The following publications are possibly variants of this publication:
- Adversarial robustness of deep neural networks: A survey from a formal verification perspectiveMeng, Mark Huasong, Bai, Guangdong, Teo, Sin Gee, Hou, Zhe, Xiao, Yan, Lin, Yun, Dong, Jin Song. IEEE Transactions on Dependable and Secure Computing, , 2022.
- Optimizing Efficient Personalized Federated Learning with Hypernetworks at EdgeRongyu Zhang, Yun Chen, Chenrui Wu, Fangxin Wang 0001, Jiangchuan Liu. network, 37(4):120-126, July / August 2023. [doi]
- Federated Deep Learning Meets Autonomous Vehicle Perception: Design and VerificationShuai Wang 0004, Chengyang Li, Derrick Wing Kwan Ng, Yonina C. Eldar, H. Vincent Poor, Qi Hao, Cheng-Zhong Xu 0001. network, 37(3):16-25, May / June 2023. [doi]
- A Genetic Learning Algorithm for Generating a Parsimonious Functional Link NetworkC. L. Philip Chen, Chandrakumar Bhumireddy. pet 2003: 179-184
- Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G NetworksChangyang She, Rui Dong 0001, Zhouyou Gu, Zhanwei Hou, Yonghui Li 0001, Wibowo Hardjawana, Chenyang Yang, Lingyang Song, Branka Vucetic. network, 34(5):219-225, 2020. [doi]
- A Dropconnect Deep Computation Model for Highly Heterogeneous Data Feature Learning in Mobile Sensing NetworksQingchen Zhang, Laurence T. Yang, Zhikui Chen, Peng Li. network, 32(4):22-27, 2018. [doi]
- Multi-User QoE Enhancement: Federated Multi-Agent Reinforcement Learning for Cooperative Edge IntelligenceXiuhua Li, Chuan Sun, Junhao Wen, Xiaofei Wang 0001, Mohsen Guizani, Victor C. M. Leung. network, 36(5):144-151, 2022. [doi]
- Congestion Control in SDN-Based Networks via Multi-Task Deep Reinforcement LearningKai Lei, Yuzhi Liang, Wei Li. network, 34(4):28-34, 2020. [doi]