Derives closed-form gradient of WS upper bound on Hessian max eigenvalue for 3-layer cross-entropy NNs and proposes HSR regularization to steer toward flat minima.
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A closed-form upper bound on the maximum Hessian eigenvalue of cross-entropy loss is derived for smooth nonlinear neural networks.
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Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks
Derives closed-form gradient of WS upper bound on Hessian max eigenvalue for 3-layer cross-entropy NNs and proposes HSR regularization to steer toward flat minima.
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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.