| Learning to prompt for continual learning Z Wang, Z Zhang, CY Lee, H Zhang, R Sun, X Ren, G Su, V Perot, J Dy, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 620 | 2022 |
| Dualprompt: Complementary prompting for rehearsal-free continual learning Z Wang, Z Zhang, S Ebrahimi, R Sun, H Zhang, CY Lee, X Ren, G Su, ... European Conference on Computer Vision, 631-648, 2022 | 371 | 2022 |
| NeuroPAL: a multicolor atlas for whole-brain neuronal identification in C. elegans E Yemini, A Lin, A Nejatbakhsh, E Varol, R Sun, GE Mena, ADT Samuel, ... Cell 184 (1), 272-288. e11, 2021 | 225* | 2021 |
| Kohn-Sham equations as regularizer: Building prior knowledge into machine-learned physics L Li, S Hoyer, R Pederson, R Sun, ED Cubuk, P Riley, K Burke Physical review letters 126 (3), 036401, 2021 | 170 | 2021 |
| Towards understanding retrosynthesis by energy-based models R Sun, H Dai, L Li, S Kearnes, B Dai Advances in Neural Information Processing Systems 34, 10186-10194, 2021 | 62* | 2021 |
| SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) R Sun, SÖ Arik, A Muzio, L Miculicich, S Gundabathula, P Yin, H Dai, ... arXiv preprint arXiv:2306.00739, 2023 | 60 | 2023 |
| Does gnn pretraining help molecular representation? R Sun, H Dai, AW Yu Advances in Neural Information Processing Systems 35, 12096-12109, 2022 | 59 | 2022 |
| Capabilities of gemini models in medicine K Saab, T Tu, WH Weng, R Tanno, D Stutz, E Wulczyn, F Zhang, ... arXiv preprint arXiv:2404.18416, 2024 | 57 | 2024 |
| Using a thousand optimization tasks to learn hyperparameter search strategies L Metz, N Maheswaranathan, R Sun, CD Freeman, B Poole, ... arXiv preprint arXiv:2002.11887, 2020 | 38 | 2020 |
| Better zero-shot reasoning with self-adaptive prompting X Wan, R Sun, H Dai, SO Arik, T Pfister arXiv preprint arXiv:2305.14106, 2023 | 32 | 2023 |
| Combination use of ultrasound irradiation and ionic liquid in enzymatic isomerization of glucose to fructose Y Wang, Y Pan, Z Zhang, R Sun, X Fang, D Yu Process Biochemistry 47 (6), 976-982, 2012 | 27 | 2012 |
| Reverse engineering learned optimizers reveals known and novel mechanisms N Maheswaranathan, D Sussillo, L Metz, R Sun, J Sohl-Dickstein Advances in Neural Information Processing Systems 34, 19910-19922, 2021 | 22 | 2021 |
| Statistical Atlas of C. elegans Neurons E Varol, A Nejatbakhsh, R Sun, G Mena, E Yemini, O Hobert, L Paninski Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 18 | 2020 |
| Universal self-adaptive prompting X Wan, R Sun, H Nakhost, H Dai, JM Eisenschlos, SO Arik, T Pfister arXiv preprint arXiv:2305.14926, 2023 | 13 | 2023 |
| Scalable approximate Bayesian inference for particle tracking data R Sun, L Paninski International Conference on Machine Learning, ICML 2018, 276253, 2018 | 12 | 2018 |
| Ultrasound-promoted Lipase-catalyzed Enantioselective Transesterification of (R,S)-Glycidol B An, X Xie, E Xun, J Wang, R Sun, L Wang*, Z Wang* Chemical Research in Chinese universities 27 ((5)), 845-849, 2011 | 9 | 2011 |
| Effective Large Language Model Adaptation for Improved Grounding and Citation Generation X Ye, R Sun, S Arik, T Pfister Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 7 | 2024 |
| Effective large language model adaptation for improved grounding X Ye, R Sun, SÖ Arik, T Pfister arXiv preprint arXiv:2311.09533, 2023 | 7 | 2023 |
| Scalable variational inference for super resolution microscopy R Sun, E Archer, L Paninski International Conference on Artificial Intelligence and Statistics (AISTATS …, 2016 | 6 | 2016 |
| Sqlprompt: In-context text-to-sql with minimal labeled data R Sun, SÖ Arik, R Sinha, H Nakhost, H Dai, P Yin, T Pfister arXiv preprint arXiv:2311.02883, 2023 | 5 | 2023 |
