7 papers · 1 filter
S2O: Enhancing Adversarial Training with Second-Order Statistics of Weights
Gaojie Jin, Xinping Yi, Wei Huang +2
Adversarial training has emerged as a highly effective way to improve the robustness of deep neural networks (DNNs). It is typically conceptualized as a min-max optimization proble…
Reconcile Certified Robustness and Accuracy for DNN-based Smoothed Majority Vote Classifier
Gaojie Jin, Xinping Yi, Xiaowei Huang
Within the PAC-Bayesian framework, the Gibbs classifier (defined on a posterior ) and the corresponding -weighted majority vote classifier are commonly used to analyze the ge…
Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments
Gaojie Jin, Ronghui Mu, Xinping Yi +2
The Invariant Risk Minimization (IRM) approach aims to address the challenge of domain generalization by training a feature representation that remains invariant across multiple en…
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
Ganlin Liu, Ziling Liang, Xiaowei Huang +2
Despite impressive capability in learning over graph-structured data, graph neural networks (GNN) suffer from adversarial topology perturbation in both training and inference phase…
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks
Jiaxu Liu, Xinping Yi, Xiaowei Huang
Hyperbolic graph convolutional networks (HGCNs) have demonstrated significant potential in extracting information from hierarchical graphs. However, existing HGCNs are limited to s…
Symplectic Structure-Aware Hamiltonian (Graph) Embeddings
Jiaxu Liu, Xinping Yi, Tianle Zhang +1
In traditional Graph Neural Networks (GNNs), the assumption of a fixed embedding manifold often limits their adaptability to diverse graph geometries. Recently, Hamiltonian system-…