no code implementations • 18 Mar 2024 • Armin Karamzade, KyungMin Kim, Montek Kalsi, Roy Fox
In standard Reinforcement Learning settings, agents typically assume immediate feedback about the effects of their actions after taking them.
no code implementations • 19 Oct 2023 • Jenny S Kim, KyungMin Kim, Richard Van Weelden
We consider the classic veto bargaining model but allow the agenda setter to engage in persuasion to convince the veto player to approve her proposal.
no code implementations • 21 Jul 2023 • Kolby Nottingham, Yasaman Razeghi, KyungMin Kim, JB Lanier, Pierre Baldi, Roy Fox, Sameer Singh
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities.
1 code implementation • 2 Dec 2022 • Jinyoung Park, Hyeong Kyu Choi, Juyeon Ko, Hyeonjin Park, Ji-Hoon Kim, Jisu Jeong, KyungMin Kim, Hyunwoo J. Kim
To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner.
no code implementations • 26 Oct 2022 • Jiwoong Park, Jisu Jeong, KyungMin Kim, Jin Young Choi
To tackle this challenge, we propose a novel concept of meta-node for message passing that can learn enriched relational knowledge from complex heterogeneous graphs without any meta-paths and meta-graphs by explicitly modeling the relations among the same type of nodes.
no code implementations • 29 Sep 2021 • Chaojian Li, KyungMin Kim, Bichen Wu, Peizhao Zhang, Hang Zhang, Xiaoliang Dai, Peter Vajda, Yingyan Lin
In particular, when transferred to PiT, our scaling strategies lead to a boosted ImageNet top-1 accuracy of from $74. 6\%$ to $76. 7\%$ ($\uparrow2. 1\%$) under the same 0. 7G FLOPs; and when transferred to the COCO object detection task, the average precision is boosted by $\uparrow0. 7\%$ under a similar throughput on a V100 GPU.
1 code implementation • 22 Jun 2021 • Hyolim Kang, Jinwoo Kim, KyungMin Kim, Taehyun Kim, Seon Joo Kim
Generic Event Boundary Detection (GEBD) is a newly introduced task that aims to detect "general" event boundaries that correspond to natural human perception.
no code implementations • ICCV 2021 • Hyolim Kang, KyungMin Kim, Yumin Ko, Seon Joo Kim
Temporal action localization has been one of the most popular tasks in video understanding, due to the importance of detecting action instances in videos.
no code implementations • 16 Mar 2020 • Yeon-Koo Che, KyungMin Kim, Konrad Mierendorff
We consider a dynamic model of Bayesian persuasion in which information takes time and is costly for the sender to generate and for the receiver to process, and neither player can commit to their future actions.