18 citations · 43 across the 5 of their papers we have counts for
5 papers
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…
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Xiaowei Huang, Wenjie Ruan, Wei Huang +14
Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many…
Randomized Adversarial Training via Taylor Expansion
Gaojie Jin, Xinping Yi, Dengyu Wu +2
In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the m…
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
Dengyu Wu, Gaojie Jin, Han Yu +2
Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvem…
A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network from Convolutional Neural Network
Dengyu Wu, Xinping Yi, Xiaowei Huang
Spiking neural networks (SNNs) offer an inherent ability to process spatial-temporal data, or in other words, realworld sensory data, but suffer from the difficulty of training hig…