[Deep Graph Learning] 4.5 Generalized GNN layer and Dropout

Graph Neural Networks - a perspective from the ground upПодробнее

Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.2 - A Single Layer of a GNNПодробнее

Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 6.3 - Deep Learning for GraphsПодробнее

[Deep Graph Learning] 4.3 Recap on GNN sampling methodsПодробнее

Stanford CS224W: ML with Graphs | 2021 | Lecture 16.3 - Identity-Aware Graph Neural NetworksПодробнее

Stefanie Jegelka - Representation and Learning in Graph Neural NetworksПодробнее

KDD2020: DEEP LEARNING DAY: Graph Mining HamiltonПодробнее

Self-/Unsupervised GNN TrainingПодробнее

Graph Attention Networks (GAT) in 5 minutesПодробнее

Dropout layer in Neural Network | Dropout Explained | Quick ExplainedПодробнее

NeurIPS 2021 || DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks PaperПодробнее

[Deep Graph Learning] 4.6 GNN inductive vs transductive learningПодробнее

Machine Learning with Graphs: Graph Neural Network Model - Design SpaceПодробнее

An Introduction to Graph Neural Networks: Models and ApplicationsПодробнее

Stanford CS224W: ML with Graphs | 2021 | Lecture 19.3 - Design Space of Graph Neural NetworksПодробнее

Day 93 Temporal graph networks #facts #artificialintelligence #ai #facts #learning #chatgptПодробнее

Applied Deep Learning 2022 - Lecture 11 - Graph Neural NetworksПодробнее

Graph Neural Networks (GNN) with TensorboardПодробнее

AWS ML Summit 2021 | Deep Graph Library: Deep Graph learning at scaleПодробнее