MIT researchers have developed a new machine-learning-based adaptive control algorithm for autonomous drones. The ...

It is rare that a problem is submitted to an optimization algorithm "as is ... Furthermore, DBLDOG needs only gradient calls for the update of the Cholesky factor of an approximate Hessian. performs a ...

Gradient descent algorithms take the loss function and use partial derivatives to determine what each variable (weights and biases) in the network contributed to the loss value. It then moves ...

In 1847, the French mathematician Augustin-Louis Cauchy was working on a suitably complicated example — astronomical calculations — when he pioneered a common method of optimization now known as ...

Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.

An evolutionary algorithm is any algorithm that loosely mimics ... DEO typically takes much longer to train a deep neural network than standard stochastic gradient descent (SGD) optimization ...