Zecheng Hao

Orcid: 0000-0001-9074-2857

According to our database1, Zecheng Hao authored at least 17 papers between 2023 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

2023
2024
2025
0
5
10
15
1
5
8
3

Legend:

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In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2025
UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction.
IEEE Geosci. Remote. Sens. Lett., 2025

2024
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation.
CoRR, 2024

USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting.
CoRR, 2024

Comprehensive Online Training and Deployment for Spiking Neural Networks.
CoRR, 2024

UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction.
CoRR, 2024

LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model.
CoRR, 2024

LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Towards High-performance Spiking Transformers from ANN to SNN Conversion.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

Enhancing Adversarial Robustness in SNNs with Sparse Gradients.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Threaten Spiking Neural Networks through Combining Rate and Temporal Information.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Reducing ANN-SNN Conversion Error through Residual Membrane Potential.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023


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