| When are deep networks really better than decision forests at small sample sizes, and how? H Xu, KA Kinfu, W LeVine, S Panda, J Dey, M Ainsworth, YC Peng, ... arXiv preprint arXiv:2108.13637, 2021 | 24 | 2021 |
| A general approach to progressive learning JT Vogelstein, J Dey, HS Helm, W LeVine, RD Mehta, W Yang, B Tower, ... Preprint at https://arxiv. org/abs/2004.12908, 2020 | 12* | 2020 |
| Enabling Calibration In The Zero-Shot Inference of Large Vision-Language Models W LeVine, B Pikus, P Raj, FA Gil ICLR 2023 Workshop on Trustworthy ML, 2023 | 10 | 2023 |
| Accurate layerwise interpretable competence estimation V Rajendran, W LeVine 33rd Conference on Neural Information Processing Systems, 2019 | 7 | 2019 |
| A Baseline Analysis of Reward Models' Ability To Accurately Analyze Foundation Models Under Distribution Shift W LeVine, B Pikus, T Chen, S Hendryx AAAI Workshop on Responsible Language Models, 2023 | 2* | 2023 |
| Out-of-distribution detection using kernel density polytopes J Dey, A De Silva, W LeVine, J Shin, H Xu, A Geisa, T Chu, L Isik, ... arXiv preprint arXiv:2201.13001, 2022 | 2 | 2022 |
| Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy W LeVine, B Pikus, J Phillips, B Norman, FA Gil, S Hendryx arXiv preprint arXiv:2401.12129, 2024 | | 2024 |
Joshua T. VogelsteinDept of Biomedical Engineering, Johns Hopkins University在 jhu.edu 的电子邮件经过验证
