3 citations · 5 across the 10 of their papers we have counts for
6 papers · 1 filter
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…