Noisy Correspondence Rectification in Multimodal Clustering Space for Cross-Modal Matching.
Shuo Yang, Yancheng Long, Yujie Wei, Zeke Xie, Hongxun Yao, Min Xu, Liqiang Nie.
In IEEE Transactions on Pattern Analysis and Machine Intelligence. [TPAMI 2025]
Channel Matters: Estimating Channel Influence for Multivariate Time Series.
Muyao Wang, Zeke Xie#, Bo Chen#, Hongwei Liu, and James Kwok.
In Neural Information Processing Systems. [NeurIPS 2025]
UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation.
Jiyu Guo, Shuo Yang, Yiming Huang, Yancheng Long, Xiaobo Xia, Xiu Su, Bo Zhao, Zeke Xie, and Liqiang Nie..
In Neural Information Processing Systems. [NeurIPS 2025]
Pre-trained Molecular Language Models with Random Functional Group Masking.
Tianhao Peng , Yuchen Li, Xuhong Li , Jiang Bian, Zeke Xie, Ning Sui, Shahid Mumtaz, Yanwu Xu, Linghe Kong, and Haoyi Xiong.
In npj Artificial Intelligence, Springer Nature Press. [npj AI 2025]
Golden Noise for Diffusion Models: A Learning Framework.
Zikai Zhou, Shitong Shao, Lichen Bai, Shufei Zhang, Zhiqiang Xu, Bo Han, and Zeke Xie#.
In International Conference on Computer Vision. [ICCV 2025][Blog]
Investigating the Overlooked Hessian Structure: From CNNs to LLMs.
Qian-Yuan Tang*, Yufei Gu*, Yunfeng Cai, Mingming Sun, Ping Li, Xun Zhou, and Zeke Xie#.
In International Conference on Machine Learning. [ICML 2025]
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples.
Chengqian Gao, Haonan Li, Liu Liu, Zeke Xie, Peilin Zhao, and Zhiqiang Xu.
In International Conference on Machine Learning. [ICML 2025]
Mono2Stereo: A Benchmark and Empirical Study for Stereo Conversion.
Songsong Yu, Yuxin Chen, Zhongang Qi, Zeke Xie, Yifan Wang, Lijun Wang, Ying Shan, and Huchuan Lu.
In IEEE/CVF Conference on Computer Vision and Pattern Recognition. [CVPR 2025]
IV-mixed Sampler: Leveraging Image Diffusion Model for Enhanced Video Synthesis.
Shitong Shao*, Zikai Zhou*, Lichen Bai, Haoyi Xiong, and Zeke Xie#.
In International Conference on Learning Representations. [ICLR 2025][Project Page][Blog]
Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection.
Lichen Bai, Shitong Shao, Zikai Zhou, Zipeng Qi, Zhiqiang Xu, Haoyi Xiong, and Zeke Xie#.
In International Conference on Learning Representations. [ICLR 2025][Blog][Media][Media]
Neural Field Classifiers via Target Encoding and Classification Loss.
Xindi Yang*, Zeke Xie#*, Xiong Zhou, Boyu Liu, Buhua Liu, Yi Liu, Haoran Wang, Yunfeng Cai, and Mingming Sun.
In International Conference on Learning Representations. [ICLR 2024]
Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data.
Xiong Zhou, Xianming Liu, Hao Yu, Jialiang Wang, Zeke Xie, Junjun Jiang, and Xiangyang Ji.
In International Conference on Learning Representations. [ICLR 2024]
On the Overlooked Structure of Stochastic Gradients.
Zeke Xie, Qian-Yuan Tang, Mingming Sun, and Ping Li.
In Neural Information Processing Systems. [NeurIPS 2023]
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective.
Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, and Masashi Sugiyama.
In Neural Information Processing Systems. [NeurIPS 2023][Blog][Blog][Media][Media]
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields.
Zeke Xie*, Xindi Yang*, Yujie Yang, Qi Sun, Yixiang Jiang, Haoran Wang, Yi Liu, Yunfeng Cai, and Mingming Sun.
In International Conference on Computer Vision. [ICCV 2023][Blog][Media][Media][Media][Media][Media]
Dataset Pruning: Reducing Training Data by Examining Generalization Influence.
Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, and Ping Li.
In International Conference on Learning Representations. [ICLR 2023]
Sparse Double Descent: Where Network Pruning Aggravates Overfitting.
Zheng He, Zeke Xie, Quanzhi Zhu, and Zengchang Qin.
In International Conference on Machine Learning. [ICML 2022][Blog]
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum.
Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, and Masashi Sugiyama.
In International Conference on Machine Learning. [ICML 2022, Oral, 2%][Blog][Media][Media]
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization.
Zeke Xie, Li Yuan, Zhanxing Zhu, and Masashi Sugiyama.
In International Conference on Machine Learning. [ICML 2021]
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting.
Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, and Masashi Sugiyama.
Neural Computation, MIT Press. [NECO 2021]
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima.
Zeke Xie, Issei Sato, and Masashi Sugiyama.
In International Conference on Learning Representations. [ICLR 2021][Blog][Blog][Media]