Update 2022-2-8-quantization-in-practice.md

Author: subramen

_posts/2022-2-8-quantization-in-practice.md

@@ -1,7 +1,7 @@
 ---
 layout: blog_detail
 title: 'Practical Quantization in PyTorch'
-author: Suraj Subramanian, Jerry Zhang
+author: Suraj Subramanian, Mark Saroufim, Jerry Zhang
 featured-img: ''
 ---
 
@@ -464,8 +464,6 @@ That was a lot to digest, congratulations for sticking with it! Next, we'll take
 
 We'll also cover a cool new feature in PyTorch Quantization called Define-by-Run, that tries to ease this constraint by needing only subsets of the model's computational graph to be free of dynamic flow. Check out the [Define-by-Run poster at PTDD'21](https://s3.amazonaws.com/assets.pytorch.org/ptdd2021/posters/C8.png) for a preview.
 
-***Thanks to Mark Saroufim for useful comments and feedback!***
-
 
 ## References
 [[1]] Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., & Keutzer, K. (2021). A survey of quantization methods for efficient neural network inference. arXiv preprint arXiv:2103.13630.