Abstract: Convolutional neural networks (CNNs) and graph neural networks (GNNs) are two widely used architectures in hyperspectral image (HSI) classification. Most CNN models tend to heavily rely on ...
Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances
Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
ABSTRACT: Knowledge Graph (KG) and neural network (NN) based Question-answering (QA) systems have evolved into the realm of intelligent information retrieval as they have been able to reach a high ...
According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...
Abstract: In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in ...
The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning. To understand this inadequacy, there is a need to ...
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