10 citations · 14 across the 6 of their papers we have counts for
6 papers
A deep learning framework for jointly solving transient Fokker-Planck equations with arbitrary parameters and initial distributions
Xiaolong Wang, Jing Feng, Qi Liu +3
Efficiently solving the Fokker-Planck equation (FPE) is central to analyzing complex parameterized stochastic systems. However, current numerical methods lack parallel computation…
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
Chengli Tan, Yubo Zhou, Haishan Ye +7
Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone…
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
Chengli Tan, Jiangshe Zhang, Junmin Liu +2
Recently, sharpness-aware minimization (SAM) has attracted much attention because of its surprising effectiveness in improving generalization performance. However, compared to stoc…
Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling
Xiaoli Wei, Chunxia Zhang, Hongtao Wang +5
Accurate interpolation of seismic data is crucial for improving the quality of imaging and interpretation. In recent years, deep learning models such as U-Net and generative advers…
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Zixiang Zhao, Jiangshe Zhang, Xiang Gu +5
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in hi…
Learning Non-Vacuous Generalization Bounds from Optimization
Chengli Tan, Jiangshe Zhang, Junmin Liu +1
One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approach…