| 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 | 866 | 2020 |
| 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, ... ICML 2024 (Oral), 2024 | 260 | 2024 |
| In-context vectors: Making in context learning more effective and controllable through latent space steering S Liu, H Ye, L Xing, J Zou ICML 2024, 2024 | 214 | 2024 |
| 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 | 205 | 2022 |
| TextGrad: Automatic" Differentiation" via Text M Yuksekgonul*, F Bianchi*, J Boen*, S Liu*, Z Huang*, C Guestrin, J Zou Nature 2025, 2024 | 198 | 2024 |
| Robust Training under Label Noise by Over-parameterization S Liu, Z Zhu, Q Qu, C You ICML 2022, 2022 | 195 | 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, ... Conference On Language Modeling (COLM), 2024 | 192* | 2024 |
| 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 | 174 | 2022 |
| Optimizing generative AI by backpropagating language model feedback M Yuksekgonul*, F Bianchi*, J Boen*, S Liu*, P Lu*, Z Huang*, C Guestrin, ... Nature 639 (8055), 609-616, 2025 | 129 | 2025 |
| On Learning Contrastive Representations for Learning with Noisy Labels L Yi, S Liu, Q She, AI McLeod, B Wang CVPR 2022, 16682-16691, 2022 | 112 | 2022 |
| Medtrinity-25m: A large-scale multimodal dataset with multigranular annotations for medicine Y Xie, C Zhou, L Gao, J Wu, X Li, HY Zhou, S Liu, L Xing, J Zou, C Xie, ... ICLR 2025, 2024 | 95 | 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 | 89 | 2022 |
| On the design of convolutional neural networks for automatic detection of Alzheimer’s disease S Liu, C Yadav, C Fernandez-Granda, N Razavian Machine Learning for Healthcare Workshop NeurIPS 2019, 184-201, 2020 | 82 | 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 | 60 | 2023 |
| Reducing hallucinations in large vision-language models via latent space steering S Liu, H Ye, J Zou ICLR 2025 (spotlight), 2025 | 59* | 2025 |
| Tfg: Unified training-free guidance for diffusion models H Ye, H Lin, J Han, M Xu, S Liu, Y Liang, J Ma, JY Zou, S Ermon Advances in Neural Information Processing Systems 37, 22370-22417, 2024 | 59 | 2024 |
| OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning P Lu*, B Chen*, S Liu*, R Thapa, J Boen, J Zou arXiv preprint arXiv:2502.11271, 2025 | 56 | 2025 |
| An integrated wearable differential microneedle array for continuous glucose monitoring in interstitial fluids Y Yang, C Sheng, F Dong, S Liu Biosensors and Bioelectronics 256, 116280, 2024 | 50 | 2024 |
| Understanding and Improving Transfer Learning of Deep Models via Neural Collapse X Li*, S Liu*, J Zhou, X Lu, C Fernandez-Granda, Z Zhu, Q Qu Transactions on Machine Learning Research, 2024 | 49* | 2024 |
| Swin MAE: Masked autoencoders for small datasets Y Dai, F Liu, W Chen, Y Liu, L Shi, S Liu, Y Zhou | 49* | 2023 |
Carlos Fernandez-GrandaCourant Institute and Center for Data Science, New York UniversityVerified email at cims.nyu.edu
