Results 71 to 80 of about 1,708,308 (346)
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning [PDF]
International Journal of Production Research, 2021 We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSSP as a sequential decision-making problem with graph representation of the
Junyoung Park +4 more
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Grammars and cellular automata for evolving neural networks architectures [PDF]
, 2000 IEEE International Conference on Systems, Man, and Cybernetics. Nashville, TN, 8-11 October 2000The class of feedforward neural networks trained with back-propagation admits a large variety of specific architectures applicable to approximation pattern ...
Molina López, José Manuel +3 more
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Network Slicing End-to-end Latency Prediction Based on Heterogeneous Graph Neural Network [PDF]
Jisuanji kexueEnd-to-end latency,as a crucial performance metric for network slicing,is difficult to predict accurately via modeling due to the influences of network topology,traffic model,and scheduling policies.To tackle the above issues,we propose a heterogeneous ...
HU Haifeng, ZHU Yiwen, ZHAO Haitao
doaj +1 more source
Graph Neural Networks for Graph Search [PDF]
Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 2020 Graph neural networks (GNNs) have received more and more attention in past several years, due to the wide applications of graphs and networks, and the superiority of their performance compared to traditional heuristics-driven approaches. However, most existing GNNs still focus on node-level applications, such as node classification and link prediction,
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IEEE Access, 2023 To improve the accuracy of graph neural network recommendation algorithms, research mainly integrates multi head attention mechanism and GRU, which is to construct a graph neural network recommendation model; Considering the long and short term ...
Fang Liu, Juan Wang, Junye Yang
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Rethinking Graph Regularization for Graph Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model f(X). However, with the recent popularity of graph neural networks (GNNs), directly encoding graph structure A into a model, i.e., f(A, X), has become the more common approach.
Han Yang 0002 +2 more
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Encrypted Network Traffic Classification with Higher Order Graph Neural Network
, 2023 Encryption protects internet users’ data security and privacy but makes network traffic classification a much harder problem. Network traffic classification is essential for identifying and predicting user behaviour which is important for the overall ...
Jadidi, Z +4 more
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Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network
, 2023 Rowing competitions require consistent rowing strokes among crew members to achieve optimal performance. However, existing motion analysis techniques often rely on wearable sensors, leading to challenges in sporter inconvenience.
Chien-Chang Chen 1, Cheng-Shian Lin 1,*, Yen-Ting Chen 1 , Wen-Her Chen 2 , Chien-Hua Chen 2,3 and I-Cheng Chen 2
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The Logic of Graph Neural Networks [PDF]
2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 2021 Graph neural networks (GNNs) are deep learning architectures for machine learning problems on graphs. It has recently been shown that the expressiveness of GNNs can be characterised precisely by the combinatorial Weisfeiler-Leman algorithms and by finite variable counting logics.
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Edge-Labeling Graph Neural Network for Few-Shot Learning [PDF]
Computer Vision and Pattern Recognition, 2019 In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning.
Jongmin Kim +3 more
semanticscholar +1 more source