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 ...

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

I recently ran across a blog post that discusses a very important characteristic for machine learning solutions – Generalization. If you’ve ever wondered about the primary reason why machines can ...

Robotics is entering a new phase where general-purpose learning matters as much as mechanical design. Instead of programming ...

The effect of variability on learning is recognized in many fields: learning is harder when input is variable, but variability leads to better generalization of the knowledge we learned. In this ...