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cs.LG2023
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…
cs.LG2023★ 1 cited
Differentially Private Optimization for Smooth Nonconvex ERM
Changyu Gao, Stephen J. Wright
We develop simple differentially private optimization algorithms that move along directions of (expected) descent to find an approximate second-order solution for nonconvex ERM. We…