Federated graph machine learning: A survey of concepts, techniques, and applications
Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Graph neural networks in particle physics
Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …
Generating useful accident-prone driving scenarios via a learned traffic prior
Evaluating and improving planning for autonomous vehicles requires scalable generation of
long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging …
long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging …
Slaps: Self-supervision improves structure learning for graph neural networks
Graph neural networks (GNNs) work well when the graph structure is provided. However,
this structure may not always be available in real-world applications. One solution to this …
this structure may not always be available in real-world applications. One solution to this …
My body is a cage: the role of morphology in graph-based incompatible control
V Kurin, M Igl, T Rocktäschel, W Boehmer… - ar**s to predict the properties of a graph system at its stationary state (fixed point) with …
Robust graph structure learning via multiple statistical tests
Graph structure learning aims to learn connectivity in a graph from data. It is particularly
important for many computer vision related tasks since no explicit graph structure is …
important for many computer vision related tasks since no explicit graph structure is …