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Understanding the Rise of Graph Neural Networks in AI

Graph Neural Networks (GNNs) are reshaping AI by enhancing data interpretation and improving applications. Learn how GNNs are crucial in advancing machine learning models. Graph Neural Networks (GNNs) ...
Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
Frontiers

Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal ...

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 ...
Scientific Research Publishing

Zhang, X., Zhao, J.B. and LeCun, Y. (2015) Character-Level Convolutional Networks for Text ...

ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...
IEEE

Deep Graph Wavelet Neural Networks Using Heat Kernel for Semi-supervised Node Classification

Abstract: Graph convolutional neural networks have demonstrated promising solutions for processing non-Euclidean data in tasks such as node classification. While existing graph convolution models aim ...
IEEE

Semi-supervised Classification of Hyperspectral Image through Deep Encoder-Decoder and ...

Abstract: The hyperspectral image (HSI) classification is a challenging task due to the high dimensional spectral feature space, and a low number of labeled training samples. To overcome these issues, ...