Additive Powers-of-Two Quantization: A Non-uniform Discretization for Neural Networks. - Yuhang Li 001; Xin Dong 009; Wei Wang 059 (CoRR, 2019)

RTN: Reparameterized Ternary Network. - Yuhang Li 001; Xin Dong 009; Sai Qian Zhang; Haoli Bai; Yuanpeng Chen; Wei Wang 059 (AAAI, 2020)

Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks. - Yuhang Li 001; Xin Dong 009; Wei Wang 059 (ICLR, 2020)

Efficient Bitwidth Search for Practical Mixed Precision Neural Network. - Yuhang Li 001; Wei Wang 059; Haoli Bai; Ruihao Gong; Xin Dong 009; Fengwei Yu (CoRR, 2020)

Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization. - Yuhang Li 001; Feng Zhu 006; Ruihao Gong; Mingzhu Shen; Fengwei Yu; Shaoqing Lu; Shi Gu (CoRR, 2020)

Diversifying Sample Generation for Accurate Data-Free Quantization. - Xiangguo Zhang; Haotong Qin; Yifu Ding; Ruihao Gong; Qinghua Yan; Renshuai Tao; Yuhang Li 001; Fengwei Yu; Xianglong Liu 001 (CVPR, 2021)

MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing. - Yuhang Li 001; Feng Zhu 006; Ruihao Gong; Mingzhu Shen; Xin Dong 009; Fengwei Yu; Shaoqing Lu; Shi Gu (ICCV, 2021)

Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search. - Mingzhu Shen; Feng Liang; Ruihao Gong; Yuhang Li 001; Chuming Li; Chen Lin 003; Fengwei Yu; Junjie Yan; Wanli Ouyang (ICCV, 2021)

BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction. - Yuhang Li 001; Ruihao Gong; Xu Tan; Yang Yang; Peng Hu; Qi Zhang; Fengwei Yu; Wei Wang 059; 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)

Real World Robustness from Systematic Noise. - Yan Wang 033; Yuhang Li 001; Ruihao Gong; Tianzi Xiao; Fengwei Yu (AdvM @ ACM Multimedia, 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)

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. - Yuhang Li 001; Mingzhu Shen; Jian Ma; Yan Ren; Mingxin Zhao; Qi Zhang; Ruihao Gong; Fengwei Yu; Junjie Yan (NeurIPS Datasets and Benchmarks, 2021)

Real World Robustness from Systematic Noise. - Yan Wang 033; Yuhang Li 001; Ruihao Gong (CoRR, 2021)

Neural Architecture Search for Spiking Neural Networks. - Youngeun Kim; Yuhang Li 001; Hyoungseob Park; Yeshwanth Venkatesha; Priyadarshini Panda (ECCV (24), 2022)

Exploring Lottery Ticket Hypothesis in Spiking Neural Networks. - Youngeun Kim; Yuhang Li 001; Hyoungseob Park; Yeshwanth Venkatesha; Ruokai Yin; Priyadarshini Panda (ECCV (12), 2022)

Neuromorphic Data Augmentation for Training Spiking Neural Networks. - Yuhang Li 001; Youngeun Kim; Hyoungseob Park; Tamar Geller; Priyadarshini Panda (ECCV (7), 2022)

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

QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization. - Xiuying Wei; Ruihao Gong; Yuhang Li 001; Xianglong Liu 001; Fengwei Yu (ICLR, 2022)

Addressing Client Drift in Federated Continual Learning with Adaptive Optimization. - Yeshwanth Venkatesha; Youngeun Kim; Hyoungseob Park; Yuhang Li 001; Priyadarshini Panda (CoRR, 2022)

When Sparsity Meets Dynamic Convolution. - Shwai He; Yuhang Li 001; Chenbo Jiang; Shi Gu (CoRR, 2022)

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

Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks. - Yuhang Li 001; Ruokai Yin; Hyoungseob Park; Youngeun Kim; Priyadarshini Panda (CoRR, 2022)

Exploring Temporal Information Dynamics in Spiking Neural Networks. - Youngeun Kim; Yuhang Li 001; Hyoungseob Park; Yeshwanth Venkatesha; Anna Hambitzer; Priyadarshini Panda (AAAI, 2023)

Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing. - Yuhang Li 001; Abhishek Moitra; Tamar Geller; Priyadarshini Panda (DAC, 2023)

Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling. - Xiuying Wei; Yunchen Zhang; Yuhang Li 001; Xiangguo Zhang; Ruihao Gong; Jinyang Guo; Xianglong Liu 001 (EMNLP, 2023)

Augmentation Robust Self-Supervised Learning for Human Activity Recognition. - Cong Xu; Yuhang Li 001; Dae Lee; Dae Hoon Park; Hongda Mao; Huyen Do; Jonathan Chung 001; Dinesh Nair (ICASSP, 2023)

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

SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency. - Yan Wang 033; Yuhang Li 001; Ruihao Gong; Aishan Liu; Yanfei Wang; Jian Hu; Yongqiang Yao; Yunchen Zhang; Tianzi Xiao; Fengwei Yu; Xianglong Liu 001 (MLSys, 2023)

SEENN: Towards Temporal Spiking Early Exit Neural Networks. - Yuhang Li 001; Tamar Geller; Youngeun Kim; Priyadarshini Panda (NeurIPS, 2023)

