6 papers
Zero-Shot Size Transfer for Neural ODEs on Sparse Random Graphs: Graphon Limits and Adjoint Convergence
Mingsong Yan, Zhida Wang, Sui Tang
Graph Neural Differential Equations (GNDEs) model continuous-time graph dynamics by parameterizing Neural ODE velocity fields with Graph Neural Networks. Their local, size-independ…
Unified Ergodic Primal-Dual Gap Rates with Unhalved Primal Stepsizes
Sirong Dai, Ming Yan
We study ergodic primal-dual gap rates for first-order primal-dual methods applied to \[ \min_x f(x)+g(x)+h(Ax), \] where is smooth and convex, and are proper, closed,…
On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks
Mingsong Yan, Charles Kulick, Sui Tang
Continuous-depth graph neural networks, also known as Graph Neural Differential Equations (GNDEs), combine the structural inductive bias of Graph Neural Networks (GNNs) with the co…
Hypothesis Spaces for Deep Learning
Rui Wang, Yuesheng Xu, Mingsong Yan
This paper introduces a hypothesis space for deep learning based on deep neural networks (DNNs). By treating a DNN as a function of two variables - the input variable and the param…
Sparse Deep Learning Models with the Regularization
Lixin Shen, Rui Wang, Yuesheng Xu +1
Sparse neural networks are highly desirable in deep learning in reducing its complexity. The goal of this paper is to study how choices of regularization parameters influence the s…
Inexact FPPA for the Sparse Regularization Problem
Ronglong Fang, Yuesheng Xu, Mingsong Yan
We study inexact fixed-point proximity algorithms for solving a class of sparse regularization problems involving the norm. Specifically, the model has an objecti…