39 citations · 78 across the 10 of their papers we have counts for
10 papers
On the Sparse DAG Structure Learning Based on Adaptive Lasso
Danru Xu, Erdun Gao, Wei Huang +3
Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidenti…
Enhancing Adversarial Training with Second-Order Statistics of Weights
Gaojie Jin, Xinping Yi, Wei Huang +2
Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over mo…
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
Wei Huang, Chunrui Liu, Yilan Chen +2
PAC-Bayesian is an analysis framework where the training error can be expressed as the weighted average of the hypotheses in the posterior distribution whilst incorporating the pri…
Tutorials on Testing Neural Networks
Nicolas Berthier, Youcheng Sun, Wei Huang +3
Deep learning achieves remarkable performance on pattern recognition, but can be vulnerable to defects of some important properties such as robustness and security. This tutorial i…
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
Xingyu Zhao, Wei Huang, Alec Banks +4
The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications. While it shows great potential to provide transformational capabilities, DL al…
Detecting Operational Adversarial Examples for Reliable Deep Learning
Xingyu Zhao, Wei Huang, Sven Schewe +2
The utilisation of Deep Learning (DL) raises new challenges regarding its dependability in critical applications. Sound verification and validation methods are needed to assure the…