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pro vyhledávání: '"Kim, Hyeonah"'
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
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a mu
Externí odkaz:
http://arxiv.org/abs/2403.07041
The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative methods have shown promise but face the issue of sample efficiency due
Externí odkaz:
http://arxiv.org/abs/2402.05961
Autor:
Berto, Federico, Hua, Chuanbo, Park, Junyoung, Luttmann, Laurin, Ma, Yining, Bu, Fanchen, Wang, Jiarui, Ye, Haoran, Kim, Minsu, Choi, Sanghyeok, Zepeda, Nayeli Gast, Hottung, André, Zhou, Jianan, Bi, Jieyi, Hu, Yu, Liu, Fei, Kim, Hyeonah, Son, Jiwoo, Kim, Haeyeon, Angioni, Davide, Kool, Wouter, Cao, Zhiguang, Zhang, Qingfu, Kim, Joungho, Zhang, Jie, Shin, Kijung, Wu, Cathy, Ahn, Sungsoo, Song, Guojie, Kwon, Changhyun, Tierney, Kevin, Xie, Lin, Park, Jinkyoo
Deep reinforcement learning (RL) has recently shown significant benefits in solving combinatorial optimization (CO) problems, reducing reliance on domain expertise, and improving computational efficiency. However, the field lacks a unified benchmark
Externí odkaz:
http://arxiv.org/abs/2306.17100
The cutting plane method is a key technique for successful branch-and-cut and branch-price-and-cut algorithms that find the exact optimal solutions for various vehicle routing problems (VRPs). Among various cuts, the rounded capacity inequalities (RC
Externí odkaz:
http://arxiv.org/abs/2306.17283
Min-max routing problems aim to minimize the maximum tour length among multiple agents by having agents conduct tasks in a cooperative manner. These problems include impactful real-world applications but are known as NP-hard. Existing methods are fac
Externí odkaz:
http://arxiv.org/abs/2306.02689
This paper proposes Meta-SAGE, a novel approach for improving the scalability of deep reinforcement learning models for combinatorial optimization (CO) tasks. Our method adapts pre-trained models to larger-scale problems in test time by suggesting tw
Externí odkaz:
http://arxiv.org/abs/2306.02688
Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO). However, its practicality is hindered by the necessity for a large number of reward evaluations, especially in scenarios involving computationa
Externí odkaz:
http://arxiv.org/abs/2306.01276
Autor:
Kim, Hyeonah1 (AUTHOR) hyeonah_kim@kaist.ac.kr, Park, Jinkyoo1,2 (AUTHOR) jinkyoo.park@kaist.ac.kr, Kwon, Changhyun1,2 (AUTHOR) chkwon@kaist.ac.kr
Publikováno v:
INFORMS Journal on Computing. Jul/Aug2024, Vol. 36 Issue 4, p987-1005. 19p.
Akademický článek
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