Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Park, Jinkyoo"'
Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure pr
Externí odkaz:
http://arxiv.org/abs/2410.17270
Autor:
Seo, Seonghwan, Kim, Minsu, Shen, Tony, Ester, Martin, Park, Jinkyoo, Ahn, Sungsoo, Kim, Woo Youn
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world scenarios. In
Externí odkaz:
http://arxiv.org/abs/2410.04542
Autor:
Kim, Hyeonah, Kim, Minsu, Yun, Taeyoung, Choi, Sanghyeok, Bengio, Emmanuel, Hernández-García, Alex, Park, Jinkyoo
Designing biological sequences with desired properties is a significant challenge due to the combinatorially vast search space and the high cost of evaluating each candidate sequence. To address these challenges, reinforcement learning (RL) methods,
Externí odkaz:
http://arxiv.org/abs/2410.04461
Autor:
Kim, Minsu, Choi, Sanghyeok, Yun, Taeyoung, Bengio, Emmanuel, Feng, Leo, Rector-Brooks, Jarrid, Ahn, Sungsoo, Park, Jinkyoo, Malkin, Nikolay, Bengio, Yoshua
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is intractable. When sampling is implemented as a sequential decision-makin
Externí odkaz:
http://arxiv.org/abs/2410.01432
The offline datasets for imitation learning (IL) in multi-agent games typically contain player trajectories exhibiting diverse strategies, which necessitate measures to prevent learning algorithms from acquiring undesirable behaviors. Learning repres
Externí odkaz:
http://arxiv.org/abs/2409.19363
Autor:
Berto, Federico, Hua, Chuanbo, Luttmann, Laurin, Son, Jiwoo, Park, Junyoung, Ahn, Kyuree, Kwon, Changhyun, Xie, Lin, Park, Jinkyoo
Multi-agent combinatorial optimization problems such as routing and scheduling have great practical relevance but present challenges due to their NP-hard combinatorial nature, hard constraints on the number of possible agents, and hard-to-optimize ob
Externí odkaz:
http://arxiv.org/abs/2409.03811
Autor:
Yun, Taeyoung, Lee, Kanghoon, Yun, Sujin, Kim, Ilmyung, Jung, Won-Woo, Kwon, Min-Cheol, Choi, Kyujin, Lee, Yoohyeon, Park, Jinkyoo
Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However, most current traffic light m
Externí odkaz:
http://arxiv.org/abs/2408.07327
Optimizing complex and high-dimensional black-box functions is ubiquitous in science and engineering fields. Unfortunately, the online evaluation of these functions is restricted due to time and safety constraints in most cases. In offline model-base
Externí odkaz:
http://arxiv.org/abs/2407.01624
Autor:
Berto, Federico, Hua, Chuanbo, Zepeda, Nayeli Gast, Hottung, André, Wouda, Niels, Lan, Leon, Park, Junyoung, Tierney, Kevin, Park, Jinkyoo
This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model for VRPs should be able to represent variants by treating each as a sub
Externí odkaz:
http://arxiv.org/abs/2406.15007
Min-max problems are important in multi-agent sequential decision-making because they improve the performance of the worst-performing agent in the network. However, solving the multi-agent min-max problem is challenging. We propose a modular, distrib
Externí odkaz:
http://arxiv.org/abs/2405.19570