Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Deep learning on point clouds has received increased attention thanks to its wide
applications in AR/VR and autonomous driving. These applications require low latency and …

High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …

Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is
a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is …

Sparse tensors are rapidly becoming critical components of modern deep learning
workloads. However, developing high-performance sparse operators can be difficult and …

Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Edge intelligence has arisen as a promising computing paradigm for supporting
miscellaneous smart applications that rely on machine learning techniques. While the …

Graph attention models (A-GNNs), a type of Graph Neural Networks (GNNs), have been
shown to be more powerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs …