3 papers
cs.CV2025
Adaptive Distribution-aware Quantization for Mixed-Precision Neural Networks
Shaohang Jia, Zhiyong Huang, Zhi Yu +3
Quantization-Aware Training (QAT) is a critical technique for deploying deep neural networks on resource-constrained devices. However, existing methods often face two major challen…
cs.LG2025
Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
Junyuan Fang, Han Yang, Haixian Wen +3
Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent…
cs.LG2025
Mitigating the Structural Bias in Graph Adversarial Defenses
Junyuan Fang, Huimin Liu, Han Yang +3
In recent years, graph neural networks (GNNs) have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that cur…