diff --git a/_posts/2020-08-18-pytorch-1.6-now-includes-stochastic-weight-averaging.md b/_posts/2020-08-18-pytorch-1.6-now-includes-stochastic-weight-averaging.md
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--- a/_posts/2020-08-18-pytorch-1.6-now-includes-stochastic-weight-averaging.md
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 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!