4 citations · 6 across the 6 of their papers we have counts for
6 papers
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond
Andi Han, Dai Shi, Lequan Lin +1
Graph neural networks (GNNs) have demonstrated significant promise in modelling relational data and have been widely applied in various fields of interest. The key mechanism behind…
Linear analysis of flow mode transition triggered by finite-sized particles in Rayleigh-Bénard convection
Dai Shi
A mathematical model to study the flow evolution in RB convection laden with finite-sized particles after a flow perturbation is developed with an Euler-Lagrange viewpoint. A linea…
Flow evolution in particle-laden Rayleigh-Bénard convection
Dai Shi
A theoretical analysis is carried out to study flow evolution inside the laminar Rayleigh-Bénard convection system laden with small particles. By describing particle dynamics and p…
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
Zhiqi Shao, Dai Shi, Andi Han +3
Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challe…
Bregman Graph Neural Network
Jiayu Zhai, Lequan Lin, Dai Shi +1
Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption. However, in node cl…
Frameless Graph Knowledge Distillation
Dai Shi, Zhiqi Shao, Yi Guo +1
Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be acco…