| 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 | 647 | 2020 |
| Robust Training under Label Noise by Over-parameterization S Liu, Z Zhu, Q Qu, C You ICML 2022, 2022 | 131 | 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 | 123 | 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 | 107 | 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 | 92 | 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 | 74 | 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 | 67 | 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 | 63 | 2024 |
| 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 | 58 | 2022 |
| 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 | 52 | 2024 |
| 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 | 39 | 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 | 33 | 2023 |
| Swin MAE: Masked autoencoders for small datasets Y Dai, F Liu, W Chen, Y Liu, L Shi, S Liu, Y Zhou | 30 | 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 | 30 | 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 | 28 | 2021 |
| Avoiding spurious correlations via logit correction S Liu, X Zhang, N Sekhar, Y Wu, P Singhal, C Fernandez-Granda ICLR 2023, 2022 | 22 | 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 | 18 | 2021 |
| Deep probability estimation S Liu, A Kaku, W Zhu, M Leibovich, S Mohan, B Yu, L Zanna, N Razavian, ... ICML 2022, 2021 | 17 | 2021 |
| Adaptation and learning to learn (all): An integrated approach for small-sample parking occupancy prediction H Qu, S Liu, J Li, Y Zhou, R Liu Mathematics 10 (12), 2039, 2022 | 15 | 2022 |
| Adversarial multiscale feature learning framework for overlapping chromosome segmentation L Mei, Y Yu, H Shen, Y Weng, Y Liu, D Wang, S Liu, F Zhou, C Lei Entropy 24 (4), 522, 2022 | 15 | 2022 |
Carlos Fernandez-GrandaCourant Institute and Center for Data Science, New York UniversityVerified email at cims.nyu.edu