Search Results for author:
Found 2 papers, 0 papers with code
A note on Demailly's approach towards a conjecture of Griffiths
We prove that a "cushioned" Hermitian-Einstein-type equation proposed by Demailly in an approach towards a conjecture of Griffiths on the existence of a Griffiths positively curved metric on a Hartshorne ample vector bundle, has an essentially unique solution when the bundle is stable.
Differential Geometry
Revisiting the Stability of Stochastic Gradient Descent: A Tightness Analysis
We provide empirical evidence that this condition holds for several loss functions, and provide theoretical evidence that the known tight SGD stability bounds for convex and non-convex loss functions can be circumvented by HC loss functions, thus partially explaining the generalization of deep neural networks.
