| Incorporating bert into neural machine translation J Zhu, Y Xia, L Wu, D He, T Qin, W Zhou, H Li, TY Liu ICLR-2020, 2020 | 499 | 2020 |
| Soft contextual data augmentation for neural machine translation J Zhu, F Gao, L Wu, Y Xia, T Qin, W Zhou, X Cheng, TY Liu ACL-2019, 2019 | 170* | 2019 |
| Dual-view molecular pre-training J Zhu, Y Xia, L Wu, S Xie, W Zhou, T Qin, H Li, TY Liu Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 92 | 2023 |
| Unified 2D and 3D Pre-Training of Molecular Representations J Zhu, Y Xia, L Wu, S Xie, T Qin, W Zhou, H Li, TY Liu KDD-2022, 2022 | 70 | 2022 |
| Masked contrastive representation learning for reinforcement learning J Zhu, Y Xia, L Wu, J Deng, W Zhou, T Qin, TY Liu, H Li IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 3421-3433, 2022 | 65 | 2022 |
| Biot5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations Q Pei, W Zhang, J Zhu, K Wu, K Gao, L Wu, Y Xia, R Yan arXiv preprint arXiv:2310.07276, 2023 | 60 | 2023 |
| Multimodal sentiment analysis with two-phase multi-task learning B Yang, L Wu, J Zhu, B Shao, X Lin, TY Liu IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, 2015-2024, 2022 | 56 | 2022 |
| Direct molecular conformation generation J Zhu, Y Xia, C Liu, L Wu, S Xie, T Wang, Y Wang, W Zhou, T Qin, H Li, ... TMLR-2022, 2022 | 56 | 2022 |
| Return-based contrastive representation learning for reinforcement learning G Liu, C Zhang, L Zhao, T Qin, J Zhu, J Li, N Yu, TY Liu ICLR-2021, 2021 | 54 | 2021 |
| Normalization helps training of quantized lstm L Hou, J Zhu, J Kwok, F Gao, T Qin, T Liu Advances in Neural Information Processing Systems 32, 2019 | 51 | 2019 |
| A study of BERT for context-aware neural machine translation X Wu, Y Xia, J Zhu, L Wu, S Xie, T Qin Machine Learning 111 (3), 917-935, 2022 | 33 | 2022 |
| Biot5+: Towards generalized biological understanding with iupac integration and multi-task tuning Q Pei, L Wu, K Gao, X Liang, Y Fang, J Zhu, S Xie, T Qin, R Yan arXiv preprint arXiv:2402.17810, 2024 | 28 | 2024 |
| Microsoft Research Asia's Systems for WMT19 Y Xia, X Tan, F Tian, F Gao, W Chen, Y Fan, L Gong, Y Leng, R Luo, ... WMT-2019, 2019 | 27 | 2019 |
| Fabind: Fast and accurate protein-ligand binding Q Pei, K Gao, L Wu, J Zhu, Y Xia, S Xie, T Qin, K He, TY Liu, R Yan Advances in Neural Information Processing Systems 36, 2024 | 22 | 2024 |
| Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey Q Pei, L Wu, K Gao, J Zhu, Y Wang, Z Wang, T Qin, R Yan arXiv preprint arXiv:2403.01528, 2024 | 19 | 2024 |
| Discovering drug–target interaction knowledge from biomedical literature Y Hou, Y Xia, L Wu, S Xie, Y Fan, J Zhu, T Qin, TY Liu Bioinformatics 38 (22), 5100-5107, 2022 | 17 | 2022 |
| Breaking the barriers of data scarcity in drug–target affinity prediction Q Pei, L Wu, J Zhu, Y Xia, S Xie, T Qin, H Liu, TY Liu, R Yan Briefings in Bioinformatics 24 (6), bbad386, 2023 | 16 | 2023 |
| R2-DDI: relation-aware feature refinement for drug–drug interaction prediction J Lin, L Wu, J Zhu, X Liang, Y Xia, S Xie, T Qin, TY Liu Briefings in Bioinformatics 24 (1), bbac576, 2023 | 16 | 2023 |
| Machine translation with weakly paired documents L Wu, J Zhu, D He, F Gao, T Qin, J Lai, TY Liu Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 16 | 2019 |
| mixSeq: A simple data augmentation methodfor neural machine translation X Wu, Y Xia, J Zhu, L Wu, S Xie, Y Fan, T Qin Proceedings of the 18th International Conference on Spoken Language …, 2021 | 15 | 2021 |
Lijun WuMicrosoft ResearchVerified email at microsoft.com
