| Symmetric cross entropy for robust learning with noisy labels Y Wang, X Ma, Z Chen, Y Luo, J Yi, J Bailey ICCV 2019, 2019 | 1128 | 2019 |
| Characterizing adversarial subspaces using local intrinsic dimensionality X Ma, B Li, Y Wang, SM Erfani, S Wijewickrema, ME Houle, ... ICLR2018, 2018 | 863 | 2018 |
| Improving Adversarial Robustness Requires Revisiting Misclassified Examples Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu ICLR2020, 2020 | 860 | 2020 |
| Adversarial Weight Perturbation Helps Robust Generalization D Wu, S Xia, Y Wang NeurIPS 2020, 2020 | 811 | 2020 |
| Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems X Ma, Y Niu, L Gu, Y Wang, Y Zhao, J Bailey, F Lu Pattern Recognition, 2019 | 548 | 2019 |
| Normalized Loss Functions for Deep Learning with Noisy Labels X Ma, H Huang, Y Wang, S Romano, S Erfani, J Bailey ICML2020, 2020 | 516 | 2020 |
| Dimensionality-Driven Learning with Noisy Labels X Ma*, Y Wang*, ME Houle, S Zhou, SM Erfani, ST Xia, S Wijewickrema, ... ICML2018, 2018 | 480 | 2018 |
| On the Convergence and Robustness of Adversarial Training Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu ICML2019, 2019 | 436 | 2019 |
| Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets D Wu, Y Wang, ST Xia, J Bailey, X Ma ICLR2020, 2020 | 399 | 2020 |
| Iterative Learning with Open-set Noisy Labels Y Wang, W Liu, X Ma, J Bailey, H Zha, L Song, ST Xia CVPR2018, 2018 | 377 | 2018 |
| Adversarial Neuron Pruning Purifies Backdoored Deep Models D Wu, Y Wang NeurIPS 2021, 2021 | 310 | 2021 |
| Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles R Duan, X Ma, Y Wang, J Bailey, AK Qin, Y Yang CVPR2020, 2020 | 266 | 2020 |
| Unlearnable Examples: Making Personal Data Unexploitable H Huang, X Ma, SM Erfani, J Bailey, Y Wang ICLR 2021, 2021 | 198 | 2021 |
| Jailbreak and guard aligned language models with only few in-context demonstrations Z Wei, Y Wang, A Li, Y Mo, Y Wang arXiv preprint arXiv:2310.06387, 2023 | 178 | 2023 |
| Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation Q Meng, M Xiao, S Yan, Y Wang, Z Lin, ZQ Luo CVPR 2022, 2022 | 164 | 2022 |
| Improving adversarial robustness via channel-wise activation suppressing Y Bai, Y Zeng, Y Jiang, ST Xia, X Ma, Y Wang ICLR 2021, 2021 | 144 | 2021 |
| Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap Y Wang, Q Zhang, Y Wang, J Yang, Z Lin ICLR 2022, 2022 | 140 | 2022 |
| Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks H Huang, Y Wang, S Erfani, Q Gu, J Bailey, X Ma NeurIPS 2021, 2021 | 116 | 2021 |
| A novel consistent random forest framework: Bernoulli random forests Y Wang, ST Xia, Q Tang, J Wu, X Zhu IEEE transactions on neural networks and learning systems 29 (8), 3510-3523, 2017 | 115* | 2017 |
| Leveraged Weighted Loss for Partial Label Learning H Wen, J Cui, H Hang, J Liu, Y Wang, Z Lin ICML 2021, 2021 | 112 | 2021 |
Xingjun MaSchool of Computer Science, Fudan University在 unimelb.edu.au 的电子邮件经过验证
