Saebyeol Shin — accepted AI-conference papers

2025

Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond

Qizhou Wang, Jin Peng Zhou, Zhanke Zhou, Saebyeol Shin, Bo Han, Kilian Q Weinberger

ICLR 2025poster

Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are crucial to remove undesirable responses, ensuring legal and safe model usage. It has spurred recent research into LLM unle…

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2021

VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection

Jaeju An, Jeongho Kim, Hanbeen Lee, Jinbeom Kim, Junhyung Kang, Minha Kim +3

NeurIPS 2021poster

Detection of fallen persons due to, for example, health problems, violence, or accidents, is a critical challenge. Accordingly, detection of these anomalous events is of paramount importance for a number of applications, including but not limited to CCTV surveillance, security, and health care. Give…

Cited by 20 Source ↗Scholar ↗