In two, we recognized that many machine learning problems involve minimizing empirical risk functions with well-behaved population risks. Instead of analyzing the non-convex empirical risk directly, ...

Samy Wu Fung, Department of Applied Mathematics and Statistics, Colorado School of Mines Efficient Training of Infinite-depth Neural Networks via Jacobian-free Backpropagation A promising trend in ...

A machine learning algorithm, on the other hand, might recognize that the strongest signal differentiating a dog from a cat is whether the photo is a bright outdoor photo or a dim indoor photo.

Proximal algorithms are useful for obtaining solutions to difficult optimization problems, especially those involving nonsmooth or composite objective functions. A proximal algorithm is one whose ...

1. Mathematical optimization and machine learning are two highly sophisticated advanced analytics software technologies that are used in a vast array of applications, making it hard to swiftly and ...