A closed-form upper bound on the maximum Hessian eigenvalue of cross-entropy loss is derived for smooth nonlinear neural networks.
Benoˆıt Assi, Bernd A
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
VRAdam hybridizes Adam's per-parameter adaptation with a physics-inspired velocity regularizer to stabilize training at the edge of stability, delivering better empirical performance than AdamW and O(ln(N)/sqrt(N)) convergence bounds under mild assumptions.
citing papers explorer
-
Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks
A closed-form upper bound on the maximum Hessian eigenvalue of cross-entropy loss is derived for smooth nonlinear neural networks.
-
A Physics-Inspired Optimizer: Velocity Regularized Adam
VRAdam hybridizes Adam's per-parameter adaptation with a physics-inspired velocity regularizer to stabilize training at the edge of stability, delivering better empirical performance than AdamW and O(ln(N)/sqrt(N)) convergence bounds under mild assumptions.