Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks. - Shikuang Deng; Shi Gu (ICLR, 2021)

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration. - Yuhang Li 001; Shikuang Deng; Xin Dong 009; Ruihao Gong; Shi Gu (ICML, 2021)

Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks. - Yuhang Li 001; Yufei Guo; Shanghang Zhang; Shikuang Deng; Yongqing Hai; Shi Gu (NeurIPS, 2021)

Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting. - Shikuang Deng; Yuhang Li 001; Shanghang Zhang; Shi Gu (ICLR, 2022)

Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration. - Yuhang Li 001; Shikuang Deng; Xin Dong 009; Shi Gu (CoRR, 2022)

Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks. - Shikuang Deng; Hao Lin; Yuhang Li 001; Shi Gu (ICML, 2023)

Spiking Token Mixer: An event-driven friendly Former structure for spiking neural networks. - Shikuang Deng; Yuhang Wu; Kangrui Du; Shi Gu (NeurIPS, 2024)

Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration. - Yuhang Li 001; Shikuang Deng; Xin Dong 009; Shi Gu (Int. J. Comput. Vis., 2024)

Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness. - Kangrui Du; Yuhang Wu; Shikuang Deng; Shi Gu (ICLR, 2025)