| 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 arXiv preprint arXiv:2303.04347, 2023 | 145 | 2023 |
| Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks J Ding, Z Yu, Y Tian, T Huang Proceedings of the Thirtieth International Joint Conference on Artificial …, 2021 | 124 | 2021 |
| Spikingjelly W Fang, Y Chen, J Ding, D Chen, Z Yu, H Zhou, Y Tian Multimedia Learn. Group, Inst. Digit. Media (NELVT), 2020 | 73 | 2020 |
| Optimized potential initialization for low-latency spiking neural networks T Bu, J Ding, Z Yu, T Huang Proceedings of the AAAI Conference on Artificial Intelligence 36 (1), 11-20, 2022 | 68 | 2022 |
| 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 | 50 | 2023 |
| Temporal effective batch normalization in spiking neural networks C Duan, J Ding, S Chen, Z Yu, T Huang Advances in Neural Information Processing Systems 35, 34377-34390, 2022 | 49 | 2022 |
| Reducing ann-snn conversion error through residual membrane potential Z Hao, T Bu, J Ding, T Huang, Z Yu Proceedings of the AAAI Conference on Artificial Intelligence 37 (1), 11-21, 2023 | 31 | 2023 |
| Bridging the gap between anns and snns by calibrating offset spikes Z Hao, J Ding, T Bu, T Huang, Z Yu arXiv preprint arXiv:2302.10685, 2023 | 24 | 2023 |
| SNN-RAT: Robustness-enhanced spiking neural network through regularized adversarial training J Ding, T Bu, Z Yu, T Huang, J Liu Advances in Neural Information Processing Systems 35, 24780-24793, 2022 | 21 | 2022 |
| Rate gradient approximation attack threats deep spiking neural networks T Bu, J Ding, Z Hao, Z Yu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 9 | 2023 |
| SpikeCV: Open a Continuous Computer Vision Era Y Zheng, J Zhang, R Zhao, J Ding, S Chen, R Xiong, Z Yu, T Huang arXiv preprint arXiv:2303.11684, 2023 | 3 | 2023 |
| An adaptive control momentum method as an optimizer in the cloud J Ding, L Han, D Li Future Generation Computer Systems 89, 192-200, 2018 | 3 | 2018 |
| Accelerating training of deep spiking neural networks with parameter initialization J Ding, J Zhang, Z Yu, T Huang | 2 | 2021 |
| Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs Z Huang, J Ding, Z Pan, H Li, Y Fang, Z Yu, JK Liu arXiv preprint arXiv:2404.17456, 2024 | | 2024 |
| Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms J Ding, Z Yu, T Huang, JK Liu Proceedings of the AAAI Conference on Artificial Intelligence 38 (1), 492-502, 2024 | | 2024 |
| Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization Y Liu, C Yang, D Li, J Ding, T Jiang arXiv preprint arXiv:2403.11397, 2024 | | 2024 |
| Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework X Shi, J Ding, Z Hao, Z Yu The Twelfth International Conference on Learning Representations, 2023 | | 2023 |
| Spike timing reshapes robustness against attacks in spiking neural networks J Ding, Z Yu, T Huang, JK Liu arXiv preprint arXiv:2306.05654, 2023 | | 2023 |
| A Fixed point view: A Model-Based Clustering Framework J Ding, L Han arXiv preprint arXiv:2002.08032, 2020 | | 2020 |
| Online Stabilization of Spiking Neural Networks Y Zhu, J Ding, T Huang, X Xie, Z Yu The Twelfth International Conference on Learning Representations, 0 | | |
Zhaofei YuPeking UniversityVerified email at pku.edu.cn
