As real-world graphs expand in size, larger GNN models with billions of parameters are
deployed. High parameter count in such models makes training and inference on graphs …

Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based
applications due to their intrinsic capacity in modeling structural and contextual relations …

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs)
that enable the management of inline human interventions in AI-assisted manufacturing …

Deep learning on point clouds has attracted increasing attention for various emerging 3D
computer vision applications, such as autonomous driving, robotics, and virtual reality …

Graph-related applications have experienced significant growth in academia and industry,
driven by the powerful representation capabilities of graph. However, efficiently executing …

Graph convolutional networks (GCNs) have become a powerful deep learning approach for
graph-structured data. Different from traditional neural networks such as convolutional …

Graph neural networks (GNNs) are a promising method for learning graph representations
and demonstrate remarkable performance on various graph-related tasks. Existing typical …

In terms of human-computer interaction, it is becoming more and more important to correctly
understand the user's emotional state in a conversation, so the task of multimodal emotion …

Graph neural networks (GNN) inferencing involves weighting vertex feature vectors,
followed by aggregating weighted vectors over a vertex neighborhood. High and variable …

Many relational data in our daily life are represented as graphs, making graph application
an important workload. Because of the large scale of graph datasets, moving graph data to …