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ORCIDJianhao Ding
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2020 – today
- 2024
[j3]Yuhong Zhang, Jianhao Ding, Jieyue Yu:
Art image inpainting via embedding multiple attention dilated convolutions. Multim. Tools Appl. 83(12): 36455-36468 (2024)- [c18]Jianhao Ding, Zhaofei Yu, Tiejun Huang, Jian K. Liu
:
Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms. AAAI 2024: 492-502 - [c17]Yujia Liu, Chenxi Yang, Dingquan Li, Jianhao Ding, Tingting Jiang:
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization. CVPR 2024: 25554-25563 - [c16]Xinyu Shi, Jianhao Ding, Zecheng Hao, Zhaofei Yu:
Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework. ICLR 2024 - [c15]Yaoyu Zhu, Jianhao Ding, Tiejun Huang, Xiaodong Xie, Zhaofei Yu:
Online Stabilization of Spiking Neural Networks. ICLR 2024 - [c14]Jianhao Ding, Zhiyu Pan, Yujia Liu, Zhaofei Yu, Tiejun Huang:
Robust Stable Spiking Neural Networks. ICML 2024 - [c13]Yujia Liu, Tong Bu, Jianhao Ding, Zecheng Hao, Tiejun Huang, Zhaofei Yu:
Enhancing Adversarial Robustness in SNNs with Sparse Gradients. ICML 2024 - [c12]Xian Zhong, Shengwang Hu, Wenxuan Liu, Wenxin Huang, Jianhao Ding, Zhaofei Yu, Tiejun Huang:
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks. ACM Multimedia 2024: 9828-9836 - [c11]Zihan Huang, Xinyu Shi, Zecheng Hao, Tong Bu, Jianhao Ding, Zhaofei Yu, Tiejun Huang:
Towards High-performance Spiking Transformers from ANN to SNN Conversion. ACM Multimedia 2024: 10688-10697 - [i14]Yujia Liu, Chenxi Yang, Dingquan Li, Jianhao Ding, Tingting Jiang:
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization. CoRR abs/2403.11397 (2024) - [i13]Zhipeng Huang, Jianhao Ding, Zhiyu Pan, Haoran Li, Ying Fang, Zhaofei Yu, Jian K. Liu:
Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs. CoRR abs/2404.17456 (2024) - [i12]Yujia Liu, Tong Bu, Jianhao Ding, Zecheng Hao, Tie-Jun Huang, Zhaofei Yu:
Enhancing Adversarial Robustness in SNNs with Sparse Gradients. CoRR abs/2405.20355 (2024) - [i11]Jianhao Ding, Zhiyu Pan, Yujia Liu, Zhaofei Yu, Tie-Jun Huang:
Robust Stable Spiking Neural Networks. CoRR abs/2405.20694 (2024) - [i10]Xian Zhong, Shengwang Hu, Wenxuan Liu, Wenxin Huang, Jianhao Ding, Zhaofei Yu, Tiejun Huang:
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks. CoRR abs/2409.12507 (2024) - 2023
- [c10]Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, Zhaofei Yu:
Reducing ANN-SNN Conversion Error through Residual Membrane Potential. AAAI 2023: 11-21 - [c9]Guishan Xiang, Yong Liang, Jianhao Ding, Zhijie Lin:
3D Scene Modeling Method based on Sparse Representation. APIT 2023: 45-48 - [c8]Tong Bu, Jianhao Ding, Zecheng Hao, Zhaofei Yu:
Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks. CVPR 2023: 7896-7906 - [c7]Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu:
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes. ICLR 2023 - [i9]Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, Zhaofei Yu:
Reducing ANN-SNN Conversion Error through Residual Membrane Potential. CoRR abs/2302.02091 (2023) - [i8]Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu:
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes. CoRR abs/2302.10685 (2023) - [i7]Tong Bu, Wei Fang, Jianhao Ding, Penglin Dai, Zhaofei Yu, Tiejun Huang:
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks. CoRR abs/2303.04347 (2023) - [i6]Yajing Zheng, Jiyuan Zhang, Rui Zhao, Jianhao Ding, Shiyan Chen, Ruiqin Xiong, Zhaofei Yu, Tiejun Huang:
SpikeCV: Open a Continuous Computer Vision Era. CoRR abs/2303.11684 (2023) - [i5]Jianhao Ding, Zhaofei Yu, Tiejun Huang, Jian K. Liu:
Spike timing reshapes robustness against attacks in spiking neural networks. CoRR abs/2306.05654 (2023) - [i4]Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timothée Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, Yonghong Tian:
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence. CoRR abs/2310.16620 (2023) - 2022
- [c6]Tong Bu, Jianhao Ding, Zhaofei Yu, Tiejun Huang:
Optimized Potential Initialization for Low-Latency Spiking Neural Networks. AAAI 2022: 11-20 - [c5]Tong Bu, Wei Fang, Jianhao Ding, Penglin Dai, Zhaofei Yu, Tiejun Huang:
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks. ICLR 2022 - [c4]Jianhao Ding, Tong Bu, Zhaofei Yu, Tiejun Huang, Jian K. Liu:
SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training. NeurIPS 2022 - [c3]Chaoteng Duan, Jianhao Ding, Shiyan Chen, Zhaofei Yu, Tiejun Huang:
Temporal Effective Batch Normalization in Spiking Neural Networks. NeurIPS 2022 - [i3]Tong Bu, Jianhao Ding, Zhaofei Yu, Tiejun Huang:
Optimized Potential Initialization for Low-latency Spiking Neural Networks. CoRR abs/2202.01440 (2022) - 2021
- [c2]Jianhao Ding, Zhaofei Yu, Yonghong Tian, Tiejun Huang:
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks. IJCAI 2021: 2328-2336 - [i2]Jianhao Ding, Zhaofei Yu, Yonghong Tian, Tiejun Huang:
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks. CoRR abs/2105.11654 (2021) - 2020
- [i1]Jianhao Ding, Lansheng Han:
A Fixed point view: A Model-Based Clustering Framework. CoRR abs/2002.08032 (2020)
2010 – 2019
- 2018
- [j2]Jianhao Ding, Lansheng Han, Dan Li:
An adaptive control momentum method as an optimizer in the cloud. Future Gener. Comput. Syst. 89: 192-200 (2018) - [c1]Jianhao Ding, Shuai Xiong, Yifan Liu, Dazhi He, Wenjun Zhang, Kaikai Wang:
System Design of ATSC3.0 Broadcast Gateway Based on CPU-FPGA. VTC Fall 2018: 1-5 - 2013
- [j1]Jianhao Ding, Yigang Wang, Weidong Geng:
An HOG-CT human detector with histogram-based search. Multim. Tools Appl. 63(3): 791-807 (2013)
Coauthor Index
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last updated on 2024-11-07 21:35 CET by the dblp team
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