| Understanding and improving early stopping for learning with noisy labels Y Bai, E Yang, B Han, Y Yang, J Li, Y Mao, G Niu, T Liu Advances in Neural Information Processing Systems 34, 24392-24403, 2021 | 157 | 2021 |
| Me-momentum: Extracting hard confident examples from noisily labeled data Y Bai, T Liu Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 36 | 2021 |
| Biomedical image analysis competitions: The state of current participation practice M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, ... arXiv preprint arXiv:2212.08568, 2022 | 23 | 2022 |
| RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning Y Bai, E Yang, Z Wang, Y Du, B Han, C Deng, D Wang, T Liu Advances in Neural Information Processing Systems 35, 21128-21141, 2022 | 12* | 2022 |
| Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping Y Bai, Z Han, E Yang, J Yu, B Han, D Wang, T Liu Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Robust Representation Learning: Understanding the Role of Early Stopping amidst Noisy Labels Y Bai | | 2024 |
Tongliang LiuDirector, Sydney AI Centre, University of Sydney & Mohamed bin Zayed University of AI確認したメール アドレス: sydney.edu.au
