publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2026

  1. Parallel Training Time-to-First-Spike Spiking Neural Networks
    Kaiwei Che, Wei Fang, Peng Xue, Yifan Huang, Zhengyu Ma, and Yonghong Tian
    In AAAI Conference on Artificial Intelligence, 2026
  2. Towards Lossless Memory-efficient Training of Spiking Neural Networks via Gradient Checkpointing and Spike Compression
    Yifan Huang, Wei Fang, Zecheng Hao, Zhengyu Ma, and Yonghong Tian
    In International Conference on Learning Representations, 2026
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  3. Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space
    Wei Fang and Priyadarshini Panda
    In International Conference on Machine Learning, 2026
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2025

  1. Review of Surrogate Gradient Methods in Spiking Deep Learning
    Wei Fang, Yaoyu Zhu, Zihan Huang, Man Yao, Zhaofei Yu, and Yonghong Tian
    Chinese Journal of Computers, 2025
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  2. Differential Coding for Training-Free ANN-to-SNN Conversion
    Zihan Huang, Wei Fang, Tong Bu, Peng Xue, Zecheng Hao, Wenxuan Liu, Yuanhong Tang, Zhaofei Yu, and Tiejun Huang
    In International Conference on Machine Learning, 2025
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  3. Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
    Peng Xue, Wei Fang, Zhengyu Ma, Zihan Huang, Zhaokun Zhou, Yonghong Tian, Timothée Masquelier, and Huihui Zhou
    In Advances in Neural Information Processing Systems, 2025
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2024

  1. Optimal ANN-SNN Conversion with Group Neurons
    Liuzhenghao Lv, Wei Fang, Li Yuan, and Yonghong Tian
    In IEEE International Conference on Acoustics, Speech and Signal Processing, 2024
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  2. Spike-based Dynamic Computing with Asynchronous Sensing-Computing Neuromorphic Chip
    Man Yao, Ole Richter, Guangshe Zhao, Ning Qiao, Yannan Xing, Dingheng Wang, Tianxiang Hu, Wei Fang, Tugba Demirci, Michele De Marchi, Lei Deng, Tianyi Yan, Carsten Nielsen, Sadique Sheik, Chenxi Wu, Yonghong Tian, Bo Xu, and Guoqi Li
    Nature Communications, 2024
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  3. Self-architectural Knowledge Distillation for Spiking Neural Networks
    Haonan Qiu, Munan Ning, Zeyin Song, Wei Fang, Yanqi Chen, Tao Sun, Zhengyu Ma, Li Yuan, and Yonghong Tian
    Neural Networks, 2024
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2023

  1. SpikingJelly: An Open-source Machine Learning Infrastructure Platform for Spike-based Intelligence
    Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timothée Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, and Yonghong Tian
    Science Advances, 2023
    Recommended by Nature Computational Science in the Research Highlight article: A Full-stack Platform for Spiking Deep Learning.
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  2. Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
    Wei Fang, Zhaofei Yu, Zhaokun Zhou, Ding Chen, Yanqi Chen, Zhengyu Ma, Timothée Masquelier, and Yonghong Tian
    In Advances in Neural Information Processing Systems, 2023
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  3. A Unified Framework for Soft Threshold Pruning
    Yanqi Chen, Zhengyu Ma, Wei Fang, Xiawu Zheng, Zhaofei Yu, and Yonghong Tian
    In International Conference on Learning Representations, 2023
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  4. Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
    Yaoyu Zhu, Wei Fang, Xiaodong Xie, Tiejun Huang, and Zhaofei Yu
    In Advances in Neural Information Processing Systems, 2023
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2022

  1. Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
    Tong Bu, Wei Fang, Jianhao Ding, PengLin Dai, Zhaofei Yu, and Tiejun Huang
    In International Conference on Learning Representations, 2022
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  2. State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks
    Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, and Yonghong Tian
    In International Conference on Machine Learning, 2022
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  3. Training Spiking Neural Networks with Event-driven Backpropagation
    Yaoyu Zhu, Zhaofei Yu, Wei Fang, Xiaodong Xie, Tiejun Huang, and Timothée Masquelier
    In Advances in Neural Information Processing Systems, 2022
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2021

  1. Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
    Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, and Yonghong Tian
    In IEEE/CVF International Conference on Computer Vision, 2021
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  2. Deep Residual Learning in Spiking Neural Networks
    Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian
    In Advances in Neural Information Processing Systems, 2021
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  3. Pruning of Deep Spiking Neural Networks through Gradient Rewiring
    Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, and Yonghong Tian
    In International Joint Conference on Artificial Intelligence, 2021
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