Zhou, Mo

28 publications

NeurIPS 2025 A Technical Report on “Erasing the Invisible”: The 2024 NeurIPS Competition on Stress Testing Image Watermarks Mucong Ding, Bang An, Tahseen Rabbani, Chenghao Deng, Anirudh Satheesh, Souradip Chakraborty, Mehrdad Saberi, Yuxin Wen, Kyle Rui Sang, Aakriti Agrawal, Xuandong Zhao, Mo Zhou, Mary-Anne Hartley, Lei Li, Yu-Xiang Wang, Vishal M. Patel, Soheil Feizi, Tom Goldstein, Furong Huang

ICLR 2025 Field-DiT: Diffusion Transformer on Unified Video, 3D, and Game Field Generation Kangfu Mei, Mo Zhou, Vishal M. Patel

CVPRW 2025 PartStickers: Generating Parts of Objects for Rapid Prototyping Mo Zhou, Josh Myers-Dean, Danna Gurari

ICCV 2025 UniRes: Universal Image Restoration for Complex Degradations Mo Zhou, Keren Ye, Mauricio Delbracio, Peyman Milanfar, Vishal M. Patel, Hossein Talebi

NeurIPS 2025 V2X-Radar: A Multi-Modal Dataset with 4D Radar for Cooperative Perception Lei Yang, Xinyu Zhang, Jun Li, Chen Wang, Jiaqi Ma, Zhiying Song, Tong Zhao, Ziying Song, Li Wang, Mo Zhou, Yang Shen, Kai Wu, Chen Lv

NeurIPS 2024 How Does Gradient Descent Learn Features --- a Local Analysis for Regularized Two-Layer Neural Networks Mo Zhou, Rong Ge

TMLR 2024 MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers Yatong Bai, Mo Zhou, Vishal M. Patel, Somayeh Sojoudi

ICLR 2023 Depth Separation with Multilayer Mean-Field Networks Yunwei Ren, Mo Zhou, Rong Ge

NeurIPSW 2023 How Does Gradient Descent Learn Features --- a Local Analysis for Regularized Two-Layer Neural Networks Mo Zhou, Rong Ge

ICML 2023 Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression Mo Zhou, Rong Ge

NeurIPSW 2023 Multi-Head CLIP: Improving CLIP with Diverse Representations and Flat Minima Mo Zhou, Xiong Zhou, Li Erran Li, Stefano Ermon, Rong Ge

ICLR 2023 Plateau in Monotonic Linear Interpolation --- a "Biased" View of Loss Landscape for Deep Networks Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge

JMLR 2023 Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees Mo Zhou, Jianfeng Lu

ICLR 2023 Understanding Edge-of-Stability Training Dynamics with a Minimalist Example Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge

ICLR 2023 Understanding the Robustness of Self-Supervised Learning Through Topic Modeling Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge

CVPR 2022 Enhancing Adversarial Robustness for Deep Metric Learning Mo Zhou, Vishal M. Patel

NeurIPS 2022 Resource-Adaptive Federated Learning with All-in-One Neural Composition Yiqun Mei, Pengfei Guo, Mo Zhou, Vishal Patel

COLT 2021 A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network Mo Zhou, Rong Ge, Chi Jin

ICCV 2021 Practical Relative Order Attack in Deep Ranking Mo Zhou, Le Wang, Zhenxing Niu, Qilin Zhang, Yinghui Xu, Nanning Zheng, Gang Hua

CVPR 2021 SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction Liushuai Shi, Le Wang, Chengjiang Long, Sanping Zhou, Mo Zhou, Zhenxing Niu, Gang Hua

NeurIPS 2021 Understanding Deflation Process in Over-Parametrized Tensor Decomposition Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou

ECCV 2020 Adversarial Ranking Attack and Defense Mo Zhou, Zhenxing Niu, Le Wang, Qilin Zhang, Gang Hua

AAAI 2020 Ladder Loss for Coherent Visual-Semantic Embedding Mo Zhou, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang, Gang Hua

ICML 2019 Toward Understanding the Importance of Noise in Training Neural Networks Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao

NeurIPS 2019 Towards Understanding the Importance of Shortcut Connections in Residual Networks Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S Du, Enlu Zhou, Tuo Zhao

ICCV 2017 Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding Zhenxing Niu, Mo Zhou, Le Wang, Xinbo Gao, Gang Hua

CVPR 2016 Ordinal Regression with Multiple Output CNN for Age Estimation Zhenxing Niu, Mo Zhou, Le Wang, Xinbo Gao, Gang Hua

WACV 2015 Automated Axon Segmentation from Highly Noisy Microscopic Videos John Bowler, Rogério Schmidt Feris, Liangliang Cao, Jun Wang, Mo Zhou