| A comprehensive survey on deep graph representation learning W Ju, Z Fang, Y Gu, Z Liu, Q Long, Z Qiao, Y Qin, J Shen, F Sun, Z Xiao, ... Neural Networks, 106207, 2024 | 168 | 2024 |
| A deep‐learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot‐assisted radical prostatectomy AJ Hung, J Chen, S Ghodoussipour, PJ Oh, Z Liu, J Nguyen, ... BJU international 124 (3), 487-495, 2019 | 135 | 2019 |
| Molxpt: Wrapping molecules with text for generative pre-training Z Liu, W Zhang, Y Xia, L Wu, S Xie, T Qin, M Zhang, TY Liu arXiv preprint arXiv:2305.10688, 2023 | 67 | 2023 |
| Few-shot molecular property prediction via hierarchically structured learning on relation graphs W Ju, Z Liu, Y Qin, B Feng, C Wang, Z Guo, X Luo, M Zhang Neural Networks 163, 122-131, 2023 | 50 | 2023 |
| Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks Y Song, Z Liu, W Bi, R Yan, M Zhang ACL 2020, 2019 | 44* | 2019 |
| Multi-task learning via adaptation to similar tasks for mortality prediction of diverse rare diseases L Liu, Z Liu, H Wu, Z Wang, J Shen, Y Song, M Zhang Amia annual symposium proceedings 2020, 763, 2021 | 29 | 2021 |
| Poisoning medical knowledge using large language models J Yang, H Xu, S Mirzoyan, T Chen, Z Liu, Z Liu, W Ju, L Liu, Z Xiao, ... Nature Machine Intelligence 6 (10), 1156-1168, 2024 | 21* | 2024 |
| When does maml work the best? an empirical study on model-agnostic meta-learning in nlp applications Z Liu, R Zhang, Y Song, W Ju, M Zhang arXiv preprint arXiv:2005.11700, 2020 | 15 | 2020 |
| Graphine: A dataset for graph-aware terminology definition generation Z Liu, S Wang, Y Gu, R Zhang, M Zhang, S Wang EMNLP 2021, 2021 | 13 | 2021 |
| Early prediction of sepsis from clinical data via heterogeneous event aggregation L Liu, H Wu, Z Wang, Z Liu, M Zhang 2019 Computing in Cardiology (CinC), Page 1-Page 4, 2019 | 12 | 2019 |
| A bioactivity foundation model using pairwise meta-learning B Feng, Z Liu, N Huang, Z Xiao, H Zhang, S Mirzoyan, H Xu, J Hao, Y Xu, ... Nature Machine Intelligence 6 (8), 962-974, 2024 | 7 | 2024 |
| Pathway2text: Dataset and method for biomedical pathway description generation J Yang, Z Liu, M Zhang, S Wang Findings of the Association for Computational Linguistics: NAACL 2022, 1441-1454, 2022 | 2 | 2022 |
| SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision K Zheng, S Liang, J Yang, B Feng, Z Liu, W Ju, Z Xiao, M Zhang arXiv preprint arXiv:2412.05569, 2024 | 1 | 2024 |
| Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data B Feng, T Ao, Z Liu, W Ju, L Liu, M Zhang arXiv preprint arXiv:2303.16856, 2023 | 1 | 2023 |
| MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks Z Liu, K Duan, J Yang, H Xu, M Zhang, S Wang EMNLP 2022, 2022 | 1 | 2022 |
| PD27-07 DEEP LEARNING MODEL TO PREDICT TIME TO URINARY CONTINENCE RECOVERY AFTER ROBOT-ASSISTED RADICAL PROSTATECTOMY USING AUTOMATED PERFORMANCE METRICS AND CLINICAL DATA A Hung, J Chen, Z Liu, J Nguyen, P Oh, D Stewart, D Remulla, T Chu, ... Journal of Urology 201, e483, 2019 | 1 | 2019 |
| ExLM: Rethinking the Impact of Tokens in Masked Language Models K Zheng, J Yang, S Liang, B Feng, Z Liu, W Ju, Z Xiao, M Zhang arXiv preprint arXiv:2501.13397, 2025 | | 2025 |
| MP60-14 COMPARING DEEP LEARNING, MACHINE LEARNING, AND CONVENTIONAL REGRESSION AS PREDICTIVE MODELS OF TIME TO URINARY CONTINENCE AFTER ROBOT-ASSISTED RADICAL PROSTATECTOMY A Hung, J Chen, Z Liu, J Nguyen, S Purushotham, Y Liu Journal of Urology 201, e874, 2019 | | 2019 |
| Deep learning model to predict urinary continence after robot-assisted radical prostatectomy A Hung, J Chen, ZQ Liu, J Nguyen, S Purushotham, Y Liu European Urology Supplements 18 (1), e851, 2019 | | 2019 |
Ming Zhang（张铭）Professor,Peking University確認したメール アドレス: pku.edu.cn