Update README.md

README.md

@@ -28,7 +28,7 @@ NULA is trained to reduce this dependence and instead form representations that
 
 Downsampling operations are linear maps from a high-dimensional space to a lower-dimensional one.
 
-By the Rank-Nullity theorem, this matrix has a massive NULL space ;). 
+By the Rank-Nullity theorem, this matrix has a massive NULL space.
 
 An attacker can exploit this: they utilize the discarded samples of these downsampling operations as extra degrees of freedom. 
 
@@ -40,8 +40,8 @@ The result is an image perceptually identical to the original, with a manipulate
 
 [BlurPool: what it does mechanically and why it addresses the problem]
 [SE blocks: what they add]
-The augmentation functions used during adversarial training are in [`augmentations.py`](augmentations.py). 
-The training script is [`train_robust.py`](train_robust.py).
+- For augmentation functions, see [`augmentations.py`](augmentations.py).  
+- For the adversarial training loop, see [`train_robust.py`](train_robust.py).
 
 
 ### FIRST EVALUATION (Base)