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PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
IEEE

RandomViG: Random Vision Graph Neural Network for Image Classification

Abstract: Vision Graph Neural Network (ViG) is the first graph neural network model capable of directly processing image data. The community primarily focuses on the model structures to improve ViG’s ...
Hosted on MSN

Backpropagation from scratch in Python – step by step neural network tutorial

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...
C&EN

Multilevel Fusion Graph Neural Network for Molecule Property Prediction

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
IEEE

Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization

Abstract: In this paper, we design Graph Neural Networks (GNNs) with attention mechanisms to tackle an important yet challenging nonlinear regression problem: massive network localization. We first ...
Scientific Research Publishing

Han, L.Q. (2006) Artificial neural network tutorial. Beijing University of Posts and Telecommunications Press, Beijing, 12Ver 1, 137.

ABSTRACT: It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one ...