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检索条件"主题词=neural combinatorial optimization"
34 条 记 录,以下是31-40 订阅
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Graph Representation for Learning the Traveling Salesman Problem  13th
Graph Representation for Learning the Traveling Salesman Pro...
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13th Mexican Conference on Pattern Recognition (MCPR)
作者: Gutierrez, Omar Zamora, Erik Menchaca, Ricardo Inst Politecn Nacl CIC Av Juan Dios Batiz S-NGustavo A Madero Mexico City 07738 Mexico
Training deep learning models for solving the Travelling Salesman Problem (TSP) directly on large instances is computationally challenging. An approach to tackle large-scale TSPs is through identifying elements in the... 详细信息
来源: 评论
Exploring the use of Deep Reinforcement Learning to allocate tasks in Critical Adaptive Distributed Embedded Systems  26
Exploring the use of Deep Reinforcement Learning to allocate...
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26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
作者: Rotaeche, Ramon Ballesteros, Alberto Proenza, Julian Univ Illes Balears DMI Palma De Mallorca Spain
Critical Adaptive Distributed Embedded Systems (CADES) must carry out a set of funcionalities while fulfilling their associated real-time and dependability requirements. Moreover, they must be able to reconfigure them... 详细信息
来源: 评论
One model packs thousands of items with Recurrent Conditional Query Learning
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KNOWLEDGE-BASED SYSTEMS 2022年 235卷
作者: Li, Dongda Gu, Zhaoquan Wang, Yuexuan Ren, Changwei Lau, Francis C. M. Guangzhou Univ Guangzhou Guangdong Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Univ Hong Kong Hong Kong Peoples R China
Recent studies have revealed that neural combinatorial optimization (NCO) has advantages over conventional algorithms in many combinatorial optimization problems such as routing, but it is less efficient for more comp... 详细信息
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TilinGNN: Learning to Tile with Self-Supervised Graph neural Network
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ACM TRANSACTIONS ON GRAPHICS 2020年 第4期39卷 129:1-129:16页
作者: Xu, Hao Hui, Ka-Hei Fu, Chi-Wing Zhang, Hao Chinese Univ Hong Kong Hong Kong Peoples R China Simon Fraser Univ Burnaby BC Canada
We introduce the first neural optimization framework to solve a classical instance of the tiling problem. Namely, we seek a non-periodic tiling of an arbitrary 2D shape using one or more types of tiles-the tiles maxim... 详细信息
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