| GPT understands, too X Liu, Y Zheng, Z Du, M Ding, Y Qian, Z Yang, J Tang AI Open 5, 208-215, 2024 | 1566* | 2024 |
| 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 | 1394* | 2021 |
| Glm-130b: An open bilingual pre-trained model A Zeng, X Liu, Z Du, Z Wang, H Lai, M Ding, Z Yang, Y Xu, W Zheng, X Xia, ... ICLR 2023, 2022 | 992* | 2022 |
| 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 | 979 | 2020 |
| 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 | 744 | 2021 |
| Cogvlm: Visual expert for pretrained language models W Wang, Q Lv, W Yu, W Hong, J Qi, Y Wang, J Ji, Z Yang, L Zhao, X Song, ... NeurIPS 2024, 2023 | 455 | 2023 |
| CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers W Hong*, M Ding*, W Zheng, X Liu, J Tang ICLR 2023, 2022 | 403 | 2022 |
| Imagereward: Learning and evaluating human preferences for text-to-image generation J Xu, X Liu, Y Wu, Y Tong, Q Li, M Ding, J Tang, Y Dong NeurIPS 2023, 2023 | 310 | 2023 |
| CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers M Ding, W Zheng, W Hong, J Tang NeurIPS 2022, 2022 | 305 | 2022 |
| 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 | 301 | 2021 |
| Cognitive graph for multi-hop reading comprehension at scale M Ding, C Zhou, Q Chen, H Yang, J Tang ACL 2019, 2019 | 296 | 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 | 268 | 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 | 223 | 2019 |
| Understanding Negative Sampling in Graph Representation Learning Z Yang*, M Ding*, C Zhou, H Yang, J Zhou, J Tang KDD 2020, 2020 | 207 | 2020 |
| 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 | 198 | 2021 |
| Cogagent: A visual language model for gui agents W Hong, W Wang, Q Lv, J Xu, W Yu, J Ji, Y Wang, Z Wang, Y Dong, ... CVPR 2024, 2023 | 196 | 2023 |
| CogLTX: Applying BERT to Long Texts M Ding, C Zhou, H Yang, J Tang NeurIPS 2020, 2020 | 171 | 2020 |
| 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 | 144 | 2021 |
| 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 | 128 | 2018 |
| Wudaocorpora: A super large-scale chinese corpora for pre-training language models S Yuan, H Zhao, Z Du, M Ding, X Liu, Y Cen, X Zou, Z Yang, J Tang AI Open 2, 65-68, 2021 | 107* | 2021 |
Tang JieWeBank Chair Professor, Tsinghua UniversityZweryfikowany adres z tsinghua.edu.cn
