Representation learning lies at the core of modern artificial intelligence, enabling neural networks to uncover meaningful, ...

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 ...

This course is available on the MSc in Data Science, MSc in Geographic Data Science, MSc in Health Data Science, MSc in Operations Research & Analytics, MSc in Quantitative Methods for Risk Management ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

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 ...

Have you ever done a Google search to find a restaurant or look up what your favorite actor is up to? Most of us have, and therefore understand the benefit of knowledge graphs, possibly without even ...

The concept of knowledge graphs arose from scientific advances in a variety of research fields, including the semantic web, databases, natural language processing, and machine learning. According to ...