IEEE

A Flexible Diffusion Convolution for Graph Neural Networks

Abstract: Graph Neural Networks (GNNs) have been gaining more attention due to their excellent performance in modeling various graph-structured data. However, most of the current GNNs only consider ...
IEEE

Dual-Semantic Graph Convolution Network for Hyperspectral Image Classification With Few Labeled Samples

Abstract: In recent years, superpixel-based graph convolutional networks (GCNs) have drawn increasing attention within the hyperspectral image (HSI) classification community. Due to the ...
Inc

Your Behaviors Speak Louder Than Your Words: Leadership Through Example

Leadership is about much more than issuing orders or delegating tasks. One of your most essential roles as a leader is to serve as a role model. Your team is constantly observing how you conduct ...
Oak Ridge National Lab

Impedance-Aware Graph Convolutional Networks for Voltage Estimation in Active Distribution Networks

Voltage estimation plays a key role in ensuring the effective control and reliability of distribution networks. However, traditional machine learning methods often fail to capture the details of the ...
Microsoft

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input ...