| Recurrent neural networks for multivariate time series with missing values Z Che, S Purushotham, K Cho, D Sontag, Y Liu Scientific reports 8 (1), 6085, 2018 | 2153 | 2018 |
| Benchmarking deep learning models on large healthcare datasets S Purushotham*, C Meng*, Z Che, Y Liu Journal of biomedical informatics 83, 112-134, 2018 | 473 | 2018 |
| Interpretable deep models for ICU outcome prediction Z Che, S Purushotham, R Khemani, Y Liu AMIA Annual Symposium Proceedings 2016, 371, 2016 | 357 | 2016 |
| Deep computational phenotyping Z Che*, D Kale*, W Li, MT Bahadori, Y Liu Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 326 | 2015 |
| Boosting deep learning risk prediction with generative adversarial networks for electronic health records Z Che*, Y Cheng*, S Zhai, Z Sun, Y Liu IEEE International Conference on Data Mining (ICDM) 2017, 787-792, 2017 | 197 | 2017 |
| Distilling knowledge from deep networks with applications to healthcare domain Z Che, S Purushotham, R Khemani, Y Liu arXiv preprint arXiv:1512.03542, 2015 | 164 | 2015 |
| Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes AJ Hung, J Chen, Z Che, T Nilanon, A Jarc, M Titus, PJ Oh, IS Gill, Y Liu Journal of endourology 32 (5), 438-444, 2018 | 152 | 2018 |
| Robust Unsupervised Video Anomaly Detection by Multipath Frame Prediction X Wang, Z Che, B Jiang, N Xiao, K Yang, J Tang, J Ye, J Wang, Q Qi IEEE Transactions on Neural Networks and Learning Systems 33 (6), 2301-2312, 2021 | 134 | 2021 |
| Interpretable Deep Learning Framework for Mining and Predictive Modeling of Health Care Data Y Liu, Z Che, S Purushotham US Patent 11,144,825, 2018 | 92 | 2018 |
| Exploiting convolutional neural network for risk prediction with medical feature embedding Z Che, Y Cheng, Z Sun, Y Liu arXiv preprint arXiv:1701.07474, 2017 | 66 | 2017 |
| D-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios Z Che, G Li, T Li, B Jiang, X Shi, X Zhang, Y Lu, G Wu, Y Liu, J Ye arXiv preprint arXiv:1904.01975, 2019 | 63 | 2019 |
| Deep Learning Solutions for Classifying Patients on Opioid Use Z Che, J St. Sauver, H Liu, Y Liu AMIA Annual Symposium Proceedings 2017, 525-534, 2017 | 63 | 2017 |
| Hierarchical deep generative models for multi-rate multivariate time series Z Che*, S Purushotham*, G Li*, B Jiang, Y Liu International Conference on Machine Learning (ICML) 2018, 784-793, 2018 | 51 | 2018 |
| An examination of multivariate time series hashing with applications to health care DC Kale*, D Gong*, Z Che*, Y Liu, G Medioni, R Wetzel, P Ross IEEE International Conference on Data Mining (ICDM) 2014, 260-269, 2014 | 48 | 2014 |
| Causal phenotype discovery via deep networks DC Kale, Z Che, MT Bahadori, W Li, Y Liu, R Wetzel AMIA Annual Symposium Proceedings 2015, 677, 2015 | 41 | 2015 |
| DECADE: A Deep Metric Learning Model for Multivariate Time Series Z Che, X He, K Xu, Y Liu KDD Workshop on Mining and Learning from Time Series (MiLeTS) 2017, 2017 | 39 | 2017 |
| Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control Z Xu, K Wu, Z Che, J Tang, J Ye Advances in Neural Information Processing Systems (NeurIPS) 33, 15146--15155, 2020 | 37 | 2020 |
| Deep learning solutions to computational phenotyping in health care Z Che, Y Liu IEEE International Conference on Data Mining Workshops (ICDMW) 2017, 1100-1109, 2017 | 36 | 2017 |
| Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not? N Liu*, G Yuan*, Z Che, X Shen, X Ma, Q Jin, J Ren, J Tang, S Liu, ... International Conference on Machine Learning (ICML) 2021, 7011-7020, 2021 | 34 | 2021 |
| DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search Y Guan, N Liu, P Zhao, Z Che, K Bian, Y Wang, J Tang IEEE Transactions on Neural Networks and Learning Systems 34 (12), 9847-9858, 2022 | 29 | 2022 |
Yan LiuProfessor, Director of Machine Learning Center@USC在 usc.edu 的电子邮件经过验证
