Stochastic gradient Langevin dynamics
id:
stochastic-gradient-langevin-dynamics-297-1826821
title:
Stochastic gradient Langevin dynamics
text:
Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins–Monro optimization algorithm, and Langevin dynamics, a mathematical extension of molecular dynamics models. Like stochastic gradient descent, SGLD is an iterative optimization algorithm which uses minibatching to create a stochastic gradient estimator, as used in SGD to optimize a differentiable objective function. Unlike traditional SGD,
brand slug:
wiki
category slug:
encyclopedia
description:
Optimization and sampling technique
original url:
https://en.wikipedia.org/wiki/Stochastic_gradient_Langevin_dynamics
date created:
date modified:
2024-03-01T22:11:18Z
main entity:
{"identifier":"Q60750312","url":"https://www.wikidata.org/entity/Q60750312"}
image:
{"content_url":"https://upload.wikimedia.org/wikipedia/commons/e/e3/Non-Convex_Objective_Function.gif","width":451,"height":337}
fields total:
13
integrity:
15