no code implementations • 17 Mar 2024 • Yuetong Fang, Ziqing Wang, Lingfeng Zhang, Jiahang Cao, Honglei Chen, Renjing Xu
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
1 code implementation • 24 Nov 2023 • Ziqing Wang, Yuetong Fang, Jiahang Cao, Renjing Xu
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs).
1 code implementation • 1 Jul 2023 • Ziqing Wang, Qidong Zhao, Jinku Cui, Xu Liu, Dongkuan Xu
To address these limitations, we introduce AutoST, a training-free NAS method for Spiking Transformers, to rapidly identify high-performance Spiking Transformer architectures.
1 code implementation • 29 Jun 2023 • Jiahang Cao, Ziqing Wang, Hanzhong Guo, Hao Cheng, Qiang Zhang, Renjing Xu
In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality.
1 code implementation • 15 Oct 2022 • Ziqing Wang, Zhirong Ye, Yuyang Du, Yi Mao, Yanying Liu, Ziling Wu, Jun Wang
DBSCAN has been widely used in density-based clustering algorithms.
1 code implementation • ICCV 2023 • Ziqing Wang, Yuetong Fang, Jiahang Cao, Qiang Zhang, Zhongrui Wang, Renjing Xu
The combination of Spiking Neural Networks (SNNs) and Transformers has attracted significant attention due to their potential for high energy efficiency and high-performance nature.
no code implementations • 27 May 2021 • Ziqing Wang, Mohammad Ali Armin, Simon Denman, Lars Petersson, David Ahmedt-Aristizabal
Inpatient falls are a serious safety issue in hospitals and healthcare facilities.