Results 51 to 60 of about 4,082,283 (306)
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 +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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International Journal of Computer VisionAbstract Existing Graph Neural Networks (GNNs) are limited to process graphs each of whose vertices is represented by a vector or a single value, limited their representing capability to describe complex objects. In this paper, we propose a novel GNN (called Graph in Graph Neural (GIG) Network) which can process ...
Jiongshu Wang +4 more
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CoRR, 2019 Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks have been successfully applied to graph structured data such as point cloud and molecular data.
Zhen Zhang 0008 +2 more
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The Laplacian spectrum of neural networks
Frontiers in Computational Neuroscience, 2014 The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks.
Siemon ede Lange +2 more
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Graph coloring with physics-inspired graph neural networks
Physical Review Research, 2022 We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multiclass node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts model ...
Martin J. A. Schuetz +3 more
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A Survey on Graph Neural Networks for Microservice-Based Cloud Applications
Sensors, 2022 Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in ...
Hoa Xuan Nguyen +2 more
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Path integral based convolution and pooling for graph neural networks [PDF]
, 2020 Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success.
Lio P. +4 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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Symbolic Hyperdimensional Vectors with Sparse Graph Convolutional Neural Networks
, 2022 In this paper, we propose a novel way of representing graphs for processing in Graph Neural Networks. We reduce the dimensionality of the input data by using Random Indexing, a Vector Symbolic Architectural framework; we implement a new trainable neural ...
Karlgren, Jussi, +3 more
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