Zobrazeno 1 - 10
of 2 181
pro vyhledávání: '"Kim, SungHwan"'
Autor:
Chae, Hyungjoo, Kim, Namyoung, Ong, Kai Tzu-iunn, Gwak, Minju, Song, Gwanwoo, Kim, Jihoon, Kim, Sunghwan, Lee, Dongha, Yeo, Jinyoung
Large language models (LLMs) have recently gained much attention in building autonomous agents. However, the performance of current LLM-based web agents in long-horizon tasks is far from optimal, often yielding errors such as repeatedly buying a non-
Externí odkaz:
http://arxiv.org/abs/2410.13232
Autor:
Kim, Sunghwan, Kang, Dongjin, Kwon, Taeyoon, Chae, Hyungjoo, Won, Jungsoo, Lee, Dongha, Yeo, Jinyoung
Reward models are key in reinforcement learning from human feedback (RLHF) systems, aligning the model behavior with human preferences. Particularly in the math domain, there have been plenty of studies using reward models to align policies for impro
Externí odkaz:
http://arxiv.org/abs/2410.01729
Autor:
Kim, Dasom, Hou, Jin, Lee, Geon, Agrawal, Ayush, Kim, Sunghwan, Zhang, Hao, Bao, Di, Baydin, Andrey, Wu, Wenjing, Tay, Fuyang, Huang, Shengxi, Chia, Elbert E. M., Kim, Dai-Sik, Seo, Minah, Mohite, Aditya D., Hagenmüller, David, Kono, Junichiro
Phonons, or vibrational quanta, are behind some of the most fundamental physical phenomena in solids, including superconductivity, Raman processes, and broken-symmetry phases. It is therefore of fundamental importance to find ways to harness phonons
Externí odkaz:
http://arxiv.org/abs/2409.04505
Offline multi-agent reinforcement learning (MARL) is increasingly recognized as crucial for effectively deploying RL algorithms in environments where real-time interaction is impractical, risky, or costly. In the offline setting, learning from a stat
Externí odkaz:
http://arxiv.org/abs/2408.13092
In this paper, we introduce a method to tackle Domain Generalized Semantic Segmentation (DGSS) by utilizing domain-invariant semantic knowledge from text embeddings of vision-language models. We employ the text embeddings as object queries within a t
Externí odkaz:
http://arxiv.org/abs/2407.09033
Autor:
Lee, Suyeon, Kim, Sunghwan, Kim, Minju, Kang, Dongjin, Yang, Dongil, Kim, Harim, Kang, Minseok, Jung, Dayi, Kim, Min Hee, Lee, Seungbeen, Chung, Kyoung-Mee, Yu, Youngjae, Lee, Dongha, Yeo, Jinyoung
Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. This surge has accelerated efforts to improve the accessibility of counseling by using large language models
Externí odkaz:
http://arxiv.org/abs/2407.03103
Next-generation wireless networks are projected to empower a broad range of Internet-of-things (IoT) applications and services with extreme data rates, posing new challenges in delivering large-scale connectivity at a low cost to current communicatio
Externí odkaz:
http://arxiv.org/abs/2406.01921
Autor:
Vu, Thai-Hoc, Jagatheesaperumal, Senthil Kumar, Nguyen, Minh-Duong, Van Huynh, Nguyen, Kim, Sunghwan, Pham, Quoc-Viet
The success of Artificial Intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet-of-Things (IoT) era. Nevertheless, most
Externí odkaz:
http://arxiv.org/abs/2405.20024
Autor:
Kang, Dongjin, Kim, Sunghwan, Kwon, Taeyoon, Moon, Seungjun, Cho, Hyunsouk, Yu, Youngjae, Lee, Dongha, Yeo, Jinyoung
Emotional Support Conversation (ESC) is a task aimed at alleviating individuals' emotional distress through daily conversation. Given its inherent complexity and non-intuitive nature, ESConv dataset incorporates support strategies to facilitate the g
Externí odkaz:
http://arxiv.org/abs/2402.13211
Autor:
Kim, Sekeun, Kim, Kyungsang, Hu, Jiang, Chen, Cheng, Lyu, Zhiliang, Hui, Ren, Kim, Sunghwan, Liu, Zhengliang, Zhong, Aoxiao, Li, Xiang, Liu, Tianming, Li, Quanzheng
The Segmentation Anything Model (SAM) has gained significant attention for its robust generalization capabilities across diverse downstream tasks. However, the performance of SAM is noticeably diminished in medical images due to the substantial dispa
Externí odkaz:
http://arxiv.org/abs/2309.13539