| Unsupervised deep embedding for clustering analysis J Xie, R Girshick, A Farhadi International conference on machine learning, 478-487, 2016 | 2047 | 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 | 1565 | 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 | 921 | 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 | 431 | 2018 |
| Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks J Xie, R Girshick, A Farhadi European conference on computer vision, 842-857, 2016 | 405 | 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 | 229 | 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 | 144 | 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, ... J. Mach. Learn. Res. 21 (23), 1-7, 2020 | 143 | 2020 |
| xgboost: extreme gradient boosting. R package version 0.71. 2 T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ... Grin Verlag: München, Germnay, 2018 | 91 | 2018 |
| xgboost: Extreme Gradient Boosting (2017) T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ... R package version 0.6-4, 2015 | 69 | 2015 |
| 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 | 42 | 2012 |
| 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 | 41 | 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 | 35 | 2021 |
| 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 | 7 | 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 | 5 | 2021 |
| Differentially Private Label Protection in Split Learning X Yang, J Sun, Y Yao, J Xie, C Wang arXiv preprint arXiv:2203.02073, 2022 | 1 | 2022 |
| Transfer learning with deep neural networks for computer vision J Xie | 1 | 2019 |
| DPAUC: Differentially Private AUC Computation in Federated Learning J Sun, X Yang, Y Yao, J Xie, D Wu, C Wang arXiv preprint arXiv:2208.12294, 2022 | | 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 | | 2022 |
| A Social Influence Model Based On Circuit Theory B Xiang, E Chen, Q Liu, H Xiong, Y Yang, J Xie arXiv preprint arXiv:1205.6024, 2012 | | 2012 |
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