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pro vyhledávání: '"Yao, Man"'
Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are currently limited to simple classification tasks because of their poor performance. In this
Externí odkaz:
http://arxiv.org/abs/2407.20708
Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poisson coding are more robust than Artificial Neural Networks (ANNs) on
Externí odkaz:
http://arxiv.org/abs/2407.20099
Brain-inspired Spiking Neural Network (SNN) has demonstrated its effectiveness and efficiency in vision, natural language, and speech understanding tasks, indicating their capacity to "see", "listen", and "read". In this paper, we design \textbf{Spik
Externí odkaz:
http://arxiv.org/abs/2408.00788
Autor:
Hu, JiaKui, Yao, Man, Qiu, Xuerui, Chou, Yuhong, Cai, Yuxuan, Qiao, Ning, Tian, Yonghong, XU, Bo, Li, Guoqi
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods cannot simultaneously solve both training and inference dilemmas. This w
Externí odkaz:
http://arxiv.org/abs/2405.16466
Autor:
Yao, Man, Hu, Jiakui, Hu, Tianxiang, Xu, Yifan, Zhou, Zhaokun, Tian, Yonghong, Xu, Bo, Li, Guoqi
Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the current mainstream of neuromorphic computing. By contrast, no neuromorp
Externí odkaz:
http://arxiv.org/abs/2404.03663
Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SNNs are sparse firing, the analysis and optimization of inherent redund
Externí odkaz:
http://arxiv.org/abs/2308.08227
Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option due to their unique spike-based event-driven (i.e., spike-driven) paradigm. In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike
Externí odkaz:
http://arxiv.org/abs/2307.01694
The Lottery Ticket Hypothesis (LTH) states that a randomly-initialized large neural network contains a small sub-network (i.e., winning tickets) which, when trained in isolation, can achieve comparable performance to the large network. LTH opens up a
Externí odkaz:
http://arxiv.org/abs/2305.12148
Autor:
He Yu, Zeng Zhaojun, Lu Songjie, Liu Junqiao, Fan Hanying, Jing Lin, Wang Suzhen, Yao Man, Shu Jing, Zeng Liuzhi
Publikováno v:
Guoji Yanke Zazhi, Vol 24, Iss 6, Pp 848-856 (2024)
AIM:To observe the anti-scarring effects and safety of triamcinolone acetonide(TA)-loaded hydrogel sustained-release sheeting on stab incision glaucoma surgery(SIGS)with “one-step tunnel method” in rabbit eyes.METHODS:A total of 48 healthy New Ze
Externí odkaz:
https://doaj.org/article/8c035196271d464e9033821bb324fe1f
Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption characteristics. However, the event stream output from event cameras has asynchrono
Externí odkaz:
http://arxiv.org/abs/2211.12156