Deep Networks with Stochastic Depth. - Gao Huang 001; Yu Sun 020; Zhuang Liu 003; Daniel Sedra; Kilian Q. Weinberger (ECCV (4), 2016)

Densely Connected Convolutional Networks. - Gao Huang 001; Zhuang Liu 003; Kilian Q. Weinberger (CoRR, 2016)

Densely Connected Convolutional Networks. - Gao Huang 001; Zhuang Liu 003; Laurens van der Maaten; Kilian Q. Weinberger (CVPR, 2017)

Learning Efficient Convolutional Networks through Network Slimming. - Zhuang Liu 003; Jianguo Li; Zhiqiang Shen; Gao Huang 001; Shoumeng Yan; Changshui Zhang (ICCV, 2017)

DSOD: Learning Deeply Supervised Object Detectors from Scratch. - Zhiqiang Shen; Zhuang Liu 003; Jianguo Li; Yu-Gang Jiang 001; Yurong Chen 001; Xiangyang Xue 001 (ICCV, 2017)

Snapshot Ensembles: Train 1, Get M for Free. - Gao Huang 001; Yixuan Li 001; Geoff Pleiss; Zhuang Liu 003; John E. Hopcroft; Kilian Q. Weinberger (ICLR (Poster), 2017)

Knowledge Distillation from Few Samples. - Tianhong Li; Jianguo Li; Zhuang Liu 003; Changshui Zhang (CoRR, 2018)

Few-Shot Object Detection via Feature Reweighting. - Bingyi Kang; Zhuang Liu 003; Xin Wang 066; Fisher Yu 001; Jiashi Feng; Trevor Darrell (ICCV, 2019)

Rethinking the Value of Network Pruning. - Zhuang Liu 003; Mingjie Sun; Tinghui Zhou; Gao Huang 001; Trevor Darrell (ICLR (Poster), 2019)

Test-Time Training for Out-of-Distribution Generalization. - Yu Sun 020; Xiaolong Wang 004; Zhuang Liu 003; John Miller 001; Alexei A. Efros; Moritz Hardt (CoRR, 2019)

Transferable Recognition-Aware Image Processing. - Zhuang Liu 003; Tinghui Zhou; Zhiqiang Shen; Bingyi Kang; Trevor Darrell (CoRR, 2019)

Regularization Matters in Policy Optimization. - Zhuang Liu 003; Xuanlin Li; Bingyi Kang; Trevor Darrell (CoRR, 2019)

Few Sample Knowledge Distillation for Efficient Network Compression. - Tianhong Li; Jianguo Li; Zhuang Liu 003; Changshui Zhang (CVPR, 2020)

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts. - Yu Sun 020; Xiaolong Wang 004; Zhuang Liu 003; John Miller 001; Alexei A. Efros; Moritz Hardt (ICML, 2020)

A New Meta-Baseline for Few-Shot Learning. - Yinbo Chen; Xiaolong Wang 004; Zhuang Liu 003; Huijuan Xu 001; Trevor Darrell (CoRR, 2020)

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

Object Detection from Scratch with Deep Supervision. - Zhiqiang Shen; Zhuang Liu 003; Jianguo Li; Yu-Gang Jiang 001; Yurong Chen 001; Xiangyang Xue 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning. - Yinbo Chen; Zhuang Liu 003; Huijuan Xu 001; Trevor Darrell; Xiaolong Wang 004 (ICCV, 2021)

Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control. - Zhuang Liu 003; Xuanlin Li; Bingyi Kang; Trevor Darrell (ICLR, 2021)

sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data. - Guanhua Wang; Zhuang Liu 003; Brandon Hsieh; Siyuan Zhuang; Joseph Gonzalez 001; Trevor Darrell; Ion Stoica (MLSys, 2021)

Confidence Adaptive Anytime Pixel-Level Recognition. - Zhuang Liu 003; Trevor Darrell; Evan Shelhamer (CoRR, 2021)

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)

A ConvNet for the 2020s. - Zhuang Liu 003; Hanzi Mao; Chao-Yuan Wu; Christoph Feichtenhofer; Trevor Darrell; Saining Xie (CVPR, 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)

Anytime Dense Prediction with Confidence Adaptivity. - Zhuang Liu 003; Zhiqiu Xu; Hung-Ju Wang; Trevor Darrell; Evan Shelhamer (ICLR, 2022)

