Search Results for author:
Found 2 papers, 0 papers with code
SGD Can Converge to Local Maxima
Stochastic gradient descent (SGD) is widely used for the nonlinear, nonconvex problem of training deep neural networks, but its behavior remains poorly understood.
Neural tangent kernel eigenvalues accurately predict generalization
Finding a quantitative theory of neural network generalization has long been a central goal of deep learning research.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
