Wei Fang's Homepage

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Postdoc Association, Electrical & Computer Engineering, Yale University

Wei Fang’s Homepage

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I am currently the Postdoc Association in Electrical & Computer Engineering, Yale University, mentored by Prof. Priya Panda.

I received my Ph.D. degree in School of Computer Science, Peking University, supervised by Prof. Yonghong Tian. I closely cooperated with Prof. Zhaofei Yu of Peking University, Researcher Timothée Masquelier of CNRS, and Prof. Guoqi Li of Institute of Automation, Chinese Academy of Sciences during my Ph.D. career.

My recent research interests are learning and network structure of Spiking Neural Network.

Please contact me (fangwei123456g at gmail.com) if you are interested in my research.

Last update: 2025.03.11

Education and Working Experience

Publications

First/Corresponding-author Papers

  1. Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, Yonghong Tian, Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks, ICCV 2021

  2. Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian, Deep Residual Learning in Spiking Neural Networks, NeurIPS 2021

  3. 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, Science Advances, 2023

  4. Wei Fang, Zhaofei Yu, Zhaokun Zhou, Ding Chen, Yanqi Chen, Zhengyu Ma, Timothée Masquelier, Yonghong Tian, Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies, NeurIPS 2023

  5. Wei Fang, Yaoyu Zhu, Zihan Huang, Man Yao, Zhaofei Yu, Yonghong Tian, Review of Surrogate Gradient Methods in Spiking Deep Learning (In Chinese), Chinese Journal of Computers, 2025 (In Press)

  6. Zihan Huang, Wei Fang, Tong Bu, Peng Xue, Zecheng Hao, Wenxuan Liu, Yuanhong Tang, Zhaofei Yu, Tiejun Huang, Differential Coding for Training-Free ANN-to-SNN Conversion, ICML, 2025 (In Press)

  7. Wei Fang, Priyadarshini Panda, Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space, ArXiv, 2025
    • This article is so interesting that I put it here even though it is only a pre-print

Other Papers

  1. Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, Yonghong Tian, Pruning of Deep Spiking Neural Networks through Gradient Rewiring, IJCAI 2021
  2. 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
  3. Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, Yonghong Tian, State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks, ICML 2022
  4. Yaoyu Zhu, Zhaofei Yu, Wei Fang, Xiaodong Xie, Tiejun Huang, Timothée Masquelier, Training Spiking Neural Networks with Event-driven Backpropagation, NeurIPS 2022
  5. Yanqi Chen, Zhengyu Ma, Wei Fang, Xiawu Zheng, Zhaofei Yu, Yonghong Tian, A Unified Framework for Soft Threshold Pruning, ICLR 2023
  6. Yaoyu Zhu, Wei Fang, Xiaodong Xie, Tiejun Huang, Zhaofei Yu, Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks, NeurIPS 2023
  7. Liuzhenghao Lv, Wei Fang, Li Yuan, Yonghong Tian, Optimal ANN-SNN Conversion with Group Neurons, ICASSP 2024
  8. 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, Guoqi Li, Spike-based Dynamic Computing with Asynchronous Sensing-Computing Neuromorphic Chip, Nature Communications, 2024
  9. Haonan Qiu, Munan Ning, Zeyin Song, Wei Fang, Yanqi Chen, Tao Sun, Zhengyu Ma, Li Yuan, Yonghong Tian, Self-architectural Knowledge Distillation for Spiking Neural Networks, Neural Networks, 2024

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