Results 41 to 50 of about 110,849 (310)

Review of Graph Neural Networks [PDF]

Jisuanji kexue
With the rapid development of artificial intelligence,deep learning has achieved great success in data that can be represented in Euclidean spaces,such as images,text,and speech.However,it has been difficult to apply deep learning to non-Eucli-dean ...
HOU Lei, LIU Jinhuan, YU Xu, DU Junwei
doaj   +1 more source

Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Data Science and Engineering, 2023
Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li, Man-Sheng Chen, Yuefang Gao, Chang-Dong Wang   +3 more
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Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network

Sensors, 2023
In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks ...
Dong Wang, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang, Yan Zhu   +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, Galván, Inés M., Sanchis de Miguel, María Araceli, Isasi, Pedro   +3 more
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STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting

Mathematics, 2022
Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data.
Yafeng Gu, Li Deng
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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,
openaire   +1 more source

Generative capacities of cellular automata codification for evolution of NN codification [PDF]

, 2002
Proceeding of: International Conference on Artificial Neural Networks. ICANN 2002, Madrid, Spain, August 28-30, 2002Automatic methods for designing artificial neural nets are desired to avoid the laborious and erratically human expert’s job. Evolutionary
Gutiérrez Sánchez, Germán, Galván, Inés M., Molina López, José Manuel, Sanchis de Miguel, María Araceli   +3 more
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The Graph Neural Network Model

IEEE Transactions on Neural Networks, 2009
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural ...
SCARSELLI, FRANCO, GORI, MARCO, TSOI A. C, HAGENBUCHNER M, MONFARDINI, GABRIELE   +4 more
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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, Kaili Ma 0001, James Cheng   +2 more
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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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