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Found 3 papers, 1 papers with code
To Each Optimizer a Norm, To Each Norm its Generalization
For under-parameterized linear classification, we prove that for any linear classifier separating the data, there exists a family of quadratic norms ||.||_P such that the classifier's direction is the same as that of the maximum P-margin solution.
GAIT: A Geometric Approach to Information Theory
We advocate the use of a notion of entropy that reflects the relative abundances of the symbols in an alphabet, as well as the similarities between them.
Beyond Local Nash Equilibria for Adversarial Networks
Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE).
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