Yuhang Li

Orcid: 0000-0002-6444-7253

Affiliations:

Yale University, Department of Electrical Engineering, New Haven, CT, USA
University of Electronic Science and Technology of China (UESTC), Chengdu, China (2016 - 2021)

According to our database¹, Yuhang Li authored at least 58 papers between 2019 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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In proceedings

Article

PhD thesis

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Other

Bibliography

2025

Pushing the Limit of Post-Training Quantization.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., July, 2025

OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts.

[BibT_eX]

[DOI]

CoRR, July, 2025

DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs.

[BibT_eX]

[DOI]

CoRR, June, 2025

Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba.

[BibT_eX]

[DOI]

CoRR, June, 2025

GPTQv2: Efficient Finetuning-Free Quantization for Asymmetric Calibration.

[BibT_eX]

[DOI]

CoRR, April, 2025

PacQ: A SIMT Microarchitecture for Efficient Dataflow in Hyper-asymmetric GEMMs.

[BibT_eX]

[DOI]

Ruokai Yin

Yuhang Li

Priyadarshini Panda

CoRR, February, 2025

Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning.

[BibT_eX]

[DOI]

Qian Zhang

Chenxi Wu

Adar Kahana

George Em Karniadakis

Youngeun Kim

Yuhang Li

Priyadarshini Panda

SIAM J. Sci. Comput., 2025

Spiking Transformer with Spatial-Temporal Attention.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration.

[BibT_eX]

[DOI]

Int. J. Comput. Vis., September, 2024

Workload-Balanced Pruning for Sparse Spiking Neural Networks.

[BibT_eX]

[DOI]

IEEE Trans. Emerg. Top. Comput. Intell., August, 2024

Do we really need a large number of visual prompts?

[BibT_eX]

[DOI]

Neural Networks, 2024

TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction.

[BibT_eX]

[DOI]

Yuhang Li

Priyadarshini Panda

CoRR, 2024

ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition.

[BibT_eX]

[DOI]

CoRR, 2024

When In-memory Computing Meets Spiking Neural Networks - A Perspective on Device-Circuit-System-and-Algorithm Co-design.

[BibT_eX]

[DOI]

Abhishek Moitra

Abhiroop Bhattacharjee

Yuhang Li

Youngeun Kim

Priyadarshini Panda

CoRR, 2024

Temporal Feature Matters: A Framework for Diffusion Model Quantization.

[BibT_eX]

[DOI]

CoRR, 2024

Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization.

[BibT_eX]

[DOI]

CoRR, 2024

GenQ: Quantization in Low Data Regimes with Generative Synthetic Data.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2024, 2024

A Simple Background Augmentation Method for Object Detection with Diffusion Model.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2024, 2024

One-Stage Prompt-Based Continual Learning.

[BibT_eX]

[DOI]

Youngeun Kim

Yuhang Li

Priyadarshini Panda

Proceedings of the Computer Vision - ECCV 2024, 2024

TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training.

[BibT_eX]

[DOI]

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2024

MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 29th Asia and South Pacific Design Automation Conference, 2024

2023

Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

StableQ: Enhancing Data-Scarce Quantization with Text-to-Image Data.

[BibT_eX]

[DOI]

CoRR, 2023

Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning.

[BibT_eX]

[DOI]

George Em Karniadakis

Priyadarshini Panda

CoRR, 2023

Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Do We Really Need a Large Number of Visual Prompts?

[BibT_eX]

[DOI]

CoRR, 2023

MINT: Multiplier-less Integer Quantization for Spiking Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling.

[BibT_eX]

[DOI]

CoRR, 2023

SEENN: Towards Temporal Spiking Early Exit Neural Networks.

[BibT_eX]

[DOI]

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

SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency.

[BibT_eX]

[DOI]

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Augmentation Robust Self-Supervised Learning for Human Activity Recognition.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2023

Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling.

[BibT_eX]

[DOI]

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing.

[BibT_eX]

[DOI]

Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

Exploring Temporal Information Dynamics in Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2022

Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration.

[BibT_eX]

[DOI]

CoRR, 2022

When Sparsity Meets Dynamic Convolution.

[BibT_eX]

[DOI]

CoRR, 2022

Addressing Client Drift in Federated Continual Learning with Adaptive Optimization.

[BibT_eX]

[DOI]

CoRR, 2022

QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Neuromorphic Data Augmentation for Training Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2022, 2022

Exploring Lottery Ticket Hypothesis in Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2022, 2022

Neural Architecture Search for Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision, 2022

2021

Real World Robustness from Systematic Noise.

[BibT_eX]

[DOI]

Yan Wang

Yuhang Li

Ruihao Gong

CoRR, 2021

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Differentiable Spike: Rethinking Gradient-Descent for Training 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

Real World Robustness from Systematic Noise.

[BibT_eX]

[DOI]

Proceedings of the ADVM '21: Proceedings of the 1st International Workshop on Adversarial Learning for Multimedia, 2021

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search.

[BibT_eX]

[DOI]

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

MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing.

[BibT_eX]

[DOI]

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

Diversifying Sample Generation for Accurate Data-Free Quantization.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization.

[BibT_eX]

[DOI]

CoRR, 2020

Efficient Bitwidth Search for Practical Mixed Precision Neural Network.

[BibT_eX]

[DOI]

CoRR, 2020

Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks.

[BibT_eX]

[DOI]

Yuhang Li

Xin Dong

Wei Wang

Proceedings of the 8th International Conference on Learning Representations, 2020

RTN: Reparameterized Ternary Network.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Additive Powers-of-Two Quantization: A Non-uniform Discretization for Neural Networks.

[BibT_eX]

[DOI]

Yuhang Li

Xin Dong

Wei Wang

CoRR, 2019

Yuhang Li

Timeline

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