Deep residual learning in spiking neural networks. Chen Y, Fang W, Huang T, Yu Z. W Fang, Z Yu, Y Chen, T Huang… - Advances in Neural …, 2021 - proceedings.neurips.cc GSID: n0tpYVnpgb8J
Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks. Mostafa H, Neftci EO, Zenke F. EO Neftci, H Mostafa, F Zenke - IEEE Signal Processing …, 2019 - ieeexplore.ieee.org GSID: ALadFSudUYoJ
Differentiable spike: Rethinking gradient-descent for training spiking neural networks. Deng S, Guo Y, Li Y, Zhang S. Y Li, Y Guo, S Zhang, S Deng… - Advances in Neural …, 2021 - proceedings.neurips.cc GSID: X93ovC2WS4kJ
Temporal efficient training of spiking neural network via gradient re-weighting. Deng S, Gu S, Li Y, Zhang S. S Deng, Y Li, S Zhang, S Gu - arXiv preprint arXiv:2202.11946, 2022 - arxiv.org GSID: eUX0dv-34WYJ
Neural architecture search for spiking neural networks. Kim Y, Li Y, Panda P, Park H, Venkatesha Y. Y Kim, Y Li, H Park, Y Venkatesha, P Panda - European Conference on …, 2022 - Springer GSID: oreFSlA8EsMJ
IM-loss: information maximization loss for spiking neural networks. Guo Y. Y Guo, Y Chen, L Zhang, X Liu… - Advances in …, 2022 - proceedings.neurips.cc GSID: 14RFl_3b55MJ