| Understanding negative sampling in graph representation learning Z Yang, M Ding, C Zhou, H Yang, J Zhou, J Tang Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 169 | 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 Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 139 | 2021 |
| Stam: A spatiotemporal aggregation method for graph neural network-based recommendation Z Yang, M Ding, B Xu, H Yang, J Tang Proceedings of the ACM Web Conference 2022, 3217-3228, 2022 | 32 | 2022 |
| Region or global a principle for negative sampling in graph-based recommendation Z Yang, M Ding, X Zou, J Tang, B Xu, C Zhou, H Yang IEEE Transactions on Knowledge and Data Engineering, 2022 | 21 | 2022 |
| Gpt can solve mathematical problems without a calculator Z Yang, M Ding, Q Lv, Z Jiang, Z He, Y Guo, J Bai, J Tang arXiv preprint arXiv:2309.03241, 2023 | 17 | 2023 |
| Batchsampler: Sampling mini-batches for contrastive learning in vision, language, and graphs Z Yang, T Huang, M Ding, Y Dong, R Ying, Y Cen, Y Geng, J Tang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 4 | 2023 |
| Vilta: Enhancing vision-language pre-training through textual augmentation W Wang, Z Yang, B Xu, J Li, Y Sun Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 3 | 2023 |
| Does Negative Sampling Matter? A Review with Insights into its Theory and Applications Z Yang, M Ding, T Huang, Y Cen, J Song, B Xu, Y Dong, J Tang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 2 | 2024 |
| TriSampler: A Better Negative Sampling Principle for Dense Retrieval Z Yang, Z Shao, Y Dong, J Tang arXiv preprint arXiv:2402.11855, 2024 | | 2024 |
Ming DingTsinghua University, Zhipu AI在 mails.tsinghua.edu.cn 的电子邮件经过验证
