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Large language models (LLMs) have demonstrated strong performance on coding and reasoning benchmarks; however, their ability to solve graph-structured machine learning problems remains largely unexplored. In particular, no benchmark currently evaluates whether LLMs can autonomously solve end-to-end Graph Neural Network...
The epiDAMIK workshop serves as a platform for advancing the utilization of data-driven methods in the fields of epidemiology and public health research, providing a valuable avenue for sharing state-of-the-art research and ongoing projects.
Experimental results demonstrate that the coarsening technique significantly accelerates dynamic GNN training and inference without compromising predictive performance, offering a practical path toward scalable dynamic graph learning.
Contrastive Conformal HGNN (CCF-HGNN) is proposed that accounts for uncertainty in hypergraph-based models by explicitly regularizing on the hypergraph structure for guaranteed and robust uncertainty estimates.
Neural representations of task-relevant sounds are emphasized when they are attended to, compared with when they are ignored. Classic markers of this modulation, such as amplitude change of event-related potentials (ERPs) and attentional modulation indices (AMIs) derived from envelope-tracking analyses, provide robust...
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