BDB: biopanning data bank. - Bifang He; Guoshi Chai; Yaocong Duan; Zhiqiang Yan; Liuyang Qiu; Huixiong Zhang; Zechun Liu; Qiang He; Ke Han; Beibei Ru; Feng-Biao Guo; Hui Ding 005; et al. (Nucleic Acids Res., 2016)

Bi-Real Net: Enhancing the Performance of 1-Bit CNNs with Improved Representational Capability and Advanced Training Algorithm. - Zechun Liu; Baoyuan Wu; Wenhan Luo; Xin Yang 008; Wei Liu 005; Kwang-Ting Cheng (ECCV (15), 2018)

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning. - Zechun Liu; Haoyuan Mu; Xiangyu Zhang 005; Zichao Guo; Xin Yang 008; Kwang-Ting Cheng; Jian Sun 001 (ICCV, 2019)

Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization. - Koen Helwegen; James Widdicombe; Lukas Geiger; Zechun Liu; Kwang-Ting Cheng; Roeland Nusselder (NeurIPS, 2019)

Binarizing MobileNet via Evolution-Based Searching. - Hai Phan 001; Zechun Liu; Dang Huynh; Marios Savvides; Kwang-Ting Cheng; Zhiqiang Shen (CVPR, 2020)

Single Path One-Shot Neural Architecture Search with Uniform Sampling. - Zichao Guo; Xiangyu Zhang 005; Haoyuan Mu; Wen Heng; Zechun Liu; Yichen Wei; Jian Sun 001 (ECCV (16), 2020)

Weight-Dependent Gates for Differentiable Neural Network Pruning. - Yun Li; Weiqun Wu; Zechun Liu; Chi Zhang 026; Xiangyu Zhang 005; Haotian Yao; Baoqun Yin (ECCV Workshops (5), 2020)

ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions. - Zechun Liu; Zhiqiang Shen; Marios Savvides; Kwang-Ting Cheng (ECCV (14), 2020)

Attentive Cutmix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification. - Devesh Walawalkar; Zhiqiang Shen; Zechun Liu; Marios Savvides (ICASSP, 2020)

Rethinking Image Mixture for Unsupervised Visual Representation Learning. - Zhiqiang Shen; Zechun Liu; Zhuang Liu 003; Marios Savvides; Trevor Darrell (CoRR, 2020)

Joint Multi-Dimension Pruning. - Zechun Liu; Xiangyu Zhang 005; Zhiqiang Shen; Zhe Li; Yichen Wei; Kwang-Ting Cheng; Jian Sun 001 (CoRR, 2020)

Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance. - Zechun Liu; Wenhan Luo; Baoyuan Wu; Xin Yang 008; Wei Liu 005; Kwang-Ting Cheng (Int. J. Comput. Vis., 2020)

Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning. - Zhiqiang Shen; Zechun Liu; Jie Qin; Marios Savvides; Kwang-Ting Cheng (AAAI, 2021)

"BNN - BN = ?": Training Binary Neural Networks Without Batch Normalization. - Tianlong Chen 001; Zhenyu Zhang 015; Xu Ouyang; Zechun Liu; Zhiqiang Shen; Zhangyang Wang (CVPR Workshops, 2021)

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-Bit Neural Networks via Guided Distribution Calibration. - Zhiqiang Shen; Zechun Liu; Jie Qin; Lei Huang 015; Kwang-Ting Cheng; Marios Savvides (CVPR, 2021)

Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study. - Zhiqiang Shen; Zechun Liu; Dejia Xu; Zitian Chen; Kwang-Ting Cheng; Marios Savvides (ICLR, 2021)

How Do Adam and Training Strategies Help BNNs Optimization. - Zechun Liu; Zhiqiang Shen; Shichao Li 002; Koen Helwegen; Dong Huang 007; Kwang-Ting Cheng (ICML, 2021)

Conditional Link Prediction of Category-Implicit Keypoint Detection. - Ellen Yi-Ge; Rui Fan; Zechun Liu; Zhiqiang Shen (WACV, 2021)

CDTD: A Large-Scale Cross-Domain Benchmark for Instance-Level Image-to-Image Translation and Domain Adaptive Object Detection. - Zhiqiang Shen; Mingyang Huang; Jianping Shi; Zechun Liu; Harsh Maheshwari; Yutong Zheng; Xiangyang Xue 001; Marios Savvides; Thomas S. Huang (Int. J. Comput. Vis., 2021)

Joint Multi-Dimension Pruning via Numerical Gradient Update. - Zechun Liu; Xiangyu Zhang 005; Zhiqiang Shen; Yichen Wei; Kwang-Ting Cheng; Jian Sun 001 (IEEE Trans. Image Process., 2021)

