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 ...

At CES, what stood out to me was just how much Nvidia and AMD focused on a systems approach, which may be the most ...

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

Prism aims to move ChatGPT into scientific writing as OpenAI signals plans to share in future profits. Some are warning ...

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 ...

The Kennedy College of Science, Richard A. Miner School of Computer & Information Sciences, invites you to attend a doctoral dissertation proposal defense by Nidhi Vakil, titled: "Foundations for ...

GenAI may be accelerating a developmental transition in how learners conceptualize programming itself.

Researchers at Shanghai Jiao Tong University have made a groundbreaking discovery in the field of Temporal Knowledge Graphs (TKGs), challenging the ...

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design.

Space and time aren’t just woven into the background fabric of the universe. To theoretical computer scientists, time and space (also known as memory) are the two fundamental resources of computation.