| 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 | 567 | 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 | 430 | 2021 |
| Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks T Bu, W Fang, J Ding, PL Dai, Z Yu, T Huang International Conference on Learning Representations, 2022 | 195 | 2022 |
| SpikingJelly (GitHub) W Fang, Y Chen, J Ding, D Chen, Z Yu, H Zhou, Y Tian | 178* | 2020 |
| 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 | 128 | 2023 |
| 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 | 60 | 2021 |
| Training spiking neural networks with event-driven backpropagation Y Zhu, Z Yu, W Fang, X Xie, T Huang, T Masquelier Advances in Neural Information Processing Systems 35, 30528-30541, 2022 | 39 | 2022 |
| 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 | 33 | 2022 |
| 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, 2023 | 32* | 2023 |
| 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 | 17 | 2023 |
| Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip M Yao, O Richter, G Zhao, N Qiao, Y Xing, D Wang, T Hu, W Fang, ... Nature Communications 15 (1), 4464, 2024 | 16 | 2024 |
| Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks Y Zhu, W Fang, X Xie, T Huang, Z Yu Advances in Neural Information Processing Systems, 2023 | 11 | 2023 |
| Self-architectural knowledge distillation for spiking neural networks H Qiu, M Ning, Z Song, W Fang, Y Chen, T Sun, Z Ma, L Yuan, Y Tian Neural Networks, 106475, 2024 | 3 | 2024 |
| Optimal ANN-SNN Conversion with Group Neurons L Lv, W Fang, L Yuan, Y Tian IEEE International Conference on Acoustics, Speech and Signal Processing …, 2024 | | 2024 |
Zhaofei YuPeking University在 pku.edu.cn 的电子邮件经过验证
