An unconstrained layer-peeled perspective on neural collapse W Ji, Y Lu, Y Zhang, Z Deng, WJ Su arXiv preprint arXiv:2110.02796, 2021 | 63 | 2021 |
The power of contrast for feature learning: A theoretical analysis W Ji, Z Deng, R Nakada, J Zou, L Zhang Journal of Machine Learning Research 24 (330), 1-78, 2023 | 31 | 2023 |
Understanding multimodal contrastive learning and incorporating unpaired data R Nakada, HI Gulluk, Z Deng, W Ji, J Zou, L Zhang International Conference on Artificial Intelligence and Statistics, 4348-4380, 2023 | 19 | 2023 |
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 | 10 | 2024 |
Importance tempering: Group robustness for overparameterized models Y Lu, W Ji, Z Izzo, L Ying arXiv preprint arXiv:2209.08745, 2022 | 5 | 2022 |
Model-agnostic covariate-assisted inference on partially identified causal effects W Ji, L Lei, A Spector arXiv preprint arXiv:2310.08115, 2023 | 2 | 2023 |
Scaling Laws for the Value of Individual Data Points in Machine Learning I Covert, W Ji, T Hashimoto, J Zou arXiv preprint arXiv:2405.20456, 2024 | | 2024 |
How Gradient Descent Separates Data with Neural Collapse: A Layer-Peeled Perspective W Ji, Y Lu, Y Zhang, Z Deng, WJ Su | | |