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 ...

Jacob Holm was flipping through proofs from an October 2019 research paper he and colleague Eva Rotenberg—an associate professor in the department of applied mathematics and computer science at the ...

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 ...

GeekWire chronicles the Pacific Northwest startup scene. Sign up for our weekly startup newsletter, and check out the GeekWire funding tracker and VC directory. by John Cook on May 14, 2013 at 4:30 am ...

With a $9.2 million grant from Intelligence Advanced Research Projects Activity (IARPA), Prof. Andrew A. Chien will lead a team of University of Chicago computer science researchers building the ...

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

A research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. A KAIST research team has developed a new ...

Abstract: The speed of algorithms on massive graphs depends on the size of the given data. Grammar-based compression is a technique to compress the size of a graph while still allowing to read or to ...

Imagine a world where artificial intelligence not only understands language but creates with it, where quantum systems no longer feel like an enigma but a solvable puzzle. It might sound like science ...