35 citations · 36 across the 3 of their papers we have counts for
5 papers
Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks
Andrew Cheng, Ali Eslamian, Jie Cheng +2
Neural networks can often be trained or fine-tuned through random low-dimensional reparameterization, where a small latent vector is mapped into a full parameter update by a frozen…
GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture
Md Atik Ahamed, Andrew Cheng, Qiang Ye +1
Graph Neural Networks (GNNs) have demonstrated remarkable success in various applications, yet they often struggle to capture long-range dependencies (LRD) effectively. This paper…
Structured Regularization for Constrained Optimization on the SPD Manifold
Andrew Cheng, Melanie Weber
Matrix-valued optimization tasks, including those involving symmetric positive definite (SPD) matrices, arise in a wide range of applications in machine learning, data science and…
Disciplined Geodesically Convex Programming
Andrew Cheng, Vaibhav Dixit, Melanie Weber
Convex programming plays a fundamental role in machine learning, data science, and engineering. Testing convexity structure in nonlinear programs relies on verifying the convexity…
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
Chong Peng, Yang Liu, Yongyong Chen +5
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximatio…