blockchain

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

Identification of Depression with a Semi-supervised GCN based on EEG Data

Abstract: In this paper, to explore the application of depression EEG data in semi-supervised classification, we designed an improved semi-supervised graph convolutional neural network model for ...
IEEE

Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for semi-supervised learning tasks. It is known that the graph convolution operations in most of existing GCNs are composed of ...