13 citations · 23 across the 26 of their papers we have counts for
8 papers · 1 filter
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
Lequan Lin, Dai Shi, Andi Han +2
Graph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction. U…
When Graph Neural Networks Meet Dynamic Mode Decomposition
Dai Shi, Lequan Lin, Andi Han +3
Graph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between GNN featur…
A Riemannian Approach to Ground Metric Learning for Optimal Transport
Pratik Jawanpuria, Dai Shi, Bamdev Mishra +1
Optimal transport (OT) theory has attracted much attention in machine learning and signal processing applications. OT defines a notion of distance between probability distributions…
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration
Kuan Yan, Yue Zeng, Dai Shi +5
Age-related macular degeneration (AMD) is a major cause of blindness in older adults, severely affecting vision and quality of life. Despite advances in understanding AMD, the mole…
Unleash Graph Neural Networks from Heavy Tuning
Lequan Lin, Dai Shi, Andi Han +2
Graph Neural Networks (GNNs) are deep-learning architectures designed for graph-type data, where understanding relationships among individual observations is crucial. However, achi…
CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model
Zhiqi Shao, Michael G. H. Bell, Ze Wang +3
Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management. Current Spatio-Temporal Transformer models, despit…