| Cuda: Curriculum of data augmentation for long-tailed recognition S Ahn, J Ko, SY Yun arXiv preprint arXiv:2302.05499, 2023 | 51 | 2023 |
| Mitigating dataset bias by using per-sample gradient S Ahn, S Kim, SY Yun arXiv preprint arXiv:2205.15704, 2022 | 22 | 2022 |
| Multi-armed bandit with additional observations D Yun, A Proutiere, S Ahn, J Shin, Y Yi Proceedings of the ACM on Measurement and Analysis of Computing Systems 2 (1 …, 2018 | 21 | 2018 |
| Neuro-DCF: Design of wireless MAC via multi-agent reinforcement learning approach S Moon, S Ahn, K Son, J Park, Y Yi Proceedings of the Twenty-second International Symposium on Theory …, 2021 | 16 | 2021 |
| Comparison of prompt engineering and fine-tuning strategies in large language models in the classification of clinical notes X Zhang, N Talukdar, S Vemulapalli, S Ahn, J Wang, H Meng, ... AMIA Summits on Translational Science Proceedings 2024, 478, 2024 | 15 | 2024 |
| Active prompt learning in vision language models J Bang, S Ahn, JG Lee Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 7 | 2024 |
| Fine tuning pre trained models for robustness under noisy labels S Ahn, S Kim, J Ko, SY Yun arXiv preprint arXiv:2310.17668, 2023 | 7 | 2023 |
| Vacode: Visual augmented contrastive decoding S Kim, B Cho, S Bae, S Ahn, SY Yun arXiv preprint arXiv:2408.05337, 2024 | 5 | 2024 |
| NASH: A simple unified framework of structured pruning for accelerating encoder-decoder language models J Ko, S Park, Y Kim, S Ahn, DS Chang, E Ahn, SY Yun arXiv preprint arXiv:2310.10054, 2023 | 5 | 2023 |
| Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning X Zhang, S Vemulapalli, N Talukdar, S Ahn, J Wang, H Meng, ... arXiv preprint arXiv:2312.14184, 2023 | 4 | 2023 |
| Enlarging discriminative power by adding an extra class in unsupervised domain adaptation HH Tran, S Ahn, T Lee, Y Yi 2020 25th International Conference on Pattern Recognition (ICPR), 1812-1819, 2021 | 4 | 2021 |
| Denoising after entropy-based debiasing a robust training method for dataset bias with noisy labels S Ahn, SY Yun Proceedings of the AAAI Conference on Artificial Intelligence 37 (1), 169-177, 2023 | 3 | 2023 |
| Efficient utilization of pre-trained model for learning with noisy labels J Ko, S Ahn, SY Yun ICLR 2023 Workshop on Pitfalls of limited data and computation for …, 2023 | 3 | 2023 |
| FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning S Kim, M Jeong, S Kim, S Cho, S Ahn, SY Yun arXiv preprint arXiv:2406.02355, 2024 | 1 | 2024 |
| Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models J Wang, S Ahn, T Dalal, X Zhang, W Pan, Q Zhang, B Chen, HH Dodge, ... arXiv preprint arXiv:2405.16413, 2024 | 1 | 2024 |
| Distributed In-Context Learning under Non-IID Among Clients S Liang, S Ahn, J Zhou arXiv preprint arXiv:2408.00144, 2024 | | 2024 |
| Client Sampling Algorithm in Federated Learning via Combinatorial Averaging and Multi-Armed Bandits S Bae, T Kim, S Ahn, S Kim, J Ko, SY Yun 한국정보과학회 학술발표논문집, 1088-1090, 2022 | | 2022 |
| Robust Prompt Learning For Vision-Language Models With Noisy Labels S Ahn, S Liang, J Zhou | | |
| ConDS: Context Distribution Shift for Robust In-Context Learning S Yu, S Ahn, S Liang, B Hou, J Ji, S Chang, J Zhou | | |
| ORBIS: Open Dataset Can Rescue You From Dataset Bias S Ahn, SY Yun | | |