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 ...

In this course, you’ll learn theoretical foundations of optimization methods used for training deep machine learning models. Why does gradient descent work? Specifically, what can we guarantee about ...

The expectation maximization algorithm enables parameter ... In theory, other numerical optimization techniques, such as gradient descent or Newton-Raphson, could be used instead of expectation ...

The study was published in the journal Science. One popular technique for model optimization is gradient descent. It can be applied methodically to detect departures from the intended goal ...