3 papers
cs.LG2026
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…
cs.LG2026
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…
physics.comp-ph2026
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…