Results 31 to 40 of about 4,990,305 (257)
Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs
IEEE Access, 2020 Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li +4 more
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Semantic–Structural Graph Convolutional Networks for Whole-Body Human Pose Estimation
Information, 2022 Existing whole-body human pose estimation methods mostly segment the parts of the body’s hands and feet for specific processing, which not only splits the overall semantics of the body, but also increases the amount of calculation and the complexity of ...
Weiwei Li, Rong Du, Shudong Chen
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A deep graph convolutional neural network architecture for graph classification.
, 2023 Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to
Yuchen Zhou +3 more
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"Graph Entropy, Network Coding and Guessing games" [PDF]
, 2007 We introduce the (private) entropy of a directed graph (in a new network coding sense) as well as a number of related concepts. We show that the entropy of a directed graph is identical to its guessing number and can be bounded from below with the number
RIIS, SM
core +4 more sources
Future Internet, 2021 Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders.
Ibsa K. Jalata +4 more
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, 2021 Accumulating evidences have shown that circRNA plays an important role in human diseases. It can be used as potential biomarker for diagnose and treatment of disease.
Wang, J +7 more
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Robust cross-network node classification via constrained graph mutual information
, 2022 The recent methods for cross-network node classification mainly exploit graph neural networks (GNNs) as feature extractor to learn expressive graph representations across the source and target graphs.
Yang, Shuiqiao +6 more
core +1 more source
Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application
Advanced Materials, EarlyView.This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong +12 more
wiley +1 more source
Sports behavior analysis technology based on GCN and domain knowledge graph
Discover ComputingTo improve the performance of sports behavior recognition, the spatial temporal graph convolutional network is introduced to analyze the spatial temporal features of sports behavior, achieving accurate action recognition. In the experimental results, the
Jiaojiao Hu, Shengnan Ran
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Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods
, 2019 Graphs are ubiquitous for describing and modeling complicated data structures, and graph embedding is an effective solution to learn a mapping from a graph to a low-dimensional vector space while preserving relevant graph characteristics.
Michael Blumenstein +11 more
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