Attention Spiking Neural Networks-Reference-Cited by-同舟云学术

Attention Spiking Neural Networks

Author:

Yao Man¹^ORCID,Zhao Guangshe²^ORCID,Zhang Hengyu³^ORCID,Hu Yifan⁴^ORCID,Deng Lei⁴^ORCID,Tian Yonghong⁵^ORCID,Xu Bo⁶^ORCID,Li Guoqi⁶^ORCID

Affiliation:

1. School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China

2. School of Automation Science and Engeneering, Xi’an Jiaotong University, Xi’an, Shaanxi, China

3. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China

4. Department of Precision Instrument, Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, China

5. Institute for Artificial Intelligence, Peking University, Beijing, China

6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

Beijing Natural Science Foundation for Distinguished Young Scholars

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference63 articles.

2. Temporal efficient training of spiking neural network via gradient re-weighting;deng;Proc Int Conf Learn Representations,2022

3. Advancing residual learning towards powerful deep spiking neural networks;hu,2021

5. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,2017

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