Search Results for author:
Found 2 papers, 2 papers with code
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sherman-Morrison inversion formula to calculate the inverse.
Angle based dynamic learning rate for gradient descent
In our work, we propose a novel yet simple approach to obtain an adaptive learning rate for gradient-based descent methods on classification tasks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
