| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 503 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 11 | 2024 |
| Batch calibration: Rethinking calibration for in-context learning and prompt engineering H Zhou, X Wan, L Proleev, D Mincu, J Chen, K Heller, S Roy arXiv preprint arXiv:2309.17249, 2023 | 11 | 2023 |
| Disability prediction in multiple sclerosis using performance outcome measures and demographic data S Roy, D Mincu, L Proleev, N Rostamzadeh, C Ghate, N Harris, C Chen, ... Conference on Health, Inference, and Learning, 375-396, 2022 | 5 | 2022 |
| Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression A Norcliffe, L Proleev, D Mincu, FL Hartsell, K Heller, S Roy arXiv preprint arXiv:2302.07854, 2023 | 1 | 2023 |
| Holistic Safety and Responsibility Evaluations of Advanced AI Models L Weidinger, J Barnhart, J Brennan, C Butterfield, S Young, W Hawkins, ... arXiv preprint arXiv:2404.14068, 2024 | | 2024 |
| Performance of Machine Learning Models for Predicting High-Severity Symptoms in Multiple Sclerosis S Roy, D Mincu, L Proleev, C Ghate, J Graves, D Steiner, F Hartsell, ... | | 2023 |
| Longitudinal Modeling of Multiple Sclerosis using Continuous Time Models A Norcliffe, L Proleev, D Mincu, FL Hartsell, K Heller, S Roy arXiv e-prints, arXiv: 2302.07854, 2023 | | 2023 |
