Zeke Xie

According to our database, Zeke Xie authored at least 25 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis.

Shitong Shao, Zikai Zhou, Lichen Bai, Haoyi Xiong, Zeke Xie

CoRR, 2024

Alignment of Diffusion Models: Fundamentals, Challenges, and Future.

Buhua Liu, Shitong Shao, Bao Li, Lichen Bai, Zhiqiang Xu, Haoyi Xiong, James Kwok, Sumi Helal, Zeke Xie

CoRR, 2024

Channel-wise Influence: Estimating Data Influence for Multivariate Time Series.

Muyao Wang, Zeke Xie, Bo Chen

CoRR, 2024

Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization.

Zipeng Qi, Lichen Bai, Haoyi Xiong, Zeke Xie

CoRR, 2024

Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents.

Haoyi Xiong, Zhiyuan Wang, Xuhong Li, Jiang Bian, Zeke Xie, Shahid Mumtaz, Laura E. Barnes

CoRR, 2024

VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model.

Jinze Yang, Haoran Wang, Zining Zhu, Chenglong Liu, Meng Wymond Wu, Zeke Xie, Zhong Ji, Jungong Han, Mingming Sun

CoRR, 2024

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior.

Zhongrui Yu, Haoran Wang, Jinze Yang, Hanzhang Wang, Zeke Xie, Yunfeng Cai, Jiale Cao, Zhong Ji, Mingming Sun

CoRR, 2024

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models.

Hanzhang Wang, Haoran Wang, Jinze Yang, Zhongrui Yu, Zeke Xie, Lei Tian, Xinyan Xiao, Junjun Jiang, Xianming Liu, Mingming Sun

CoRR, 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, Xiangyang Ji

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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, Mingming Sun

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Overlooked Structure of Stochastic Gradients.

Zeke Xie, Qian-Yuan Tang, Mingming Sun, Ping Li

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dataset Pruning: Reducing Training Data by Examining Generalization Influence.

Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, Ping Li

Proceedings of the Eleventh International Conference on Learning Representations, 2023

S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields.

Zeke Xie, Xindi Yang, Yujie Yang, Qi Sun, Yixiang Jiang, Haoran Wang, Yunfeng Cai, Mingming Sun

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Rethinking the Structure of Stochastic Gradients: Empirical and Statistical Evidence.

Zeke Xie, Qian-Yuan Tang, Zheng He, Mingming Sun, Ping Li

CoRR, 2022

On the Power-Law Spectrum in Deep Learning: A Bridge to Protein Science.

Zeke Xie, Qian-Yuan Tang, Yunfeng Cai, Mingming Sun, Ping Li

CoRR, 2022

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum.

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama

Proceedings of the International Conference on Machine Learning, 2022

Sparse Double Descent: Where Network Pruning Aggravates Overfitting.

Zheng He, Zeke Xie, Quanzhi Zhu, Zengchang Qin

Proceedings of the International Conference on Machine Learning, 2022

2021

Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting.

Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama

Neural Comput., 2021

Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization.

Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama

Proceedings of the 38th International Conference on Machine Learning, 2021

A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima.

Zeke Xie, Issei Sato, Masashi Sugiyama

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Stable Weight Decay Regularization.

Zeke Xie, Issei Sato, Masashi Sugiyama

CoRR, 2020

Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia.

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama

CoRR, 2020

A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast.

Zeke Xie, Issei Sato, Masashi Sugiyama

CoRR, 2020

2017

A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors.

Zeke Xie, Issei Sato

Proceedings of The 9th Asian Conference on Machine Learning, 2017