Convolutional Networks with Dense Connectivity. - Gao Huang 001; Zhuang Liu 003; Geoff Pleiss; Laurens van der Maaten; Kilian Q. Weinberger (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Efficient and Scalable Neural Architectures for Visual Recognition - Zhuang Liu 003 (2022)

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. - Sanghyun Woo; Shoubhik Debnath; Ronghang Hu; Xinlei Chen; Zhuang Liu 003; In So Kweon; Saining Xie (CVPR, 2023)

RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning. - Rishabh Tiwari; Arnav Chavan; Deepak K. Gupta; Gowreesh Mago; Animesh Gupta; Akash Gupta; Suraj Sharan; Yukun Yang; Shanwei Zhao; Shihao Wang; Youngjun Kwak; Seonghun Jeong; et al. (ICCV (Workshops), 2023)

Dropout Reduces Underfitting. - Zhuang Liu 003; Zhiqiu Xu; Joseph Jin; Zhiqiang Shen; Trevor Darrell (ICML, 2023)

One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning. - Arnav Chavan; Zhuang Liu 003; Deepak K. Gupta; Eric P. Xing; Zhiqiang Shen (CoRR, 2023)

MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation. - John Lambert; Zhuang Liu 003; Ozan Sener; James Hays; Vladlen Koltun (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Exploring Simple and Transferable Recognition-Aware Image Processing. - Zhuang Liu 003; Hung-Ju Wang; Tinghui Zhou; Zhiqiang Shen; Bingyi Kang; Evan Shelhamer; Trevor Darrell (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs. - Shengbang Tong; Zhuang Liu 003; Yuexiang Zhai; Yi Ma 001; Yann LeCun; Saining Xie (CVPR, 2024)

A Simple and Effective Pruning Approach for Large Language Models. - Mingjie Sun; Zhuang Liu 003; Anna Bair; J. Zico Kolter (ICLR, 2024)

Initializing Models with Larger Ones. - Zhiqiu Xu; Yanjie Chen; Kirill Vishniakov; Yida Yin; Zhiqiang Shen; Trevor Darrell; Lingjie Liu; Zhuang Liu 003 (ICLR, 2024)

ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy. - Kirill Vishniakov; Zhiqiang Shen; Zhuang Liu 003 (ICML, 2024)

Understanding Bias in Large-Scale Visual Datasets. - Boya Zeng; Yida Yin; Zhuang Liu 003 (NeurIPS, 2024)

Neural Network Diffusion. - Kai Wang 036; Zhaopan Xu; Yukun Zhou; Zelin Zang; Trevor Darrell; Zhuang Liu 003; Yang You 001 (CoRR, 2024)

Massive Activations in Large Language Models. - Mingjie Sun; Xinlei Chen; J. Zico Kolter; Zhuang Liu 003 (CoRR, 2024)

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning. - Shengbang Tong; David Fan; Jiachen Zhu 002; Yunyang Xiong; Xinlei Chen; Koustuv Sinha; Michael Rabbat; Yann LeCun; Saining Xie; Zhuang Liu 003 (CoRR, 2024)

Transformers without Normalization. - Jiachen Zhu 002; Xinlei Chen; Kaiming He; Yann LeCun; Zhuang Liu 003 (CVPR, 2025)

A Decade's Battle on Dataset Bias: Are We There Yet? - Zhuang Liu 003; Kaiming He (ICLR, 2025)

Deconstructing Denoising Diffusion Models for Self-Supervised Learning. - Xinlei Chen; Zhuang Liu 003; Saining Xie; Kaiming He (ICLR, 2025)

A Coefficient Makes SVRG Effective. - Yida Yin; Zhiqiu Xu; Zhiyuan Li; Trevor Darrell; Zhuang Liu 003 (ICLR, 2025)

Idiosyncrasies in Large Language Models. - Mingjie Sun; Yida Yin; Zhiqiu Xu; J. Zico Kolter; Zhuang Liu 003 (CoRR, 2025)

Scaling Language-Free Visual Representation Learning. - David Fan; Shengbang Tong; Jiachen Zhu 002; Koustuv Sinha; Zhuang Liu 003; Xinlei Chen; Michael Rabbat; Nicolas Ballas; Yann LeCun; Amir Bar; Saining Xie (CoRR, 2025)

Generative Modeling of Weights: Generalization or Memorization? - Boya Zeng; Yida Yin; Zhiqiu Xu; Zhuang Liu 003 (CoRR, 2025)