Xi Chen, Bruce E. Ankenman, Barry L. Nelson, Enhancing Stochastic Kriging Metamodels with Gradient Estimators, Operations Research, Vol. 61, No. 2 (March-April 2013), pp. 512-528 Free online reading ...

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 ...

The study of gradient flows and large deviations in stochastic processes forms a vital link between microscopic randomness and macroscopic determinism. By characterising how systems evolve in ...

A new publication from Opto-Electronic Advances; DOI 10.29026/oea.2024.230182 , discusses efficient stochastic parallel gradient descent training for on-chip optical processors.

Omar Besbes, Yonatan Gur, Assaf Zeevi, Non-Stationary Stochastic Optimization, Operations Research, Vol. 63, No. 5 (September-October 2015), ... Kalai AT, McMahan HB (2005) Online convex optimiza- ...

A new technical paper titled “Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent” was published by researchers at Imperial College London. Abstract “The ...

Gradient descent is an essential tool of modern applied research, but there are many common problems for which it does not work well. But before this research, there was no comprehensive understanding ...