Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Learning-based methods have shown promising performance for accelerating motion
planning, but mostly in the setting of static environments. For the more challenging problem …

Graph neural networks (GNNs) are naturally distributed architectures for learning
representations from network data. This renders them suitable candidates for decentralized …

Geometric deep learning has gained much attention in recent years due to more available
data acquired from non-Euclidean domains. Some examples include point clouds for 3D …

Deep neural networks for graphs have emerged as a powerful tool for learning on complex
non-euclidean data, which is becoming increasingly common for a variety of different …

This paper proposes a data-driven graphical framework for the real-time search of risky
cascading fault chains (). While identifying risky is pivotal to alleviating cascading failures …