Zobrazeno 1 - 10
of 128
pro vyhledávání: '"Liu, Shengcai"'
Recently, graph condensation has emerged as a prevalent technique to improve the training efficiency for graph neural networks (GNNs). It condenses a large graph into a small one such that a GNN trained on this small synthetic graph can achieve compa
Externí odkaz:
http://arxiv.org/abs/2407.11025
Real-world applications involve various discrete optimization problems. Designing a specialized optimizer for each of these problems is challenging, typically requiring significant domain knowledge and human efforts. Hence, developing general-purpose
Externí odkaz:
http://arxiv.org/abs/2405.18884
This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle smal
Externí odkaz:
http://arxiv.org/abs/2406.15416
Autor:
Lu, Yongfan, Di, Zixiang, Li, Bingdong, Liu, Shengcai, Qian, Hong, Yang, Peng, Tang, Ke, Zhou, Aimin
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to transform an MOCO problem into a series of singe-objective combinatorial o
Externí odkaz:
http://arxiv.org/abs/2405.08604
Pointer Network (PtrNet) is a specific neural network for solving Combinatorial Optimization Problems (COPs). While PtrNets offer real-time feed-forward inference for complex COPs instances, its quality of the results tends to be less satisfactory. O
Externí odkaz:
http://arxiv.org/abs/2312.01150
Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In th
Externí odkaz:
http://arxiv.org/abs/2310.19046
Autor:
Wu, Jiahao, Liu, Qijiong, Hu, Hengchang, Fan, Wenqi, Liu, Shengcai, Li, Qing, Wu, Xiao-Ming, Tang, Ke
Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on large datasets. To address this issue, we explore the dataset condensa
Externí odkaz:
http://arxiv.org/abs/2310.09874
Training recommendation models on large datasets often requires significant time and computational resources. Consequently, an emergent imperative has arisen to construct informative, smaller-scale datasets for efficiently training. Dataset compressi
Externí odkaz:
http://arxiv.org/abs/2310.01038
Graph-based collaborative filtering has emerged as a powerful paradigm for delivering personalized recommendations. Despite their demonstrated effectiveness, these methods often neglect the underlying intents of users, which constitute a pivotal face
Externí odkaz:
http://arxiv.org/abs/2309.12723
Real-time solutions to the influence blocking maximization (IBM) problems are crucial for promptly containing the spread of misinformation. However, achieving this goal is non-trivial, mainly because assessing the blocked influence of an IBM problem
Externí odkaz:
http://arxiv.org/abs/2308.14012