The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

Therefore, this paper proposes a linear graph neural network framework [Linear Graph Neural Network (LGNN)] with superior performance. The model first preprocesses the input graph, and uses symmetric ...

Specifically, the upper layer of the HDM serves as a coarse-level policy generator, which utilizes the plan-informed graph attention network (P-GAT) to provide interaction-aware guidance ... which ...

Most recently, Vision Graph Neural Networks (ViG) have shown very promising performance through representing images as graphs. The performance of ViG models heavily depends on how the graph is ...

Such networks, known to mathematicians as graphs, are made up of nodes connected by edges. On a football pitch, each player ...

The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, ...

MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn ...

The textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve ...

Department of Chemical Engineering and Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, P. R. China ...

This is a lightweight repository of bayesian neural network for PyTorch. @article{lee2022graddiv, title={Graddiv: Adversarial robustness of randomized neural networks via gradient diversity ...

Developed by Meta, PyTorch is a popular machine learning library that helps develop and train neural networks.