Nature

Stochastic gradient descent for optimization for nuclear systems

The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Nature

A dual enhanced stochastic gradient descent method with dynamic momentum and step size adaptation for improved optimization performance

In modern machine learning, optimization algorithms are crucial; they steer the training process by skillfully navigating through complex, high-dimensional loss landscapes. Among these, stochastic ...
EurekAlert!

Efficient stochastic parallel gradient descent training for on-chip optical processors

a) Conceptual diagram of the on-chip optical processor used for optical switching and channel decoder in an MDM optical communications system. (b) Integrated reconfigurable optical processor schematic ...