3 citations · 8 across the 12 of their papers we have counts for
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math.OC2023
Accelerating optimization over the space of probability measures
Shi Chen, Qin Li, Oliver Tse +1
The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine learning applications. While m…
cs.LG2023★ 1 cited
Correcting Auto-Differentiation in Neural-ODE Training
Yewei Xu, Shi Chen, Qin Li
Does the use of auto-differentiation yield reasonable updates for deep neural networks (DNNs)? Specifically, when DNNs are designed to adhere to neural ODE architectures, can we tr…
math.NA2023
On optimal bases for multiscale PDEs and Bayesian homogenization
Shi Chen, Zhiyan Ding, Qin Li +1
PDE solutions are numerically represented by basis functions. Classical methods employ pre-defined bases that encode minimum desired PDE properties, which naturally cause redundant…