4 papers
Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
Qinhan Hou, Jing Tang
Graph Transformers can mix information globally, but this flexibility also creates failure modes: some tasks require long-range communication while others are better served by loca…
Exploring Heterophily in Graph-level Tasks
Qinhan Hou, Yilun Zheng, Xichun Zhang +2
While heterophily has been widely studied in node-level tasks, its impact on graph-level tasks remains unclear. We present the first analysis of heterophily in graph-level learning…
Plots Unlock Time-Series Understanding in Multimodal Models
Mayank Daswani, Mathias M. J. Bellaiche, Marc Wilson +11
While multimodal foundation models can now natively work with data beyond text, they remain underutilized in analyzing the considerable amounts of multi-dimensional time-series dat…
Health AI Developer Foundations
Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick +23
Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel…