3 citations · 8 across the 8 of their papers we have counts for
8 papers
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems
Yufeng Wang, Cong Xu, Min Yang +1
Although Physics-Informed Neural Networks (PINNs) have been successfully applied in a wide variety of science and engineering fields, they can fail to accurately predict the underl…
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions
Yufeng Wang, Dan Li, Cong Xu +1
Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives. However, on the one hand, building a s…
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
Cong Xu, Wei Zhang, Jun Wang +1
The adversarial robustness of a neural network mainly relies on two factors: model capacity and anti-perturbation ability. In this paper, we study the anti-perturbation ability of…
Missingness Augmentation: A General Approach for Improving Generative Imputation Models
Yufeng Wang, Dan Li, Cong Xu +1
Missing data imputation is a fundamental problem in data analysis, and many studies have been conducted to improve its performance by exploring model structures and learning proced…
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks
Cong Xu, Xiang Li, Min Yang
Neural networks are susceptible to artificially designed adversarial perturbations. Recent efforts have shown that imposing certain modifications on classification layer can improv…
Adversarial Momentum-Contrastive Pre-Training
Cong Xu, Dan Li, Min Yang
Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational…
Improve Adversarial Robustness via Weight Penalization on Classification Layer
Cong Xu, Dan Li, Min Yang
It is well-known that deep neural networks are vulnerable to adversarial attacks. Recent studies show that well-designed classification parts can lead to better robustness. However…
A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections
Cong Xu, Min Yang, Jin Zhang
The implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work. In this paper, a series of subspace pr…