Zobrazeno 1 - 10
of 420
pro vyhledávání: '"Kim, Seonghwan"'
Transporting between arbitrary distributions is a fundamental goal in generative modeling. Recently proposed diffusion bridge models provide a potential solution, but they rely on a joint distribution that is difficult to obtain in practice. Furtherm
Externí odkaz:
http://arxiv.org/abs/2410.01500
Autor:
Research, LG AI, An, Soyoung, Bae, Kyunghoon, Choi, Eunbi, Choi, Stanley Jungkyu, Choi, Yemuk, Hong, Seokhee, Hong, Yeonjung, Hwang, Junwon, Jeon, Hyojin, Jo, Gerrard Jeongwon, Jo, Hyunjik, Jung, Jiyeon, Jung, Yountae, Kim, Euisoon, Kim, Hyosang, Kim, Joonkee, Kim, Seonghwan, Kim, Soyeon, Kim, Sunkyoung, Kim, Yireun, Kim, Youchul, Lee, Edward Hwayoung, Lee, Haeju, Lee, Honglak, Lee, Jinsik, Lee, Kyungmin, Lee, Moontae, Lee, Seungjun, Lim, Woohyung, Park, Sangha, Park, Sooyoun, Park, Yongmin, Seo, Boseong, Yang, Sihoon, Yeen, Heuiyeen, Yoo, Kyungjae, Yun, Hyeongu
We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote ope
Externí odkaz:
http://arxiv.org/abs/2408.03541
Understanding transition pathways between meta-stable states in molecular systems is crucial to advance material design and drug discovery. However, unbiased molecular dynamics simulations are computationally infeasible due to the high energy barrier
Externí odkaz:
http://arxiv.org/abs/2405.19961
Autor:
Chae, Hyungjoo, Kim, Yeonghyeon, Kim, Seungone, Ong, Kai Tzu-iunn, Kwak, Beong-woo, Kim, Moohyeon, Kim, Seonghwan, Kwon, Taeyoon, Chung, Jiwan, Yu, Youngjae, Yeo, Jinyoung
Algorithmic reasoning refers to the ability to understand the complex patterns behind the problem and decompose them into a sequence of reasoning steps towards the solution. Such nature of algorithmic reasoning makes it a challenge for large language
Externí odkaz:
http://arxiv.org/abs/2404.02575
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However
Externí odkaz:
http://arxiv.org/abs/2304.12233
As quantum chemical properties have a dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tasks. However, they often require 3D geometries obtained from high-level
Externí odkaz:
http://arxiv.org/abs/2304.03724
Autor:
Kim, Mingyu, Oh, Jihwan, Lee, Yongsik, Kim, Joonkee, Kim, Seonghwan, Chong, Song, Yun, Se-Young
In this paper, we propose a novel benchmark called the StarCraft Multi-Agent Challenges+, where agents learn to perform multi-stage tasks and to use environmental factors without precise reward functions. The previous challenges (SMAC) recognized as
Externí odkaz:
http://arxiv.org/abs/2207.02007
Autor:
Yun, Young In, Ko, Jung Hwa, Ryu, Jin Suk, Kim, Seonghwan, Jeon, Hyun Sun, Kim, Namju, Kim, Mee Kum, Oh, Joo Youn
Publikováno v:
In The Ocular Surface October 2024 34:96-107
Autor:
Akash, Nasim Mahmud, Saad, Shabab, Bari, Md Abdullah Al, Sarker, Rahul, Gupta, Chetan, Asghari Sarabi, Ghazale, Phani, Arindam, Zahin, Farhan, Tabassum, Samia, Subramanian, Kasimuthumaniyan, Kim, Seonghwan, Rahman, Muhammad M., Egberts, Philip, Kibria, Md Golam
Publikováno v:
In Carbon September 2024 228
Autor:
Lim, Joong Yeon, Kim, Seonghwan, Rahman, Muhammad Toyabur, Zandi, Pegah, Phani, Arindam, Homayoonnia, Setareh, Jeon, Hee Chang, Jin, Zhengyun, Park, Minwook, Kim, Young-Seong
Publikováno v:
In Journal of Alloys and Compounds 5 December 2024 1007