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 response ...

At the end of the concrete plaza that forms the courtyard of the Salk Institute in La Jolla, California, there is a three-hundred-fifty-foot drop to the Pacific Ocean. Sometimes people explore that ...

The Annals of Applied Probability, Vol. 27, No. 6 (December 2017), pp. 3255-3304 (50 pages) The asymptotic behavior of the stochastic gradient algorithm using biased gradient estimates is analyzed.

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 ...

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict a person's bank savings account balance based on their age, years of ...

Dr. James McCaffrey of Microsoft Research explains stochastic gradient descent (SGD) neural network training, specifically implementing a bio-inspired optimization technique called differential ...

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. “The rapid ...