Chengli Tan

According to our database, Chengli Tan authored at least 15 papers between 2021 and 2026.

Collaborative distances:

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

2026

Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching.

Yuhe Bai, Chengli Tan, Jiaqi Li, Xiangjun Wang, Zhikun Zhang

CoRR, April, 2026

A deep learning framework for jointly solving transient Fokker-Planck equations with arbitrary parameters and initial distributions.

Xiaolong Wang, Jing Feng, Qi Liu, Chengli Tan, Yuanyuan Liu, Yong Xu

CoRR, April, 2026

Warm-start or cold-start? A comparison of generalizability in gradient-based hyperparameter tuning.

Yubo Zhou, Jun Shu, Chengli Tan, Haishan Ye, Quanziang Wang, Junmin Liu, Deyu Meng, Ivor W. Tsang, Guang Dai

Neural Networks, 2026

2025

Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization.

Chengli Tan, Yubo Zhou, Haishan Ye, Guang Dai, Junmin Liu, Zengjie Song, Jiangshe Zhang, Zixiang Zhao, Yunda Hao, Yong Xu

CoRR, May, 2025

Frequency-Enhanced Subspace Clustering Network With Information Bottleneck.

Mengran Hou, Mengyao Li, Chengli Tan, Junmin Liu, Jinhai Li, Huirong Li

IEEE Trans. Multim., 2025

Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy.

Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao

J. Mach. Learn. Res., 2025

2024

Sharpness-Aware Lookahead for Accelerating Convergence and Improving Generalization.

Chengli Tan, Jiangshe Zhang, Junmin Liu, Yihong Gong

IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

Low-dimensional intrinsic dimension reveals a phase transition in gradient-based learning of deep neural networks.

Chengli Tan, Jiangshe Zhang, Junmin Liu, Zixiang Zhao

Int. J. Mach. Learn. Cybern., November, 2024

Understanding Short-Range Memory Effects in Deep Neural Networks.

Chengli Tan, Jiang-She Zhang, Junmin Liu

IEEE Trans. Neural Networks Learn. Syst., August, 2024

Seismic Data Interpolation via Denoising Diffusion Implicit Models With Coherence-Corrected Resampling.

Xiao-Li Wei, Chunxia Zhang, Hongtao Wang, Chengli Tan, Deng Xiong, Baisong Jiang, Jiangshe Zhang, Sang-Woon Kim

IEEE Trans. Geosci. Remote. Sens., 2024

2023

Robust Teacher: Self-correcting pseudo-label-guided semi-supervised learning for object detection.

Shijie Li, Junmin Liu, Weilin Shen, Jianyong Sun, Chengli Tan

Comput. Vis. Image Underst., October, 2023

Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution.

Zixiang Zhao, Jiangshe Zhang, Xiang Gu, Chengli Tan, Shuang Xu, Yulun Zhang, Radu Timofte, Luc Van Gool

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

2022

Trajectory-dependent Generalization Bounds for Deep Neural Networks via Fractional Brownian Motion.

Chengli Tan, Jiangshe Zhang, Junmin Liu

CoRR, 2022

Automatic Velocity Picking Using Unsupervised Ensemble Learning.

Hongtao Wang, Jiangshe Zhang, Chunxia Zhang, Zixiang Zhao, Chengli Tan, Zhiyu Yang, Weifeng Geng

CoRR, 2022

2021

Understanding Long Range Memory Effects in Deep Neural Networks.

Chengli Tan, Jiangshe Zhang, Junmin Liu

CoRR, 2021