Frontiers

Graph Learning and Deep Unfolding for Electromagnetic Field Modelling and Antenna Systems

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...
EurekAlert!

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 ...
IEEE

Transformer and Graph Neural Network-Based Federated Learning for Cellular Traffic Prediction With Sustainability Analysis

Abstract: Mobile network traffic prediction is critical for efficient network management in 5G and future 6G systems. Federated Learning (FL) enables multiple base stations (BSs) to collaboratively ...
IEEE

Wasserstein Graph Neural Networks for Graphs With Missing Attributes

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
Hosted on MSN

Create jewelry collection with crystal links | Flower bracelet tutorial making | Bead and wire 1019

Tutorial for making simple jewelry from copper wire, stone and bead from LanAnhHandmade. The method is simple, easy to understand and completely free, so you can do it yourself at home. In this video, ...
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 ...