3 citations · 7 across the 5 of their papers we have counts for
5 papers
Improving Generalization of Deep Neural Networks by Optimum Shifting
Yuyan Zhou, Ye Li, Lei Feng +1
Recent studies showed that the generalization of neural networks is correlated with the sharpness of the loss landscape, and flat minima suggests a better generalization ability th…
Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations
Siqi Chen, Bin Shan, Ye Li
Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficul…
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
Ye Li, Song-Can Chen, Sheng-Jun Huang
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training fa…
VI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations
Bin Shan, Ye Li, Shengjun Huang
Although physics-informed neural networks(PINNs) have progressed a lot in many real applications recently, there remains problems to be further studied, such as achieving more accu…
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
Se-In Jang, Tinsu Pan, Ye Li +4
Position emission tomography (PET) is widely used in clinics and research due to its quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR). Rec…