Results 41 to 50 of about 145,055 (276)
A Hyperbolic-to-Hyperbolic Graph Convolutional Network [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 CVPR2021 ...
Jindou Dai +3 more
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Generative Graph Convolutional Network for Growing Graphs [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nodes without nontrivial ...
Da Xu +5 more
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Learning flexible representations of stochastic processes on graphs
, 2018 Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear ...
Balan, Radu +2 more
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Text Classification Method Based on Graph Neural Networks [PDF]
ITM Web of ConferencesThe goal of text classification is to assign labels to text units accurately, which is a basic task in natural language processing. This technology has shown great value in many practical application scenarios, covering spam detection, emotional tendency
Gao Ruofei
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Upright Adjustment With Graph Convolutional Networks
2020 IEEE International Conference on Image Processing (ICIP), 2020 We present a novel method for the upright adjustment of 360 images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360 images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical ...
Raehyuk Jung, Sungmin Cho, Junseok Kwon
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Graph Learning-Convolutional Networks
CoRR, 2018 Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may be not optimal for semi-supervised learning tasks.
Bo Jiang 0002 +3 more
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Graph-Time Convolutional Neural Networks
2021 IEEE Data Science and Learning Workshop (DSLW), 2021 Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author) +1 more
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Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
Applied Sciences, 2022 Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning.
Yang Wang, Shunping Xiao
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Kernel Graph Convolutional Neural Networks
, 2018 Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel.
Meladianos, Polykarpos +4 more
core +2 more sources
Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
Applied SciencesThis research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns.
Vandana Singh +2 more
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