142 citations · 404 across the 25 of their papers we have counts for
25 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…
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…
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
Chong Peng, Qian Zhang, Zhao Kang +2
Subspace clustering methods have been widely studied recently. When the inputs are 2-dimensional (2D) data, existing subspace clustering methods usually convert them into vectors,…
Fractal Autoencoders for Feature Selection
Xinxing Wu, Qiang Cheng
Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised fe…