92 citations · 130 across the 9 of their papers we have counts for
3 papers · 1 filter
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
Zhipeng He, Chun Ouyang, Laith Alzubaidi +2
Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks ha…
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEs
Jinshuai Bai, Gui-Rong Liu, Ashish Gupta +3
Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-inform…
Utilising physics-guided deep learning to overcome data scarcity
Jinshuai Bai, Laith Alzubaidi, Qingxia Wang +3
Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challen…