| Learning to propagate labels: Transductive propagation network for few-shot learning Y Liu, J Lee, M Park, S Kim, E Yang, SJ Hwang, Y Yang arXiv preprint arXiv:1805.10002, 2018 | 827 | 2018 |
| Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks HB Lee, H Lee, D Na, S Kim, M Park, E Yang, SJ Hwang arXiv preprint arXiv:1905.12917, 2019 | 118 | 2019 |
| A deep learning model for real-time mortality prediction in critically ill children SY Kim, S Kim, J Cho, YS Kim, IS Sol, Y Sung, I Cho, M Park, H Jang, ... Critical care 23, 1-10, 2019 | 98 | 2019 |
| Mxml: Mixture of meta-learners for few-shot classification M Park, J Kim, S Kim, Y Liu, S Choi arXiv preprint arXiv:1904.05658, 2019 | 8 | 2019 |
| Quadapter: Adapter for gpt-2 quantization M Park, J You, M Nagel, S Chang arXiv preprint arXiv:2211.16912, 2022 | 6 | 2022 |
| Classifying heart conditions based on class probability output networks HB Bae, MS Park, RM Kil, HY Youn Neurocomputing 360, 198-208, 2019 | 6 | 2019 |
| Method For Predicting Of Mortality Risk Or Sepsis Risk And Device For Predicting Of Mortality Risk Or Sepsis Risk Using The Same YS Kim, KS Chung, JK Yoo, YC Sung, IH Cho, SH Kim, MS Park US Patent App. 16/598,096, 2020 | 3 | 2020 |
| Taeml: Task-adaptive ensemble of meta-learners M Park, S Kim, J Kim, Y Liu, S Choi NeurIPS 2018 Workshop on Meta-learning, 2018 | 2 | 2018 |
