As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
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 new technical paper titled “A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware” was published by ...
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 ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
Microgrids play a growing role in modern power systems, supporting renewable integration, local resilience, and decentralized energy management. Yet as renewable penetration rises, maintaining stable ...
One of the most striking outcomes of the surface-minimization framework is its ability to explain structural features that ...
This is an important work implementing data mining methods on IMC data to discover spatial protein patterns related to the triple-negative breast cancer patients' chemotherapy response. The evidence ...
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 ...
News Medical on MSN
Using AI to understand the age of disease onset in Huntington's patients
A team from the Faculty of Medicine and Health Sciences and the Institute of Neurosciences at the University of Barcelona (UBneuro) has applied advanced artificial intelligence techniques to better ...
AI autoscaling promises a self-driving cloud, but if you don’t secure the model, attackers can game it into burning cash or ...
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