Wei Fang

Orcid: 0009-0009-4409-0312

Affiliations:

Yale University, Department of Electrical & Computer Engineering, New Haven, CT, USA
Peking University, Shenzhen Graduate School, School of Electronic and Computer Engineering, Shenzhen, China (PhD 2024)
Peng Cheng Laboratory, Shenzhen, China

According to our database¹, Wei Fang authored at least 22 papers between 2021 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

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Other

Bibliography

2025

Event2Vec: Processing neuromorphic events directly by representations in vector space.

[BibT_eX]

[DOI]

Wei Fang

Priyadarshini Panda

CoRR, April, 2025

Differential Coding for Training-Free ANN-to-SNN Conversion.

[BibT_eX]

[DOI]

CoRR, March, 2025

Channel-wise Parallelizable Spiking Neuron with Multiplication-free Dynamics and Large Temporal Receptive Fields.

[BibT_eX]

[DOI]

CoRR, January, 2025

2024

Self-architectural knowledge distillation for spiking neural networks.

[BibT_eX]

[DOI]

Neural Networks, 2024

Flexible and Scalable Deep Dendritic Spiking Neural Networks with Multiple Nonlinear Branching.

[BibT_eX]

[DOI]

CoRR, 2024

ETTFS: An Efficient Training Framework for Time-to-First-Spike Neuron.

[BibT_eX]

[DOI]

CoRR, 2024

Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket.

[BibT_eX]

[DOI]

CoRR, 2024

Optimal ANN-SNN Conversion with Group Neurons.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2024

Temporal Contrastive Learning for Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

2023

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.

[BibT_eX]

[DOI]

CoRR, 2023

Auto-Spikformer: Spikformer Architecture Search.

[BibT_eX]

[DOI]

CoRR, 2023

Parallel Spiking Neurons with High Efficiency and Long-term Dependencies Learning Ability.

[BibT_eX]

[DOI]

CoRR, 2023

Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Unified Framework for Soft Threshold Pruning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Training Spiking Neural Networks with Event-driven Backpropagation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Spike-based Residual Blocks.

[BibT_eX]

[DOI]

CoRR, 2021

Deep Residual Learning in Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Pruning of Deep Spiking Neural Networks through Gradient Rewiring.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Wei Fang

Timeline

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