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 ...
Scientific Research Publishing

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

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 ...
Phys.org

Researchers develop neural network for large-scale celestial object classification

A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification, according ...
Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...
IEEE

G-KAN: Graph Kolmogorov-Arnold Network for Node Classification Using Contrastive Learning

Abstract: Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. The selection of activation ...