3 citations · 5 across the 10 of their papers we have counts for
10 papers
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…
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…
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
Sihao Wu, Jiaxu Liu, Xiangyu Yin +5
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of pur…
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…
Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model Against LLM Red-Teaming
Jiaxu Liu, Xiangyu Yin, Sihao Wu +4
With the proliferation of red-teaming strategies for Large Language Models (LLMs), the deficiency in the literature about improving the safety and robustness of LLM defense strateg…
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE
Jiaxu Liu, Xinping Yi, Sihao Wu +4
While Hyperbolic Graph Neural Network (HGNN) has recently emerged as a powerful tool dealing with hierarchical graph data, the limitations of scalability and efficiency hinder itse…
Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network
Dengyu Wu, Yi Qi, Kaiwen Cai +3
Spiking Neural Network (SNN) is acknowledged as the next generation of Artificial Neural Network (ANN) and hold great promise in effectively processing spatial-temporal information…
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-…