A Little Energy Goes a Long Way: Energy-Efficient, Accurate Conversion from Convolutional Neural Networks to Spiking Neural Networks. - Dengyu Wu; Xinping Yi; Xiaowei Huang 001 (CoRR, 2021)

Randomized Adversarial Training via Taylor Expansion. - Gaojie Jin; Xinping Yi; Dengyu Wu; Ronghui Mu; Xiaowei Huang 001 (CVPR, 2023)

Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff. - Dengyu Wu; Gaojie Jin; Han Yu 010; Xinping Yi; Xiaowei Huang 001 (CoRR, 2023)

Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network. - Dengyu Wu; Yi Qi; Kaiwen Cai; Gaojie Jin; Xinping Yi; Xiaowei Huang 001 (CoRR, 2024)

Neuromorphic Wireless Split Computing with Multi-Level Spikes. - Dengyu Wu; Jiechen Chen; Bipin Rajendran; H. Vincent Poor; Osvaldo Simeone (CoRR, 2024)

A survey of safety and trustworthiness of large language models through the lens of verification and validation. - Xiaowei Huang 001; Wenjie Ruan; Wei Huang 035; Gaojie Jin; Yi Dong 002; Changshun Wu; Saddek Bensalem; Ronghui Mu; Yi Qi; Xingyu Zhao 001; Kaiwen Cai; Yanghao Zhang; et al. (Artif. Intell. Rev., 2024)

High-cycle fatigue life prediction for fan blades considering aleatory and epistemic uncertainty with random damage. - Xiaodong Zhang; Dengyu Wu; Zhenhong Deng; Siqi Wang; Wujun Feng; Huageng Luo; Baoqiang Zhang (Reliab. Eng. Syst. Saf., 2025)

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons. - Dengyu Wu; Jiechen Chen; H. Vincent Poor; Bipin Rajendran; Osvaldo Simeone (CoRR, 2025)