1 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
Yongqi Ding, Lin Zuo, Mengmeng Jing +2
Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that sha…
cs.LG2024★ 1 cited
Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing +2
Spiking neural networks (SNNs) have attracted considerable attention for their event-driven, low-power characteristics and high biological interpretability. Inspired by knowledge d…
cs.CV2024★ 1 cited
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network
Yongqi Ding, Lin Zuo, Mengmeng Jing +2
Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of low-power neuromorphic computing. However, existing SNNs suffer from significant latency,…