| Spatio-temporal graph convolutional neural network: A deep learning framework for traffic forecasting B Yu, H Yin, Z Zhu International Joint Conference of Artificial Intelligence (IJCAI 2018), 2018 | 4685* | 2018 |
| Spatial-temporal fusion graph neural networks for traffic flow forecasting M Li, Z Zhu AAAI 2021, 2020 | 831 | 2020 |
| You only propagate once: Accelerating adversarial training via maximal principle D Zhang, T Zhang, Y Lu, Z Zhu, B Dong Advances in Neural Information Processing Systems, 2019, 2019 | 493 | 2019 |
| Reinforced continual learning J Xu, Z Zhu Advances in Neural Information Processing Systems (NeurIPS 2018), 899-908, 2018 | 430 | 2018 |
| Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. K Sun, Z Lin, Z Zhu AAAI 2020, 5892-5899, 2020 | 285 | 2020 |
| The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects Z Zhu, J Wu, B Yu, L Wu, J Ma International Conference on Machine Learning (ICML 2019), 7654-7663, 2019 | 250 | 2019 |
| Novel subgroups of patients with adult-onset diabetes in Chinese and US populations X Zou, X Zhou, Z Zhu, L Ji The Lancet Diabetes & Endocrinology 7 (1), 9-11, 2019 | 227 | 2019 |
| Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes L Wu, Z Zhu The 34th International Conference on Machine Learning (ICML 2017 …, 2017 | 227 | 2017 |
| Towards understanding and improving the transferability of adversarial examples in deep neural networks L Wu, Z Zhu Asian Conference on Machine Learning, 837-850, 2020 | 179* | 2020 |
| Interpreting Adversarially Trained Convolutional Neural Networks T Zhang, Z Zhu International Conference on Machine Learning (ICML 2019), 2019 | 178 | 2019 |
| Yet another text captcha solver: A generative adversarial network based approach G Ye, Z Tang, D Fang, Z Zhu, Y Feng, P Xu, X Chen, Z Wang Proceedings of the 2018 ACM SIGSAC conference on computer and communications …, 2018 | 174 | 2018 |
| Informative Dropout for Robust Representation Learning: A Shape-bias Perspective B Shi, D Zhang, Q Dai, Z Zhu, Y Mu, J Wang International Conference for Machine Learning (ICML 2020), 2020 | 125 | 2020 |
| Efficient Neural Architecture Search via Proximal Iterations. Q Yao, J Xu, WW Tu, Z Zhu AAAI 2020, 6664-6671, 2020 | 124 | 2020 |
| On the Noisy Gradient Descent that Generalizes as SGD J Wu, W Hu, H Xiong, J Huan, V Braverman, Z Zhu International Conference for Machine Learning (ICML 2020), 2020 | 117 | 2020 |
| AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models K Sun, Z Zhu, Z Lin ICLR 2021, 2021 | 108 | 2021 |
| Learning with noise: Enhance distantly supervised relation extraction with dynamic transition matrix B Luo, Y Feng, Z Wang, Z Zhu, S Huang, R Yan, D Zhao ACL 2017, 2017 | 105 | 2017 |
| Spatio-temporal manifold learning for human motions via long-horizon modeling H Wang, ESL Ho, HPH Shum, Z Zhu IEEE transactions on visualization and computer graphics 27 (1), 216-227, 2019 | 88 | 2019 |
| Automatic data augmentation for 3D medical image segmentation J Xu, M Li, Z Zhu Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 81 | 2020 |
| St-unet: A spatio-temporal u-network for graph-structured time series modeling B Yu, H Yin, Z Zhu arXiv preprint arXiv:1903.05631, 2019 | 79 | 2019 |
| Black-box certification with randomized smoothing: A functional optimization based framework D Zhang, M Ye, C Gong, Z Zhu, Q Liu NeurIPS 2020, 2020 | 75 | 2020 |
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