From cef8377c3f76e2bcec1dd61a4cb53d83e7bfd2d4 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Fri, 13 Sep 2019 17:35:49 +0200
Subject: [PATCH] spelling

---
 paper.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paper.md b/paper.md
index 74fb347..082c525 100755
--- a/paper.md
+++ b/paper.md
@@ -144,7 +144,7 @@ When querying $n>1$ points, the above procedure simply repeats $n$ times.
 
 #### A failure mode of such algorithms is sampling only a small neighbourhood of one point.
 The interpoint distance minimizing loss function we mentioned previously works on many functions; however, it is easy to write down a function where it will fail.
-For example, $1/x^2$ has a singularity will be sampled too densely around $x=0$ using this loss.
+For example, $1/x^2$ has a singularity and will be sampled too densely around $x=0$ using this loss.
 We can avoid this by defining additional logic inside the loss function.
 
 #### A solution is to regularize the loss such that this would be avoided.
-- 
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