Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Kim， Sangmook"'
Autor:
Yang, Yongjin, Kim, Sihyeon, Jung, Hojung, Bae, Sangmin, Kim, SangMook, Yun, Se-Young, Lee, Kimin
Fine-tuning text-to-image diffusion models with human feedback is an effective method for aligning model behavior with human intentions. However, this alignment process often suffers from slow convergence due to the large size and noise present in hu
Externí odkaz:
http://arxiv.org/abs/2410.10166
Autor:
Lee, Gihun, Jeong, Minchan, Kim, Yujin, Jung, Hojung, Oh, Jaehoon, Kim, Sangmook, Yun, Se-Young
While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents further challenges in preserving previous knowledge. This paper examines the
Externí odkaz:
http://arxiv.org/abs/2407.00693
Incorporating unanswerable questions into EHR QA systems is crucial for testing the trustworthiness of a system, as providing non-existent responses can mislead doctors in their diagnoses. The EHRSQL dataset stands out as a promising benchmark becaus
Externí odkaz:
http://arxiv.org/abs/2405.01588
Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradation when clients have heterogene
Externí odkaz:
http://arxiv.org/abs/2308.12532
Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various a
Externí odkaz:
http://arxiv.org/abs/2303.12317
Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However, many cell segmentation algorithms, including the recently emerging d
Externí odkaz:
http://arxiv.org/abs/2212.03465
Autor:
Kim, Seung Hyun1 (AUTHOR), Kim, Sangmook1 (AUTHOR) smook@seoultech.ac.kr
Publikováno v:
Public Performance & Management Review. Nov2024, Vol. 47 Issue 6, p1356-1375. 20p.
Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels. However, it is far more complex and challenging owing to varying levels of data hete
Externí odkaz:
http://arxiv.org/abs/2205.01310
Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simulta
Externí odkaz:
http://arxiv.org/abs/2106.06042
Autor:
Woo, Harin1 (AUTHOR) harinwoo@gmail.com, Kim, Sangmook2 (AUTHOR) smook@seoultech.ac.kr
Publikováno v:
Public Administration Review. Sep2024, Vol. 84 Issue 5, p966-981. 16p.