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of 2 152
pro vyhledávání: '"Sun, Pengfei"'
Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advanc
Externí odkaz:
http://arxiv.org/abs/2403.15489
Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing. Nevertheless, vanilla RNNs are confronted with the well-known issue
Externí odkaz:
http://arxiv.org/abs/2310.14982
Spiking neural networks (SNN) are a promising research avenue for building accurate and efficient automatic speech recognition systems. Recent advances in audio-to-spike encoding and training algorithms enable SNN to be applied in practical tasks. Bi
Externí odkaz:
http://arxiv.org/abs/2302.08607
Autor:
Sun, Pengfei1 (AUTHOR), Hu, Danni1 (AUTHOR), Chen, Pengfei1 (AUTHOR), Wang, Xuanzong2 (AUTHOR), Shen, Qingming1 (AUTHOR), Chen, Shangyu1 (AUTHOR) shc315@pitt.edu, Li, Daifeng2 (AUTHOR) lidaifeng@zzu.edu.cn, Fan, Quli1 (AUTHOR) iamqlfan@njupt.edu.cn
Publikováno v:
Advanced Science. 8/14/2024, Vol. 11 Issue 30, p1-14. 14p.
Spiking Neural Networks~(SNNs) are a promising research paradigm for low power edge-based computing. Recent works in SNN backpropagation has enabled training of SNNs for practical tasks. However, since spikes are binary events in time, standard loss
Externí odkaz:
http://arxiv.org/abs/2205.09845
The information of spiking neural networks (SNNs) are propagated between the adjacent biological neuron by spikes, which provides a computing paradigm with the promise of simulating the human brain. Recent studies have found that the time delay of ne
Externí odkaz:
http://arxiv.org/abs/2205.02115
Publikováno v:
In Heliyon 30 October 2024 10(20)
Publikováno v:
In Ocean Engineering 15 October 2024 310 Part 2