| 3D convolutional neural networks for human action recognition S Ji, W Xu, M Yang, K Yu IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1), 221-231, 2013 | 7329 | 2013 |
| Graph U-Nets H Gao, S Ji The 36th International Conference on Machine Learning, 2083-2092, 2019 | 1283 | 2019 |
| Deep convolutional neural networks for multi-modality isointense infant brain image segmentation W Zhang, R Li, H Deng, L Wang, W Lin, S Ji, D Shen NeuroImage 108, 214-224, 2015 | 947 | 2015 |
| SLEP: Sparse learning with efficient projections J Liu, S Ji, J Ye Arizona State University 6 (491), 7, 2009 | 822* | 2009 |
| Multi-task feature learning via efficient l2, 1-norm minimization J Liu, S Ji, J Ye arXiv preprint arXiv:1205.2631, 2012 | 796 | 2012 |
| Large-scale learnable graph convolutional networks H Gao, Z Wang, S Ji Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 665 | 2018 |
| Explainability in graph neural networks: A taxonomic survey H Yuan, H Yu, S Gui, S Ji IEEE transactions on pattern analysis and machine intelligence 45 (5), 5782-5799, 2022 | 610 | 2022 |
| Deep learning based imaging data completion for improved brain disease diagnosis R Li, W Zhang, HI Suk, L Wang, J Li, D Shen, S Ji Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 582 | 2014 |
| Towards deeper graph neural networks M Liu, H Gao, S Ji Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 573 | 2020 |
| An accelerated gradient method for trace norm minimization S Ji, J Ye Proceedings of the 26th annual international conference on machine learning …, 2009 | 542 | 2009 |
| Xgnn: Towards model-level explanations of graph neural networks H Yuan, J Tang, X Hu, S Ji Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 359 | 2020 |
| On explainability of graph neural networks via subgraph explorations H Yuan, H Yu, J Wang, K Li, S Ji International conference on machine learning, 12241-12252, 2021 | 355 | 2021 |
| Self-supervised learning of graph neural networks: A unified review Y Xie, Z Xu, J Zhang, Z Wang, S Ji IEEE transactions on pattern analysis and machine intelligence 45 (2), 2412-2429, 2022 | 343 | 2022 |
| Canonical correlation analysis for multilabel classification: A least-squares formulation, extensions, and analysis L Sun, S Ji, J Ye IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (1), 194-200, 2010 | 334 | 2010 |
| A robust deep model for improved classification of AD/MCI patients F Li, L Tran, KH Thung, S Ji, D Shen, J Li IEEE journal of biomedical and health informatics 19 (5), 1610-1616, 2015 | 333 | 2015 |
| Feature selection based on structured sparsity: A comprehensive study J Gui, Z Sun, S Ji, D Tao, T Tan IEEE transactions on neural networks and learning systems 28 (7), 1490-1507, 2016 | 322 | 2016 |
| Discriminant sparse neighborhood preserving embedding for face recognition J Gui, Z Sun, W Jia, R Hu, Y Lei, S Ji Pattern Recognition 45 (8), 2884-2893, 2012 | 300 | 2012 |
| Hypergraph spectral learning for multi-label classification L Sun, S Ji, J Ye Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008 | 288 | 2008 |
| Spherical message passing for 3d molecular graphs Y Liu, L Wang, M Liu, Y Lin, X Zhang, B Oztekin, S Ji International Conference on Learning Representations (ICLR), 2022 | 262* | 2022 |
| Extracting shared subspace for multi-label classification S Ji, L Tang, S Yu, J Ye Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008 | 249 | 2008 |
Jieping Ye, IEEE Fellow & ACM Distin...Alibaba Group在 umich.edu 的电子邮件经过验证
