6 papers
A Neural Network Framework for High-Dimensional Dynamic Unbalanced Optimal Transport
Wei Wan, Jiangong Pan, Yuejin Zhang +2
In this paper, we introduce a neural network-based method to address the high-dimensional dynamic unbalanced optimal transport (UOT) problem. Dynamic UOT focuses on the optimal tra…
Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering
Huaming Ling, Chenglong Bao, Jiebo Song +1
In this paper, we introduce a Fast and Scalable Semi-supervised Multi-view Subspace Clustering (FSSMSC) method, a novel solution to the high computational complexity commonly found…
Interface Laplace Learning: Learnable Interface Term Helps Semi-Supervised Learning
Tangjun Wang, Chenglong Bao, Zuoqiang Shi
We introduce a novel framework, called Interface Laplace learning, for graph-based semi-supervised learning. Motivated by the observation that an interface should exist between dif…
Convection-Diffusion Equation: A Theoretically Certified Framework for Neural Networks
Tangjun Wang, Chenglong Bao, Zuoqiang Shi
In this paper, we study the partial differential equation models of neural networks. Neural network can be viewed as a map from a simple base model to a complicate function. Based…
Reconstruction of dynamical systems from data without time labels
Zhijun Zeng, Pipi Hu, Chenglong Bao +2
In this paper, we study the method to reconstruct dynamical systems from data without time labels. Data without time labels appear in many applications, such as molecular dynamics,…
Riemannian Anderson Mixing Methods for Minimizing -Functions on Riemannian Manifolds
Zanyu Li, Chenglong Bao
The Anderson Mixing (AM) method is a popular approach for accelerating fixed-point iterations by leveraging historical information from previous steps. In this paper, we introduce…