Stereo Neural Vernier Caliper. - Shichao Li 002; Zechun Liu; Zhiqiang Shen; Kwang-Ting Cheng (AAAI, 2022)

Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning. - Zhiqiang Shen; Zechun Liu; Zhuang Liu 003; Marios Savvides; Trevor Darrell; Eric Poe Xing (AAAI, 2022)

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space. - Arnav Chavan; Zhiqiang Shen; Zhuang Liu 003; Zechun Liu; Kwang-Ting Cheng; Eric P. Xing (CVPR, 2022)

Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. - Zechun Liu; Kwang-Ting Cheng; Dong Huang 007; Eric P. Xing; Zhiqiang Shen (CVPR, 2022)

Data-Free Neural Architecture Search via Recursive Label Calibration. - Zechun Liu; Zhiqiang Shen; Yun Long; Eric P. Xing; Kwang-Ting Cheng; Chas Leichner (ECCV (24), 2022)

Sliced Recursive Transformer. - Zhiqiang Shen; Zechun Liu; Eric P. Xing (ECCV (24), 2022)

SDQ: Stochastic Differentiable Quantization with Mixed Precision. - Xijie Huang; Zhiqiang Shen; Shichao Li 002; Zechun Liu; Xianghong Hu; Jeffry Wicaksana; Eric P. Xing; Kwang-Ting Cheng (ICML, 2022)

BiT: Robustly Binarized Multi-distilled Transformer. - Zechun Liu; Barlas Oguz; Aasish Pappu; Lin Xiao; Scott Yih; Meng Li 004; Raghuraman Krishnamoorthi; Yashar Mehdad (NeurIPS, 2022)

Weight-Dependent Gates for Network Pruning. - Yun Li; Zechun Liu; Weiqun Wu; Haotian Yao; Xiangyu Zhang 005; Chi Zhang 026; Baoqun Yin (IEEE Trans. Circuits Syst. Video Technol., 2022)

Binary and Ternary Natural Language Generation. - Zechun Liu; Barlas Oguz; Aasish Pappu; Yangyang Shi; Raghuraman Krishnamoorthi (ACL (1), 2023)

LLM-FP4: 4-Bit Floating-Point Quantized Transformers. - Shih-Yang Liu; Zechun Liu; Xijie Huang; Pingcheng Dong; Kwang-Ting Cheng (EMNLP, 2023)

Oscillation-free Quantization for Low-bit Vision Transformers. - Shih-Yang Liu; Zechun Liu; Kwang-Ting Cheng (ICML, 2023)

EBSR: Enhanced Binary Neural Network for Image Super-Resolution. - Renjie Wei; Shuwen Zhang; Zechun Liu; Meng Li 004; Yuchen Fan; Runsheng Wang; Ru Huang 001 (CoRR, 2023)

Efficient Quantization-aware Training with Adaptive Coreset Selection. - Xijie Huang; Zechun Liu; Shih-Yang Liu; Kwang-Ting Cheng (CoRR, 2023)

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning. - Jun Chen 021; Deyao Zhu; Xiaoqian Shen; Xiang Li; Zechun Liu; Pengchuan Zhang; Raghuraman Krishnamoorthi; Vikas Chandra; Yunyang Xiong; Mohamed Elhoseiny (CoRR, 2023)

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts. - Ganesh Jawahar; Haichuan Yang; Yunyang Xiong; Zechun Liu; Dilin Wang; Fei Sun; Meng Li 004; Aasish Pappu; Barlas Oguz; Muhammad Abdul-Mageed; Laks V. S. Lakshmanan; Raghuraman Krishnamoorthi; et al. (ACL (Findings), 2024)

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models. - Zechun Liu; Barlas Oguz; Changsheng Zhao 002; Ernie Chang; Pierre Stock; Yashar Mehdad; Yangyang Shi; Raghuraman Krishnamoorthi; Vikas Chandra (ACL (Findings), 2024)

Target-Aware Language Modeling via Granular Data Sampling. - Ernie Chang; Pin-Jie Lin; Yang Li 183; Changsheng Zhao 002; Daeil Kim; Rastislav Rabatin; Zechun Liu; Yangyang Shi; Vikas Chandra (EMNLP, 2024)

Scaling Parameter-Constrained Language Models with Quality Data. - Ernie Chang; Matteo Paltenghi; Yang Li 183; Pin-Jie Lin; Changsheng Zhao 002; Patrick Huber; Zechun Liu; Rastislav Rabatin; Yangyang Shi; Vikas Chandra (EMNLP (Industry Track), 2024)

RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. - Xijie Huang; Zechun Liu; Shih-Yang Liu; Kwang-Ting Cheng (EMNLP (Findings), 2024)

On the Open Prompt Challenge in Conditional Audio Generation. - Ernie Chang; Sidd Srinivasan; Mahi Luthra; Pin-Jie Lin; Varun Nagaraja; Forrest N. Iandola; Zechun Liu; Zhaoheng Ni; Changsheng Zhao 002; Yangyang Shi; Vikas Chandra (ICASSP, 2024)

MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases. - Zechun Liu; Changsheng Zhao 002; Forrest N. Iandola; Chen Lai; Yuandong Tian; Igor Fedorov; Yunyang Xiong; Ernie Chang; Yangyang Shi; Raghuraman Krishnamoorthi; Liangzhen Lai; Vikas Chandra (ICML, 2024)

An Introduction to Vision-Language Modeling. - Florian Bordes; Richard Yuanzhe Pang; Anurag Ajay; Alexander C. Li; Adrien Bardes; Suzanne Petryk; Oscar Mañas; Zhiqiu Lin; Anas Mahmoud 002; Bargav Jayaraman; Mark Ibrahim; Melissa Hall; et al. (CoRR, 2024)

Agent-as-a-Judge: Evaluate Agents with Agents. - Mingchen Zhuge; Changsheng Zhao 002; Dylan R. Ashley; Wenyi Wang; Dmitrii Khizbullin; Yunyang Xiong; Zechun Liu; Ernie Chang; Raghuraman Krishnamoorthi; Yuandong Tian; Yangyang Shi; Vikas Chandra; et al. (CoRR, 2024)

LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding. - Xiaoqian Shen; Yunyang Xiong; Changsheng Zhao 002; Lemeng Wu; Jun Chen 021; Chenchen Zhu; Zechun Liu; Fanyi Xiao; Balakrishnan Varadarajan; Florian Bordes; Zhuang Liu; Hu Xu 001; et al. (CoRR, 2024)

Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversations. - Igor Fedorov; Kate Plawiak; Lemeng Wu; Tarek Elgamal; Naveen Suda; Eric Smith; Hongyuan Zhan; Jianfeng Chi; Yuriy Hulovatyy; Kimish Patel; Zechun Liu; Changsheng Zhao 002; et al. (CoRR, 2024)

Efficient Track Anything. - Yunyang Xiong; Chong Zhou; Xiaoyu Xiang; Lemeng Wu; Chenchen Zhu; Zechun Liu; Saksham Suri; Balakrishnan Varadarajan; Ramya Akula; Forrest N. Iandola; Raghuraman Krishnamoorthi; Bilge Soran; et al. (CoRR, 2024)

Robust and Efficient Quantization-aware Training via Coreset Selection. - Xijie Huang; Zechun Liu; Shih-Yang Liu; Kwang-Ting Cheng (Trans. Mach. Learn. Res., 2024)

SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings. - Renjie Wei; Zechun Liu; Yuchen Fan; Runsheng Wang; Ru Huang 001; Meng Li 004 (DATE, 2025)

R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference. - Zhenyu Zhang 015; Zechun Liu; Yuandong Tian; Harshit Khaitan; Zhangyang Wang; Steven Li (ICLR, 2025)

ParamΔ for Direct Mixing: Post-Train Large Language Model At Zero Cost. - Sheng Cao; Mingrui Wu; Karthik Prasad; Yuandong Tian; Zechun Liu (ICLR, 2025)

SpinQuant: LLM Quantization with Learned Rotations. - Zechun Liu; Changsheng Zhao 002; Igor Fedorov; Bilge Soran; Dhruv Choudhary; Raghuraman Krishnamoorthi; Vikas Chandra; Yuandong Tian; Tijmen Blankevoort (ICLR, 2025)

ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization. - Zechun Liu; Changsheng Zhao 002; Hanxian Huang; Sijia Chen; Jing Zhang; Jiawei Zhao; Scott Roy; Lisa Jin; Yunyang Xiong; Yangyang Shi; Lin Xiao; Yuandong Tian; et al. (CoRR, 2025)

PARQ: Piecewise-Affine Regularized Quantization. - Lisa Jin; Jianhao Ma; Zechun Liu; Andrey Gromov; Aaron Defazio; Lin Xiao (CoRR, 2025)

ParamΔ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost. - Sheng Cao; Mingrui Wu; Karthik Prasad; Yuandong Tian; Zechun Liu (CoRR, 2025)