| Unsupervised deep embedding for clustering analysis J Xie, R Girshick, A Farhadi International conference on machine learning, 478-487, 2016 | 2880 | 2016 |
| Image Denoising and Inpainting with Deep Neural Networks J Xie, L Xu, E Chen Advances in Neural Information Processing Systems 25, 350-358, 2012 | 1756 | 2012 |
| Bag of tricks for image classification with convolutional neural networks T He, Z Zhang, H Zhang, Z Zhang, J Xie, M Li Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1472 | 2019 |
| Gaan: Gated attention networks for learning on large and spatiotemporal graphs J Zhang, X Shi, J Xie, H Ma, I King, DY Yeung arXiv preprint arXiv:1803.07294, 2018 | 664 | 2018 |
| Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks J Xie, R Girshick, A Farhadi Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 476 | 2016 |
| Co-occurrent features in semantic segmentation H Zhang, H Zhang, C Wang, J Xie Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 286 | 2019 |
| Bag of freebies for training object detection neural networks Z Zhang, T He, H Zhang, Z Zhang, J Xie, M Li arXiv preprint arXiv:1902.04103, 2019 | 200 | 2019 |
| Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... The Journal of Machine Learning Research 21 (1), 845-851, 2020 | 194 | 2020 |
| Label leakage and protection in two-party split learning O Li, J Sun, X Yang, W Gao, H Zhang, J Xie, V Smith, C Wang arXiv preprint arXiv:2102.08504, 2021 | 93 | 2021 |
| xgboost: Extreme Gradient Boosting, 2020 T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ... R package version 1 (1.1), 2020 | 77 | 2020 |
| On the feasibility of user de-anonymization from shared mobile sensor data ND Lane, J Xie, T Moscibroda, F Zhao Proceedings of the Third International Workshop on Sensing Applications on …, 2012 | 41 | 2012 |
| Vertical federated learning without revealing intersection membership J Sun, X Yang, Y Yao, A Zhang, W Gao, J Xie, C Wang arXiv preprint arXiv:2106.05508, 2021 | 24 | 2021 |
| Defending against reconstruction attack in vertical federated learning J Sun, Y Yao, W Gao, J Xie, C Wang arXiv preprint arXiv:2107.09898, 2021 | 18 | 2021 |
| Differentially private label protection in split learning X Yang, J Sun, Y Yao, J Xie, C Wang arXiv preprint arXiv:2203.02073, 2022 | 15 | 2022 |
| Differentially private auc computation in vertical federated learning J Sun, X Yang, Y Yao, J Xie, D Wu, C Wang arXiv preprint arXiv:2205.12412, 2022 | 3 | 2022 |
| Transfer learning with deep neural networks for computer vision J Xie | 2 | 2019 |
| Dpauc: Differentially private auc computation in federated learning J Sun, X Yang, Y Yao, J Xie, D Wu, C Wang Proceedings of the AAAI Conference on Artificial Intelligence 37 (12), 15170 …, 2023 | 1 | 2023 |
| Method for processing model parameters, and apparatus C Chen, P Zhao, D Wu, J Xie, F Chenliaohui, LI Longyijia, L Huang, L Wu, ... US Patent App. 17/886,746, 2023 | | 2023 |
| Network connection method and device for training participant end of common training model LI Longyijia, C Chen, D Wu, F Chenliaohui, P Zhao, J Xie, Y Chen, L Wu, ... US Patent App. 17/886,771, 2022 | | 2022 |
| Method and apparatus for evaluating joint training model LI Longyijia, D Wu, C Chen, F Chenliaohui, P Zhao, J Xie, Y Chen, L Wu, ... US Patent App. 17/887,022, 2022 | | 2022 |
Tong HeSenior Applied Scientist @ AWSVerified email at amazon.com
