Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Demystifying Lipschitz verification: positive matrices, negative results
Simon Kuang, Yuezhu Xu, S. Sivaranjani +1
The global Lipschitz constant of a neural network is related to robustness and generalization, yet unlike in many classical models, it is not plainly legible from the parameters. T…
cs.LG2026
Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
Simon Kuang, Xinfan Lin
We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual) neural network using layer-by-layer moment mat…
cs.LG2024
Structural Constraints for Physics-augmented Learning
Simon Kuang, Xinfan Lin
When the physics is wrong, physics-informed machine learning becomes physics-misinformed machine learning. A powerful black-box model should not be able to conceal misconceived phy…