How Uber Uses Graph Neural Networks

The article discusses the application of relational graph learning, specifically relational graph convolutional networks (RGCN), to detect collusion in fraudulent activities within the Uber platfor...

This article discusses the use of Graph Neural Networks for predicting vehicle trajectories in autonomous driving by modeling pairwise interactions among agents.

The article introduces Graph Recurrent Attention Networks (GRANs), a new family of deep generative models designed for efficient graph generation.

The article discusses the use of Graph Neural Networks (GNNs) for inference in probabilistic graphical models, highlighting their ability to outperform traditional message-passing algorithms like b...

The article discusses NerveNet, a novel approach to learning structured policies for continuous control using Graph Neural Networks.

The article discusses the development of a 3D Graph Neural Network (3DGNN) for RGBD semantic segmentation, emphasizing the integration of 2D appearance and 3D geometric information.

The article discusses the use of Graph Neural Networks (GNNs) for recognizing situations in images by predicting salient verbs and their associated semantic roles.