Incorporating learnable membrane time constant to enhance learning of spiking neural networks W Fang, Z Yu, Y Chen, T Masquelier, T Huang, Y Tian Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 656 | 2021 |
Deep residual learning in spiking neural networks W Fang, Z Yu, Y Chen, T Huang, T Masquelier, Y Tian Advances in Neural Information Processing Systems 34, 21056-21069, 2021 | 517 | 2021 |
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence W Fang, Y Chen, J Ding, Z Yu, T Masquelier, D Chen, L Huang, H Zhou, ... Science Advances 9 (40), eadi1480, 2023 | 204 | 2023 |
SpikingJelly (GitHub) W Fang, Y Chen, J Ding, D Chen, Z Yu, H Zhou, Y Tian | 187* | 2020 |
Pruning of Deep Spiking Neural Networks through Gradient Rewiring Y Chen, Z Yu, W Fang, T Huang, Y Tian Proceedings of the Thirtieth International Joint Conference on Artificial …, 2021 | 66 | 2021 |
Parallel spiking neurons with high efficiency and ability to learn long-term dependencies W Fang, Z Yu, Z Zhou, D Chen, Y Chen, Z Ma, T Masquelier, Y Tian Advances in Neural Information Processing Systems 36, 2024 | 38 | 2024 |
State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks Y Chen, Z Yu, W Fang, Z Ma, T Huang, Y Tian International Conference on Machine Learning, 3701-3715, 2022 | 38 | 2022 |
A Unified Framework for Soft Threshold Pruning Y Chen, Z Ma, W Fang, X Zheng, Z Yu, Y Tian The Eleventh International Conference on Learning Representations, 2023 | 22 | 2023 |