| 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 | 78 | 2019 |
| Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks Y Song, Z Liu, W Bi, R Yan, M Zhang ACL 2020, 2019 | 30* | 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, 2020 | 15 | 2020 |
| When does maml work the best? an empirical study on model-agnostic meta-learning in nlp applications Z Liu, R Zhang, Y Song, M Zhang arXiv preprint arXiv:2005.11700, 2020 | 8 | 2020 |
| 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 | 8 | 2019 |
| Graphine: A dataset for graph-aware terminology definition generation Z Liu, S Wang, Y Gu, R Zhang, M Zhang, S Wang arXiv preprint arXiv:2109.04018, 2021 | 7 | 2021 |
| Graphine: A dataset for graph-aware terminology definition generation Z Liu, S Wang, Y Gu, R Zhang, M Zhang, S Wang EMNLP 2021, 2021 | 7 | 2021 |
| 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 |
| 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, ... The Journal of Urology 201 (Supplement 4), e483-e483, 2019 | 1 | 2019 |
| 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 | | 2022 |
| 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 The Journal of Urology 201 (Supplement 4), e874-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 Universitypku.edu.cn의 이메일 확인됨
