王奕森查看源代码讨论查看历史
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王奕森，男，北京大学智能学院教授。
教育背景
2018年博士毕业于清华大学计算机系
研究领域
学术成果
论著
1.Dongxian Wu, Shu-Tao Xia,Yisen Wang#; “Adversarial Weight Perturbation Helps Robust Generalization”, Neural Information Processing Systems (NeurIPS 2020)
2.Yisen Wang*, Xingjun Ma*, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu; “On the Convergence and Robustness of Adversarial Training”, International Conference on Machine Learning (ICML 2019)
3.Yisen Wang*, Difan Zou*, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu; “Improving Adversarial Robustness Requires Revisiting Misclassified Examples”, International Conference on Learning Representations (ICLR 2020)
4.Dongxian Wu,Yisen Wang#, Shu-Tao Xia, James Bailey, Xingjun Ma; “Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets”, International Conference on Learning Representations (ICLR 2020)
5.Xingjun Ma*, Hanxun Huang*,Yisen Wang#, Simone Romano, Sarah Erfani, James Bailey; “Normalized Loss Functions for Deep Learning with Noisy Labels”, International Conference on Machine Learning (ICML 2020)
6.Yisen Wang*, Xingjun Ma*, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey; “Symmetric Cross Entropy for Robust Learning with Noisy Labels”, International Conference on Computer Vision (ICCV 2019)
7.Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia; “Iterative Learning with Open-set Noisy Labels”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)[1]