4 citations · 4 across the 4 of their papers we have counts for
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cs.LG2026
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…
cs.LG2025
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…
cs.LG2024★ 4 cited
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…