4 citations · 16 across the 12 of their papers we have counts for
12 papers
Physics-Informed Generator-Encoder Adversarial Networks with Latent Space Matching for Stochastic Differential Equations
Ruisong Gao, Min Yang, Jin Zhang
We propose a new class of physics-informed neural networks, called Physics-Informed Generator-Encoder Adversarial Networks, to effectively address the challenges posed by forward,…
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations
Ruisong Gao, Yufeng Wang, Min Yang +1
We present a new category of physics-informed neural networks called physics informed variational embedding generative adversarial network (PI-VEGAN), that effectively tackles the…
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…
PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data
Yufeng Wang, Dan Li, Xiang Li +1
Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform…
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…
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
Cheng Cui, Zhi Ye, Yangxi Li +7
Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adop…
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…