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Wenjie Wei

17 papers hereh-index 11309 citations35 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author11

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV7
  • cs.NE4
  • cs.LG3
  • cs.AR1
  • cs.SD1
  • eess.SP1
same name
  • Wenjie Wei — 1 paper, h 6
  • Wenjie Wei — 1 paper, h 4
  • Wenjie Wei — 1 paper, h 2
  • Wenjie Wei — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedEvent-Driven Learning for Spiking Neural Networks

14 citations · 18 across the 15 of their papers we have counts for

collaborators
Showing cs.NEShow all

4 papers · 1 filter

cs.NE2025

Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks

Jieyuan Zhang, Xiaolong Zhou, Shuai Wang +6

Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computatio…

cs.NE2025

SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks

Yimeng Shan, Zhenbang Ren, Haodi Wu +11

Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through di…

cs.NE2025

Memory-Free and Parallel Computation for Quantized Spiking Neural Networks

Dehao Zhang, Shuai Wang, Yichen Xiao +4

Quantized Spiking Neural Networks (QSNNs) offer superior energy efficiency and are well-suited for deployment on resource-limited edge devices. However, limited bit-width weight an…

cs.NE2024★ 14 cited

Event-Driven Learning for Spiking Neural Networks

Wenjie Wei, Malu Zhang, Jilin Zhang +7

Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on ne…

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