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 ...
A team of EPFL researchers has taken a major step towards resolving the problem of drift in generative video, which is what ...
At Web Summit Qatar, AI-powered biotech startups describe how automation, data, and gene editing are filling labor gaps in ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
Abstract: With the advancement of remote sensing satellite technology and the rapid progress of deep learning, remote sensing change detection (RSCD) has become a key technique for regional monitoring ...
1 Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA, United States 2 Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States ...
Most robot headlines follow a familiar script: a machine masters one narrow trick in a controlled lab, then comes the bold promise that everything is about to change. I usually tune those stories out.
1 Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, United States 2 Department of Computer Science, Washington University in St. Louis, St. Louis, MO, United ...
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