Abstract: Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed ...

[ChrisN219] has an antique Coke machine that used to hold glass bottles. Now it holds around 30 tall boy cans of his favorite post-work suds. The only problem is that [Chris] has no idea how many cans ...

Abstract: Recent progress in research on deep graph networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important ...

This educational repository demonstrates the architectural principles and implementation techniques of high-performance Large Language Model (LLM) inference engines, from fundamental CUDA kernels to ...