Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Fix typo in name #1577

Open
wants to merge 3 commits into
base: site
Choose a base branch
from
Open

Fix typo in name #1577

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
04e218f
Update 2020-08-18-pytorch-1.6-now-includes-stochastic-weight-averagin…
vincentqb Feb 14, 2024
62233ef
Merge branch 'site' into patch-1
vincentqb Feb 15, 2024
35a79eb
Merge branch 'site' into patch-1
vincentqb Feb 22, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion _posts/2020-08-18-pytorch-1.6-now-includes-stochastic-weight-averaging.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
---
layout: blog_detail
title: 'PyTorch 1.6 now includes Stochastic Weight Averaging'
author: Pavel Izmailov, Andrew Gordon Wilson and Vincent Queneneville-Belair
author: Pavel Izmailov, Andrew Gordon Wilson and Vincent Quenneville-Belair
---

Do you use stochastic gradient descent (SGD) or Adam? Regardless of the procedure you use to train your neural network, you can likely achieve significantly better generalization at virtually no additional cost with a simple new technique now natively supported in PyTorch 1.6, Stochastic Weight Averaging (SWA) [1]. Even if you have already trained your model, it’s easy to realize the benefits of SWA by running SWA for a small number of epochs starting with a pre-trained model. [Again](https://twitter.com/MilesCranmer/status/1282140440892932096) and [again](https://twitter.com/leopd/status/1285969855062192129), researchers are discovering that SWA improves the performance of well-tuned models in a wide array of practical applications with little cost or effort!
Expand Down