Yuanhong Tang — accepted AI-conference papers

2026

Rethinking SNN Online Training and Deployment: Gradient-Coherent Learning via Hybrid-Driven LIF Model

Zecheng Hao, Yifan Huang, Zijie Xu, Wenxuan Liu, Yuanhong Tang, Zhaofei Yu +1

CVPR 2026

Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence due to their brain-inspired and energy-efficient properties. Compared to vanilla Spatial-Temporal Back-propagation (STBP) training methods, online training can effectively av

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2025

Differential Coding for Training-Free ANN-to-SNN Conversion

Zihan Huang, Wei Fang, Tong Bu, Peng Xue, Zecheng Hao, Wenxuan Liu +3

ICML 2025poster

Spiking Neural Networks (SNNs) exhibit significant potential due to their low energy consumption. Converting Artificial Neural Networks (ANNs) to SNNs is an efficient way to achieve high-performance SNNs. However, many conversion methods are based on rate coding, which requires numerous spikes and l…

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