogView: Mastering Text-to-Image Generation via Transformers M Ding, Z Yang, W Hong, W Zheng, C Zhou, D Yin, J Lin, X Zou, Z Shao, ... NeurIPS 2021, 2021 | 265 | 2021 |
| Cognitive graph for multi-hop reading comprehension at scale M Ding, C Zhou, Q Chen, H Yang, J Tang ACL 2019, 2019 | 215 | 2019 |
| ProNE: Fast and Scalable Network Representation Learning J Zhang, Y Dong, Y Wang, J Tang, M Ding Proceedings of the 28th International Joint Conference on Artificial …, 2019 | 148 | 2019 |
| Towards Knowledge-Based Recommender Dialog System Q Chen, J Lin, Y Zhang, M Ding, Y Cen, H Yang, J Tang Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 144 | 2019 |
| Understanding Negative Sampling in Graph Representation Learning Z Yang*, M Ding*, C Zhou, H Yang, J Zhou, J Tang KDD 2020, 2020 | 111 | 2020 |
| Semi-supervised learning on graphs with generative adversarial nets M Ding, J Tang, J Zhang Proceedings of the 27th ACM International Conference on Information and …, 2018 | 110 | 2018 |
| M6: A chinese multimodal pretrainer J Lin, R Men, A Yang, C Zhou, M Ding, Y Zhang, P Wang, A Wang, ... arXiv preprint arXiv:2103.00823, 2021 | 104 | 2021 |
| Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks Q Lv*, M Ding*, Q Liu, Y Chen, W Feng, S He, C Zhou, J Jiang, Y Dong, ... KDD 2021, 2021 | 96 | 2021 |
| CogLTX: Applying BERT to Long Texts M Ding, C Zhou, H Yang, J Tang NeurIPS 2020, 2020 | 87 | 2020 |
| All nlp tasks are generation tasks: A general pretraining framework Z Du, Y Qian, X Liu, M Ding, J Qiu, Z Yang, J Tang ACL 2022, 2021 | 83* | 2021 |
| CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers M Ding, W Zheng, W Hong, J Tang NeurIPS 2022, 2022 | 76 | 2022 |
| CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers W Hong*, M Ding*, W Zheng, X Liu, J Tang ICLR 2023, 2022 | 61 | 2022 |
| MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems T Huang, Y Dong, M Ding, Z Yang, W Feng, X Wang, J Tang KDD 2021, 2021 | 58 | 2021 |
| M6-ufc: Unifying multi-modal controls for conditional image synthesis Z Zhang, J Ma, C Zhou, R Men, Z Li, M Ding, J Tang, J Zhou, H Yang NeurIPS 2021, 2021 | 48* | 2021 |
| Cognitive knowledge graph reasoning for one-shot relational learning Z Du, C Zhou, M Ding, H Yang, J Tang arXiv preprint arXiv:1906.05489, 2019 | 28 | 2019 |
| Controllable Generation from Pre-trained Language Models via Inverse Prompting X Zou, D Yin, Q Zhong, M Ding, Z Yang, J Tang arXiv preprint arXiv:2103.10685, 2021 | 26 | 2021 |
| FewNLU: Benchmarking state-of-the-art methods for few-shot natural language understanding Y Zheng, J Zhou, Y Qian, M Ding, J Li, R Salakhutdinov, J Tang, S Ruder, ... ACL 2022, 2021 | 21 | 2021 |
| Adaptive Diffusion in Graph Neural Networks J Zhao, Y Dong, M Ding, E Kharlamov, J Tang Advances in Neural Information Processing Systems (NeurIPS 2021), 2021 | 20 | 2021 |
Tang JieProfessor, Tsinghua UniversityVerified email at tsinghua.edu.cn
