41 citations · 103 across the 12 of their papers we have counts for
13 papers · 1 filter
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…
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Chong Peng, Yiqun Zhang, Yongyong Chen +3
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF,…
Top- Regularization for Supervised Feature Selection
Xinxing Wu, Qiang Cheng
Feature selection identifies subsets of informative features and reduces dimensions in the original feature space, helping provide insights into data generation or a variety of dom…
Adaptive Weighted Discriminator for Training Generative Adversarial Networks
Vasily Zadorozhnyy, Qiang Cheng, Qiang Ye
Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functio…
Structured Graph Learning for Clustering and Semi-supervised Classification
Zhao Kang, Chong Peng, Qiang Cheng +4
Graphs have become increasingly popular in modeling structures and interactions in a wide variety of problems during the last decade. Graph-based clustering and semi-supervised cla…
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
Chong Peng, Zhilu Zhang, Zhao Kang +2
In this paper, we propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously…