Abstract: Existing methods based on graph convolutional neural network have made some achievements in graph ... In this paper, we propose a marginalized graph autoencoder with subspace structure ...

MLCommons' AI training tests show that the more chips you have, the more critical the network that's between them.

Using an algorithm they call the Krakencoder, researchers at Weill Cornell Medicine are a step closer to unraveling how the ...

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...

Abstract: In this article, we first propose a graph neural network encoding method for the multiobjective evolutionary algorithm (MOEA) to handle the community detection problem in complex attribute ...

It is worth noting neuromorphic computing is not synonymous with the deep neural networks (DNNs) already in wide use within industry – although there is growing interest in using the former to ...

Learns the normal patterns in network traffic data Automatically removes highly correlated features Detects anomalies based on reconstruction error Provides ...

Due to a production error, there was an error regarding the affiliation for Somayeh Makouei. Instead of having affiliation 2, they should have affiliation 1 ...