| Early-learning regularization prevents memorization of noisy labels S Liu, J Niles-Weed, N Razavian, C Fernandez-Granda 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 623 | 2020 |
| Robust Training under Label Noise by Over-parameterization S Liu, Z Zhu, Q Qu, C You ICML 2022, 2022 | 125 | 2022 |
| Adaptive early-learning correction for segmentation from noisy annotations S Liu, K Liu, W Zhu, Y Shen, C Fernandez-Granda CVPR 2022 (Oral), 2606-2616, 2022 | 114 | 2022 |
| Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs S Liu, AV Masurkar, H Rusinek, J Chen, B Zhang, W Zhu, ... Scientific reports 12 (1), 17106, 2022 | 90 | 2022 |
| Monitoring ai-modified content at scale: A case study on the impact of chatgpt on ai conference peer reviews W Liang, Z Izzo, Y Zhang, H Lepp, H Cao, X Zhao, L Chen, H Ye, S Liu, ... arXiv preprint arXiv:2403.07183, 2024 | 73 | 2024 |
| On the design of convolutional neural networks for automatic detection of Alzheimer’s disease S Liu, C Yadav, C Fernandez-Granda, N Razavian 2019 NeurIPS, 184-201, 2020 | 73 | 2020 |
| On Learning Contrastive Representations for Learning with Noisy Labels L Yi, S Liu, Q She, AI McLeod, B Wang CVPR 2022, 16682-16691, 2022 | 64 | 2022 |
| Are all losses created equal: A neural collapse perspective J Zhou, C You, X Li, K Liu, S Liu, Q Qu, Z Zhu Advances in Neural Information Processing Systems 35, 31697-31710, 2022 | 56 | 2022 |
| Mapping the increasing use of llms in scientific papers W Liang, Y Zhang, Z Wu, H Lepp, W Ji, X Zhao, H Cao, S Liu, S He, ... arXiv preprint arXiv:2404.01268, 2024 | 46 | 2024 |
| In-context vectors: Making in context learning more effective and controllable through latent space steering S Liu, H Ye, L Xing, J Zou Forty-first International Conference on Machine Learning., 2024 | 44 | 2024 |
| Multiple instance learning via iterative self-paced supervised contrastive learning K Liu, W Zhu, Y Shen, S Liu, N Razavian, KJ Geras, C Fernandez-Granda Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 29 | 2023 |
| Principled and efficient transfer learning of deep models via neural collapse X Li, S Liu, J Zhou, X Lu, C Fernandez-Granda, Z Zhu, Q Qu arXiv preprint arXiv:2212.12206, 2022 | 29 | 2022 |
| TextGrad: Automatic" Differentiation" via Text M Yuksekgonul*, F Bianchi*, J Boen*, S Liu*, Z Huang*, C Guestrin, J Zou arXiv preprint arXiv:2406.07496, 2024 | 27 | 2024 |
| Swin MAE: Masked autoencoders for small datasets Y Dai, F Liu, W Chen, Y Liu, L Shi, S Liu, Y Zhou | 27 | 2023 |
| Convolutional normalization: Improving deep convolutional network robustness and training S Liu, X Li, Y Zhai, C You, Z Zhu, C Fernandez-Granda, Q Qu 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 27 | 2021 |
| Avoiding spurious correlations via logit correction S Liu, X Zhang, N Sekhar, Y Wu, P Singhal, C Fernandez-Granda ICLR 2023, 2022 | 17 | 2022 |
| Deep probability estimation S Liu, A Kaku, W Zhu, M Leibovich, S Mohan, B Yu, L Zanna, N Razavian, ... ICML 2022, 2021 | 17 | 2021 |
| Few-shot fine-grained action recognition via bidirectional attention and contrastive meta-learning J Wang, Y Wang, S Liu, A Li Proceedings of the 29th ACM International Conference on Multimedia, 582-591, 2021 | 16 | 2021 |
| Sparse recovery beyond compressed sensing: Separable nonlinear inverse problems B Bernstein, S Liu, C Papadaniil, C Fernandez-Granda IEEE transactions on information theory 66 (9), 5904-5926, 2020 | 13 | 2020 |
| Paddles: Phase-amplitude spectrum disentangled early stopping for learning with noisy labels H Huang, H Kang, S Liu, O Salvado, T Rakotoarivelo, D Wang, T Liu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 12 | 2023 |
Carlos Fernandez-GrandaCourant Institute and Center for Data Science, New York University在 cims.nyu.edu 的电子邮件经过验证
