Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

The unprecedented explosion in the amount of information we are generating and collecting, thanks to the arrival of the internet and the always-online society, powers all the incredible advances we ...

A super geeky topic, which could have super important repercussions in the real world. That description could very well fit anything from cold fusion to knowledge graphs, so a bit of unpacking is in ...

These past few months have not been kind to any of us. The ripples caused by the COVID-19 crisis are felt far and wide, and the world's economies have taken a staggering blow. As with most things in ...

What if you could transform vast amounts of unstructured text into a living, breathing map of knowledge—one that not only organizes information but reveals hidden connections you never knew existed?