no code implementations • 12 Mar 2024 • Seungjae Shin, HeeSun Bae, Byeonghu Na, Yoon-Yeong Kim, Il-Chul Moon
In particular, by aligning the loss landscape acquired in the source domain to the loss landscape of perturbed domains, we expect to achieve generalization grounded on these flat minima for the unknown domains.
1 code implementation • Proceedings of the 40th International Conference on Machine Learning 2023 • Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon
Specifically, our proposed method, Sharpness-Aware Active Learning (SAAL), constructs its acquisition function by selecting unlabeled instances whose perturbed loss becomes maximum.
no code implementations • NeurIPS 2021 • Yoon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-Chul Moon
Active learning effectively collects data instances for training deep learning models when the labeled dataset is limited and the annotation cost is high.
no code implementations • 15 Nov 2019 • Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoon-Yeong Kim, Il-Chul Moon
This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics.