IEEE

Graph Neural Networks for Graphs With Heterophily: A Survey

Abstract: Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption ...
marktechpost

A Coding Implementation to Build Neural Memory Agents with Differentiable Memory, Meta-Learning, and Experience Replay for Continual Adaptation in Dynamic Environments

In this tutorial, we explore how neural memory agents can learn continuously without forgetting past experiences. We design a memory-augmented neural network that integrates a Differentiable Neural ...
Hosted on MSN

Network in Network (NiN) Explained – Deep Neural Network Tutorial with PyTorch

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
IEEE

End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial

Abstract: Cognitive diagnosis (CD) utilizes students' existing studying records to estimate their mastery of unknown knowledge concepts, which is vital for evaluating their learning abilities.
Game Rant

Most Iconic Tutorials In Games, Ranked

Robbie has been an avid gamer for well over 20 years. During that time, he's watched countless franchises rise and fall. He's a big RPG fan but dabbles in a little bit of everything. Writing about ...
MIT Technology Review

The next generation of neural networks could live in hardware

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...