Yifei Wang 个人学术档案 - 学术资源搜索

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	总计	2019 年至今
引用	574	574
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i10 指数	13	13

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Yisen WangAssistant Professor, Peking University在 pku.edu.cn 的电子邮件经过验证
Zhouchen LinProfessor, Peking University; Fellow of IEEE, IAPR, CSIG & AAIA; ex-VP of Samsung Research在 pku.edu.cn 的电子邮件经过验证
Zeming WeiUndergraduate, Peking University在 stu.pku.edu.cn 的电子邮件经过验证
Tianqi DuPhD Student, Peking University在 pku.edu.cn 的电子邮件经过验证
Qi ChenPeking University在 pku.edu.cn 的电子邮件经过验证
Zhengyang GengPhD student, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
Mingjie LiPostdoc Researcher, CISPA在 pku.edu.cn 的电子邮件经过验证
Dongxian WuBytedance在 bytedance.com 的电子邮件经过验证
Yichuan MoPh.D. Candidate, Peking University在 stu.pku.edu.cn 的电子邮件经过验证
Weiran HuangAssociate Professor, Shanghai Jiao Tong University在 sjtu.edu.cn 的电子邮件经过验证
Jiangyan MaPeking University在 stu.pku.edu.cn 的电子邮件经过验证
Rundong LuoUndergraduate Student, Peking University在 stu.pku.edu.cn 的电子邮件经过验证
Qixun WangPh.D. Student, Peking University在 pku.edu.cn 的电子邮件经过验证
Stefanie JegelkaTUM and MIT在 mit.edu 的电子邮件经过验证
Chawin SitawarinPostdoctoral Researcher @ Meta在 meta.com 的电子邮件经过验证
Lin LiPh.D. student, King's College London在 kcl.ac.uk 的电子邮件经过验证
Michael SpratlingUniversity of Luxembourg and King's College London在 uni.lu 的电子邮件经过验证
Jingtong SuPh.D. Student, New York University在 nyu.edu 的电子邮件经过验证
Derek LimMIT在 mit.edu 的电子邮件经过验证
Chenyu WangMIT在 mit.edu 的电子邮件经过验证

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Yifei Wang

其他姓名王一飞

MIT

在 mit.edu 的电子邮件经过验证 - 首页

Machine Learning Self-supervised Learning Adversarial Learning Graph Neural Networks


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	105	2022
Dissecting the diffusion process in linear graph convolutional networks Y Wang, Y Wang, J Yang, Z Lin NeurIPS 2021, 2021	67	2021
Jailbreak and guard aligned language models with only few in-context demonstrations Z Wei, Y Wang, Y Wang arXiv preprint arXiv:2310.06387, 2023	59	2023
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders Q Zhang, Y Wang, Y Wang NeurIPS 2022 Spotlight, 2022	39	2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture Y Mo, D Wu, Y Wang, Y Guo, Y Wang NeurIPS 2022 Spotlight, 2022	38	2022
Optimization-induced graph implicit nonlinear diffusion Q Chen, Y Wang, Y Wang, J Yang, Z Lin ICML 2022, 2022	30	2022
Residual relaxation for multi-view representation learning Y Wang, Z Geng, F Jiang, C Li, Y Wang, J Yang, Z Lin NeurIPS 2021, 2021	30	2021
CFA: Class-wise Calibrated Fair Adversarial Training Z Wei, Y Wang, Y Guo, Y Wang CVPR 2023, 2023	27	2023
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond X Guo, Y Wang, T Du*, Y Wang ICLR 2023, 2023	25	2023
G CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters M Li, X Guo, Y Wang, Y Wang, Z Lin ICML 2022, 2022	20	2022
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism Z Zhuo, Y Wang, J Ma, Y Wang ICLR 2023, 2023	15	2023
A Message Passing Perspective on Learning Dynamics of Contrastive Learning Y Wang, Q Zhang, T Du, J Yang, Z Lin, Y Wang ICLR 2023, 2023	11	2023
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning R Luo, Y Wang, Y Wang ICLR 2023, 2023	10	2023
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors Q Wang, Y Wang, H Zhu, Y Wang NeurIPS 2022 Spotlight, 2022	9	2022
Fooling Adversarial Training with Inducing Noise Z Wang, Y Wang, Y Wang arXiv preprint arXiv:2111.10130, 2021	9	2021
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective Y Wang, L Li, J Yang, Z Lin, Y Wang NeurIPS 2023, 2023	8	2023
Rethinking Weak Supervision in Helping Contrastive Learning J Cui, W Huang, Y Wang*, Y Wang ICML 2023, 2023	8	2023
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training Y Wang, Y Wang, J Yang, Z Lin ICLR 2022, 2022	8	2022
On the Generalization of Multi-modal Contrastive Learning Q Zhang, Y Wang, Y Wang ICML 2023, 2023	7	2023
Train once, and decode as you like C Tian, Y Wang, H Cheng, Y Lian, Z Zhang COLING 2020, 2020	7	2020

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