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling. - Xiuying Wei; Yunchen Zhang; Yuhang Li 001; Xiangguo Zhang; Ruihao Gong; Jinyang Guo; Xianglong Liu 001 (CoRR, 2023)

MINT: Multiplier-less Integer Quantization for Spiking Neural Networks. - Ruokai Yin; Yuhang Li 001; Abhishek Moitra; Priyadarshini Panda (CoRR, 2023)

Do We Really Need a Large Number of Visual Prompts? - Youngeun Kim; Yuhang Li 001; Abhishek Moitra; Priyadarshini Panda (CoRR, 2023)

Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks. - Youngeun Kim; Yuhang Li 001; Abhishek Moitra; Ruokai Yin; Priyadarshini Panda (CoRR, 2023)

Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning. - Qian Zhang; Chenxi Wu; Adar Kahana; Youngeun Kim; Yuhang Li 001; George Em Karniadakis; Priyadarshini Panda (CoRR, 2023)

StableQ: Enhancing Data-Scarce Quantization with Text-to-Image Data. - Yuhang Li 001; Youngeun Kim; Donghyun Lee 002; Priyadarshini Panda (CoRR, 2023)

Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient. - Yuhang Li 001; Youngeun Kim; Hyoungseob Park; Priyadarshini Panda (Trans. Mach. Learn. Res., 2023)

MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks. - Ruokai Yin; Yuhang Li 001; Abhishek Moitra; Priyadarshini Panda (ASPDAC, 2024)

TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training. - Donghyun Lee 002; Ruokai Yin; Youngeun Kim; Abhishek Moitra; Yuhang Li 001; Priyadarshini Panda (DATE, 2024)

One-Stage Prompt-Based Continual Learning. - Youngeun Kim; Yuhang Li 001; Priyadarshini Panda (ECCV (13), 2024)

A Simple Background Augmentation Method for Object Detection with Diffusion Model. - Yuhang Li 001; Xin Dong 009; Chen Chen 043; Weiming Zhuang; Lingjuan Lyu (ECCV (66), 2024)

GenQ: Quantization in Low Data Regimes with Generative Synthetic Data. - Yuhang Li 001; Youngeun Kim; Donghyun Lee 002; Souvik Kundu 009; Priyadarshini Panda (ECCV (13), 2024)

Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization. - Yuhang Li 001; Xin Dong 009; Chen Chen 043; Jingtao Li; Yuxin Wen; Michael Spranger; Lingjuan Lyu (CoRR, 2024)

Temporal Feature Matters: A Framework for Diffusion Model Quantization. - Yushi Huang; Ruihao Gong; Xianglong Liu 001; Jing Liu 048; Yuhang Li 001; Jiwen Lu; Dacheng Tao (CoRR, 2024)

When In-memory Computing Meets Spiking Neural Networks - A Perspective on Device-Circuit-System-and-Algorithm Co-design. - Abhishek Moitra; Abhiroop Bhattacharjee; Yuhang Li 001; Youngeun Kim; Priyadarshini Panda (CoRR, 2024)

ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition. - Shiting Xiao; Yuhang Li 001; Youngeun Kim; Donghyun Lee 002; Priyadarshini Panda (CoRR, 2024)

TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction. - Yuhang Li 001; Priyadarshini Panda (CoRR, 2024)

Do we really need a large number of visual prompts? - Youngeun Kim; Yuhang Li 001; Abhishek Moitra; Ruokai Yin; Priyadarshini Panda (Neural Networks, 2024)

Workload-Balanced Pruning for Sparse Spiking Neural Networks. - Ruokai Yin; Youngeun Kim; Yuhang Li 001; Abhishek Moitra; Nitin Satpute; Anna Hambitzer; Priyadarshini Panda (IEEE Trans. Emerg. Top. Comput. Intell., 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)

Spiking Transformer with Spatial-Temporal Attention. - Donghyun Lee 002; Yuhang Li 001; Youngeun Kim; Shiting Xiao; Priyadarshini Panda (CVPR, 2025)

Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning. - Qian Zhang; Chenxi Wu; Adar Kahana; George Em Karniadakis; Youngeun Kim; Yuhang Li 001; Priyadarshini Panda (SIAM J. Sci. Comput., 2025)

PacQ: A SIMT Microarchitecture for Efficient Dataflow in Hyper-asymmetric GEMMs. - Ruokai Yin; Yuhang Li 001; Priyadarshini Panda (CoRR, 2025)

GPTQv2: Efficient Finetuning-Free Quantization for Asymmetric Calibration. - Yuhang Li 001; Ruokai Yin; Donghyun Lee 002; Shiting Xiao; Priyadarshini Panda (CoRR, 2025)

Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba. - Donghyun Lee 002; Yuhang Li 001; Ruokai Yin; Shiting Xiao; Priyadarshini Panda (CoRR, 2025)

DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs. - Ruokai Yin; Yuhang Li 001; Donghyun Lee 002; Priyadarshini Panda (CoRR, 2025)

OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts. - Shiting Xiao; Rishabh Kabra; Yuhang Li 001; Donghyun Lee 002; João Carreira 001; Priyadarshini Panda (CoRR, 2025)

Pushing the Limit of Post-Training Quantization. - Ruihao Gong; Xianglong Liu 001; Yuhang Li 001; Yunqian Fan; Xiuying Wei; Jinyang Guo (IEEE Trans. Pattern Anal. Mach. Intell., 2025)