Results 51 to 60 of about 5,698,498 (295)
Stability of graph convolutional neural networks to stochastic perturbations [PDF]
Signal Processing, 2021 Graph convolutional neural networks (GCNNs) are nonlinear processing tools to learn representations from network data. A key property of GCNNs is their stability to graph perturbations. Current analysis considers deterministic perturbations but fails to provide relevant insights when topological changes are random. This paper investigates the stability
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
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Learning Convolutional Neural Networks for Graphs
CoRR, 2016 To be presented at ICML ...
Mathias Niepert +2 more
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Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting
Connection Science, 2022 Traffic forecasting is highly challenging due to its complex spatial and temporal dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in ...
Na Hu +4 more
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Dual-channel deep graph convolutional neural networks
Frontiers in Artificial IntelligenceThe dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye +15 more
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Non-convolutional graph neural networks.
Advances in Neural Information Processing Systems 37Rethink convolution-based graph neural networks (GNN) -- they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation. Here, we design a simple graph learning module entirely free of convolution operators, coined random walk with unifying memory (RUM ...
Yuanqing Wang, Kyunghyun Cho
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MIMO Graph Filters for Convolutional Neural Networks [PDF]
2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018 Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks. CNNs implement three basic blocks: convolution, pooling and pointwise nonlinearity.
Fernando Gama +3 more
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Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities
Mathematics, 2022 Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start
Xing Li +3 more
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Fast Graph Convolutional Recurrent Neural Networks [PDF]
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 This paper proposes a Fast Graph Convolutional Neural Network (FGRNN) architecture to predict sequences with an underlying graph structure. The proposed architecture addresses the limitations of the standard recurrent neural network (RNN), namely, vanishing and exploding gradients, causing numerical instabilities during training.
Sai Kiran Kadambari +1 more
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SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images [PDF]
One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification.
Akbari, Vahid +5 more
core +1 more source
Advanced Healthcare Materials, EarlyView.NeuroVisio SyncPatch integrates skin‐conformal electromyography (EMG) electrodes with camera‐tracked markers to jointly assess muscle activity and three‐dimensional knee kinematics during rehabilitation. Parallel feedback pathways provide real‐time kinematic guidance during movement and post‐trial neuromuscular feedback.
Bohyung Choi +11 more
wiley +1 more source