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Huajin Tang

4 papers hereh-index 6158 citations25 works total

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

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.NE2
  • cs.CV1
  • cs.LG1
same name
  • Huajin Tang — 10 papers, h 9
  • Huajin Tang — 2 papers, h 3
  • Huajin Tang — 2 papers, h 2
  • Huajin Tang — 2 papers, h 2
  • Huajin Tang — 1 paper, h 1
  • Huajin Tang — 1 paper, h 1

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

collaborators

4 papers

cs.LG2025

MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization

Runhao Jiang, Chengzhi Jiang, Rui Yan +1

The surrogate gradient (SG) method has shown significant promise in enhancing the performance of deep spiking neural networks (SNNs), but it also introduces vulnerabilities to adve…

cs.NE2025

ASRC-SNN: Adaptive Skip Recurrent Connection Spiking Neural Network

Shang Xu, Jiayu Zhang, Ziming Wang +3

In recent years, Recurrent Spiking Neural Networks (RSNNs) have shown promising potential in long-term temporal modeling. Many studies focus on improving neuron models and also int…

cs.CV2024

EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks

Ziming Wang, Ziling Wang, Huaning Li +4

Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection, especially under scenarios with motion blur and challenging lighting…

cs.NE2024

GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL

Lang Qin, Ziming Wang, Runhao Jiang +2

Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can…

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