| 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 | 389 | 2020 |
| 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 | 65 | 2020 |
| 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 | 57 | 2022 |
| Robust Training under Label Noise by Over-parameterization S Liu, Z Zhu, Q Qu, C You ICML 2022, 2022 | 55 | 2022 |
| On Learning Contrastive Representations for Learning with Noisy Labels L Yi, S Liu, Q She, AI McLeod, B Wang CVPR 2022, 16682-16691, 2022 | 30 | 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 | 24 | 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 | 24 | 2022 |
| 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 | 18 | 2021 |
| Deep probability estimation S Liu, A Kaku, W Zhu, M Leibovich, S Mohan, B Yu, L Zanna, N Razavian, ... ICML 2022, 2021 | 13 | 2021 |
| 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 | 11 | 2022 |
| 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 | 10 | 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 | 10 | 2020 |
| 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 | 6 | 2023 |
| 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 | 5 | 2023 |
| Avoiding spurious correlations via logit correction S Liu, X Zhang, N Sekhar, Y Wu, P Singhal, C Fernandez-Granda arXiv preprint arXiv:2212.01433, 2022 | 3 | 2022 |
| Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data S Liu, M Cheng, H Brooks, W Mackey, DJ Heeger, EG Tabak, ... 2019 NeurIPS Machine Learning for Health Workshop, 2019 | 3 | 2019 |
| Unleashing the potential of regularization strategies in learning with noisy labels H Kang, S Liu, H Huang, J Yu, B Han, D Wang, T Liu arXiv preprint arXiv:2307.05025, 2023 | 2 | 2023 |
| Development of a Deep Learning Model for Early Alzheimer’s Disease Detection from Structural MRIs and External Validation on an Independent Cohort S Liu, AV Masurkar, H Rusinek, J Chen, B Zhang, W Zhu, ... | 2 | 2021 |
| Lower Bounds for Mutual Information in Representation Learning S Liu | 1 | 2020 |
| Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling W Zhu, S Liu, C Fernandez-Granda, N Razavian arXiv preprint arXiv:2311.16361, 2023 | | 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
