4 papers
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems
Zhen Chen, Yong Liao, Youpeng Zhao +2
Cooperative Multi-Agent Reinforcement Learning (CMARL) strategies are well known to be vulnerable to adversarial perturbations. Previous works on adversarial attacks have primarily…
GIMM: InfoMin-Max for Automated Graph Contrastive Learning
Xin Xiong, Furao Shen, Xiangyu Wang +1
Graph contrastive learning (GCL) shows great potential in unsupervised graph representation learning. Data augmentation plays a vital role in GCL, and its optimal choice heavily de…
Quantum memory error correction computation based on Chamon model
Jian Zhao, Yu-Chun Wu, Guo-Ping Guo
Quantum error correction codes play a central role in the realisation of fault-tolerant quantum computing. Chamon model is a 3D generalization of the toric code. The error correcti…
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance
Xiaoliang Liu, Furao Shen, Jian Zhao +1
Deep neural networks are powerful, but they also have shortcomings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliabilit…