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.

Comp Sci High faces a new and rapidly evolving challenge: the rise of AI, a force reshaping both education and the tech ...

Keeping up with the latest research is vital for scientists, but given that millions of scientific papers are published every ...

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 ...

For half a century, computing advanced in a reassuring, predictable way. Transistors—devices used to switch electrical ...

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

Researchers at Shanghai Jiao Tong University have made a groundbreaking discovery in the field of Temporal Knowledge Graphs ...