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g3doc/framework.md
@@ -15,7 +15,7 @@ Convolutional NNs and Recurrent NNs).
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Note that the generalized neighbor loss equation is flexible and can have other
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forms besides the one illustrated above. For example, we can also select
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-\\(\sum_{x_j \in \mathcal{N}(x_i)}\mathcal{E}(y_i,g_\theta(x_j)\\) to be the
+\\(\sum_{x_j \in \mathcal{N}(x_i)}\mathcal{E}(y_i,g_\theta(x_j))\\) to be the
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neighbor loss, which calculates the distance between the ground truth \\(y_i\\)
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and the prediction from the neighbor \\(g_\theta(x_j)\\). This is commonly used
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in adversarial learning
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