IEEE

Dual-Semantic Graph Convolution Network for Hyperspectral Image Classification With Few Labeled Samples

Abstract: In recent years, superpixel-based graph convolutional networks (GCNs) have drawn increasing attention within the hyperspectral image (HSI) classification community. Due to the ...
IEEE

Global and Local Graph Convolution Network for Multi-Label Class Incremental Learning

Abstract: Despite significant progresses in single-label class incremental learning (CIL), its multi-label counterpart remains relatively understudied which is called multi-label class incremental ...
Frontiers

Association prediction of lncRNAs and diseases using multiview graph convolution neural network

1 College of Computer Science and Engineering, Changsha University, Changsha, Hunan, China 2 Department of Information and Computing Science, College of Mathematics, Changsha University, Changsha, ...
C&EN

Generating High-Temperature Polymer Dielectrics with a Graph Convolution and Reinforcement Learning Framework

State Key Laboratory of Power System Operation and Control, Department of Electrical Engineering, Tsinghua University, Beijing 100084, People’s Republic of China ...
GitHub

Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction

This archive is distributed in association with the INFORMS Journal on Computing under the MIT License. The software and data in this repository are a snapshot of the software and data that were used ...