Fine-tuning can distort pretrained features and underperform out-of-distribution A Kumar, A Raghunathan, R Jones, T Ma, P Liang arXiv preprint arXiv:2202.10054, 2022 | 461 | 2022 |
Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation K Shen, RM Jones, A Kumar, SM Xie, JZ HaoChen, T Ma, P Liang International conference on machine learning, 19847-19878, 2022 | 79 | 2022 |
In-n-out: Pre-training and self-training using auxiliary information for out-of-distribution robustness SM Xie, A Kumar, R Jones, F Khani, T Ma, P Liang arXiv preprint arXiv:2012.04550, 2020 | 51 | 2020 |
Featureless Deep Learning Methods for Automated Key-Term Extraction K Khosla, R Jones, N Bowman Stanford University, 2019 | 4 | 2019 |
How does Contrastive Pre-training Connect Disparate Domains? K Shen, RM Jones, A Kumar, SM Xie, P Liang | 3 | 2021 |
Stack overflow query outcome prediction R Jones, D Lin | 2 | 2016 |
Temporal Link Prediction on the WikiLinkGraphs Dataset N Bowman, R Jones, S Shafi | 1 | |
Extracting Kinematic Information Using Pose Estimation R Jones | | |