Graph-in-Graph Convolutional Network for Hyperspectral Image Classification.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3182715
Abstract: With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and… read more here.
Keywords: graph convolutional; graph graph; classification; hsi classification ... See more keywords
Graph-Graph Similarity Network.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3218936
Abstract: Graph learning aims to predict the label for an entire graph. Recently, graph neural network (GNN)-based approaches become an essential strand to learning low-dimensional continuous embeddings of entire graphs for graph label prediction. While GNNs… read more here.
Keywords: graph graph; similarity; graph; label ... See more keywords
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.48550/arxiv.2204.08608
Abstract: Retrosynthesis prediction, the task of identifying reactant molecules that can be used to synthesize product molecules, is a fundamental challenge in organic chemistry and related fields. To address this challenge, we propose a novel graph-to-graph… read more here.
Keywords: retrosynthesis prediction; graph; self training; graph graph ... See more keywords