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research/README.md

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@@ -48,9 +48,10 @@ Processes." ICML 2020](https://arxiv.org/abs/1901.00409)
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## [Denoised Smoothing: A Provable Defense for Pretrained Classifiers](third_party/denoised_smoothing)
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The implementations of Denoised Smoothing [5] are provided in the
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`third_party/denoised_smoothing` folder on a strict "as is" basis, without warranties or
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conditions of any kind. Also, these implementations may not be compatible with
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certain TensorFlow versions or Python versions.
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`third_party/denoised_smoothing` folder on a strict "as is" basis, without
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warranties or conditions of any kind. Also, these implementations may not be
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compatible with certain TensorFlow versions or Python versions.
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[[5] H. Salman, M.Sun, C. Mitelut, G. Yang, A. Kapoor., Z. Kolter "Denoised Smoothing:
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A Provable Defense for Pretrained Classifiers." NeurIPS 2020](https://arxiv.org/abs/2003.01908)
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[[5] H. Salman, M.Sun, C. Mitelut, G. Yang, A. Kapoor., Z. Kolter "Denoised
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Smoothing: A Provable Defense for Pretrained Classifiers." NeurIPS
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2020](https://arxiv.org/abs/2003.01908)

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