Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunities

13 November 2023

Papers citing "Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunities"

8 / 8 papers shown

Title
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks Bojian Yin Federico Corradi S. Bohté 73 215 0 12 Mar 2021
A Survey on Deep Semi-supervised Learning Xiangli Yang Zixing Song Irwin King Zenglin Xu 85 579 0 28 Feb 2021
Deep Residual Learning in Spiking Neural Networks Wei Fang Zhaofei Yu Yanqing Chen Tiejun Huang T. Masquelier Yonghong Tian 152 493 0 08 Feb 2021
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks Wei Fang Zhaofei Yu Yanqing Chen T. Masquelier Tiejun Huang Yonghong Tian 128 521 0 11 Jul 2020
Self-supervised Learning: Generative or Contrastive Xiao Liu Fanjin Zhang Zhenyu Hou Zhaoyu Wang Li Mian Jing Zhang Jie Tang SSL 119 1,612 0 15 Jun 2020
A comprehensive review on convolutional neural network in machine fault diagnosis Jinyang Jiao Ming Zhao Jing Lin Kaixuan Liang 9 414 0 13 Feb 2020
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis Tianfu Li Zhibin Zhao Chuang Sun Li Cheng Xuefeng Chen Ruqaing Yan Ruize Gao 39 325 0 12 Nov 2019
Surrogate Gradient Learning in Spiking Neural Networks Emre Neftci Hesham Mostafa Friedemann Zenke 84 1,234 0 28 Jan 2019