Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Hao, Zecheng"'
Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence (AI) due to their brain-inspired and energy-efficient properties. In the current supervised learning domain of SNNs, compare
Externí odkaz:
http://arxiv.org/abs/2410.07547
This study introduces a novel Remote Sensing (RS) Urban Prediction (UP) task focused on future urban planning, which aims to forecast urban layouts by utilizing information from existing urban layouts and planned change maps. To address the proposed
Externí odkaz:
http://arxiv.org/abs/2407.11578
Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over Artificial Neural Networks (ANNs) in terms of energy efficiency and interpret
Externí odkaz:
http://arxiv.org/abs/2405.20355
The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing meth
Externí odkaz:
http://arxiv.org/abs/2403.14302
Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more energy-efficient manner. However, despite previous efforts to
Externí odkaz:
http://arxiv.org/abs/2402.00411
Autor:
Guo, Yufei, Chen, Yuanpei, Hao, Zecheng, Peng, Weihang, Jie, Zhou, Zhang, Yuhan, Liu, Xiaode, Ma, Zhe
The Spiking Neural Network (SNN) is a biologically inspired neural network infrastructure that has recently garnered significant attention. It utilizes binary spike activations to transmit information, thereby replacing multiplications with additions
Externí odkaz:
http://arxiv.org/abs/2401.04486
Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN conversion, as the most commonly used training method for applying SNNs, can
Externí odkaz:
http://arxiv.org/abs/2302.10685
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips. Among various training methods of SNNs, ANN-SNN conversion has shown the e
Externí odkaz:
http://arxiv.org/abs/2302.02091