Fix typo in README

tum-pbs · Sep 4, 2024 · 5d0fb15 · 5d0fb15
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diff --git a/README.md b/README.md
@@ -36,7 +36,7 @@ Then the dot product between $\boldsymbol{g}_{ConFIG}$ and each loss-specific gr
 
 * **Is the ConFIG computationally expensive?**
 
-	Like many other gradient-based methods, ConFIG needs to calculate each loss's gradient in every optimization iteration, which could be computationally expensive when the number of losses increases. However, we also introduce a **momentum-based method** where we can reduce the computational cost **close to or even lower than a standard optimization procedure** with a slight degeneration in accuracy. This momentum-based method is also applied to another gradient-based method.
+	Like many other gradient-based methods, ConFIG needs to calculate each loss's gradient in every optimization iteration, which could be computationally expensive when the number of losses increases. However, we also introduce a **momentum-based method** where we can reduce the computational cost **close to or even lower than a standard optimization procedure** with a slight degeneration in accuracy. This momentum-based method is also applicable to other gradient-based methods.
 
 ## Paper Info
 

diff --git a/docs/index.md b/docs/index.md
@@ -41,7 +41,7 @@ Then the dot product between $\mathbf{g}_{ConFIG}$ and each loss-specific gradie
 
 * **Is the ConFIG computationally expensive?**
 
-	Like many other gradient-based methods, ConFIG needs to calculate each loss's gradient in every optimization iteration, which could be computationally expensive when the number of losses increases. However, we also introduce a **momentum-based method** where we can reduce the computational cost **close to or even lower than a standard optimization procedure** with a slight degeneration in accuracy. This momentum-based method is also applied to another gradient-based method.
+	Like many other gradient-based methods, ConFIG needs to calculate each loss's gradient in every optimization iteration, which could be computationally expensive when the number of losses increases. However, we also introduce a **momentum-based method** where we can reduce the computational cost **close to or even lower than a standard optimization procedure** with a slight degeneration in accuracy. This momentum-based method is also applicable to other gradient-based methods.
 
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     * **Is the ConFIG computationally expensive?**
-    	Like many other gradient-based methods, ConFIG needs to calculate each loss's gradient in every optimization iteration, which could be computationally expensive when the number of losses increases. However, we also introduce a **momentum-based method** where we can reduce the computational cost **close to or even lower than a standard optimization procedure** with a slight degeneration in accuracy. This momentum-based method is also applied to another gradient-based method.
+    	Like many other gradient-based methods, ConFIG needs to calculate each loss's gradient in every optimization iteration, which could be computationally expensive when the number of losses increases. However, we also introduce a **momentum-based method** where we can reduce the computational cost **close to or even lower than a standard optimization procedure** with a slight degeneration in accuracy. This momentum-based method is also applicable to other gradient-based methods.
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