<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Stochastic Gradient Descent on a.dev</title><link>https://aditya08.github.io/tags/stochastic-gradient-descent/</link><description>Recent content in Stochastic Gradient Descent on a.dev</description><generator>Hugo -- 0.148.0</generator><language>en</language><lastBuildDate>Mon, 13 Jan 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://aditya08.github.io/tags/stochastic-gradient-descent/index.xml" rel="self" type="application/rss+xml"/><item><title>Communication-Efficient, 2D Parallel Stochastic Gradient Descent for Distributed-Memory Optimization</title><link>https://aditya08.github.io/publications/hybridsgd/</link><pubDate>Mon, 13 Jan 2025 00:00:00 +0000</pubDate><guid>https://aditya08.github.io/publications/hybridsgd/</guid><description>This paper presents HybridSGD, a 2D parallel stochastic gradient descent method that trades off communication between s-step SGD and Federated Averaging</description></item></channel></rss>