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 ...
Representation learning lies at the core of modern artificial intelligence, enabling neural networks to uncover meaningful, ...
Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract ...
Salary: The PhD position is fully funded by QMUL and the PhD student will receive tuition fees at the home rate and a London stipend at QMUL stipend rates (currently in 2025/26 of £21,874 per year, to ...
From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...
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 ...
Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...
Generative artificial intelligence (GenAI) is now a reality in higher education, with students and professors integrating ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
A University of Phoenix study examined an introductory environmental science course redesigned for nontraditional adult learners and found that students improved on key course goals and career-aligned ...
With the rapid development of GPT-based models, educational chatbots are no longer limited to scripted dialogs. They can now support open-ended interaction and inquiry-based learning. In a study ...
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