| Reducing ANN-SNN Conversion Error through Residual Membrane Potential Z Hao, T Bu, J Ding, T Huang, Z Yu AAAI 2023, Oral Presentation, 11-21, 2023 | 44 | 2023 |
| Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes Z Hao, J Ding, T Bu, T Huang, Z Yu ICLR 2023, 2023 | 35 | 2023 |
| Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks T Bu, J Ding, Z Hao, Z Yu CVPR 2023, 7896-7906, 2023 | 13 | 2023 |
| Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework X Shi, J Ding, Z Hao, Z Yu ICLR 2024, Spotlight, 2024 | 8 | 2024 |
| SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks X Shi, Z Hao, Z Yu CVPR 2024, 2024 | 4 | 2024 |
| A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model Z Hao, X Shi, Z Huang, T Bu, Z Yu, T Huang ICLR 2024, 2024 | 3 | 2024 |
| Threaten Spiking Neural Networks through Combining Rate and Temporal Information Z Hao, T Bu, X Shi, Z Huang, Z Yu, T Huang ICLR 2024, 2024 | 2 | 2024 |
| Towards High-performance Spiking Transformers from ANN to SNN Conversion Z Huang, X Shi, Z Hao, T Bu, J Ding, Z Yu, T Huang ACM Multimedia 2024, 2024 | 1 | 2024 |
| Enhancing Adversarial Robustness in SNNs with Sparse Gradients Y Liu, T Bu, J Ding, Z Hao, T Huang, Z Yu ICML 2024, 2024 | | 2024 |
| LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model Z Hao, X Shi, Z Pan, Y Liu, Z Yu, T Huang arXiv preprint arXiv:2402.00411, 2024 | | 2024 |
Zhaofei YuPeking UniversityVerified email at pku.edu.cn
