Junbin Gao

Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications

2021 · arXiv (Cornell University)

The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., data are not observed over equally-spaced time intervals. In multi-dimensional …

Domain Adaptation as Optimal Transport on Grassmann Manifolds

2022 · IEEE Transactions on Neural Networks and Learning Systems

Domain adaptation in the Euclidean space is a challenging task on which researchers recently have made great progress. However, in practice, there are rich data representations that are not Euclidean. For example, many high-dimensional data …

Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion

2023 · arXiv (Cornell University)

This paper presents a comprehensive theoretical analysis of the graph p-Laplacian regularized framelet network (pL-UFG) to establish a solid understanding of its properties. We conduct a convergence analysis on pL-UFG, addressing the gap in the …

Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond

2023 · arXiv (Cornell University)

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 challenges such as over-smoothing, over-squashing, and limited expressive power continue to …