Results 231 to 240 of about 35,453 (267)

Graph Convolutional Networks for multi-modal robotic martial arts leg pose recognition. [PDF]

Front Neurorobot
Yao S, Ping Y, Yue X, Chen H.
europepmc   +1 more source

CellMsg: graph convolutional networks for ligand-receptor-mediated cell-cell communication analysis. [PDF]

Brief Bioinform
Xia H, Ji B, Qiao D, Peng S.
europepmc   +1 more source

ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks. [PDF]

ACS Omega
Li G, Zhang N, Fan L.
europepmc   +1 more source

Shape-based disease grading via functional maps and graph convolutional networks with application to Alzheimer's disease. [PDF]

BMC Med Imaging
Mayer J, Baum D, Ambellan F, von Tycowicz C, Alzheimer’s Disease Neuroimaging Initiative.   +4 more
europepmc   +1 more source

SignFormer-GCN: Continuous sign language translation using spatio-temporal graph convolutional networks. [PDF]

PLoS One
Arib SH, Akter R, Rahman S, Rahman S.
europepmc   +1 more source

Predicting Organ-Specific Toxicity of Selective Androgen Receptor Modulators, using Transfer Learning on Graph Convolutional Networks

Kalian AD, Silva AC, Lee J, Dorne JC, Potter C, Benfenati E, Osborne OJ, Guo M, Hogstrand C.   +8 more
europepmc   +1 more source

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Graph sparsification with graph convolutional networks

International Journal of Data Science and Analytics, 2021
Graphs are ubiquitous across the globe and within science and engineering. Some powerful classifiers are proposed to classify nodes in graphs, such as Graph Convolutional Networks (GCNs). However, as graphs are growing in size, node classification on large graphs can be space and time consuming due to using whole graphs.
Jiayu Li 0002, Tianyun Zhang, Hao Tian, Shengmin Jin, Makan Fardad, Reza Zafarani   +5 more
openaire   +1 more source

Graph Convolutional Network Hashing

IEEE Transactions on Cybernetics, 2020
Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity graph has been extensively studied for large-scale image retrieval. However, most graph-based hashing methods resort to intractable binary quadratic programs, making them unscalable to massive data.
Xiang Zhou 0008, Fumin Shen, Li Liu 0004, Wei Liu 0005, Liqiang Nie, Yang Yang 0002, Heng Tao Shen   +6 more
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Fuzzy Graph Subspace Convolutional Network

IEEE Transactions on Neural Networks and Learning Systems
Graph convolutional networks (GCNs) are a popular approach to learn the feature embedding of graph-structured data, which has shown to be highly effective as well as efficient in performing node classification in an inductive way. However, with massive nongraph-organized data existing in application scenarios nowadays, it is critical to exploit the ...
Jianhang Zhou, Qi Zhang 0059, Shaoning Zeng, Bob Zhang 0001   +3 more
openaire   +2 more sources