Update README.md

README.md

@@ -16,13 +16,13 @@ metrics:
 For a thorough description of our models trained on the ImageNet-1k dataset, please read our preprint, 
 *Towards Errorless Training ImageNet-1k*, which is available at [ADD LINK to arXiv preprint]. 
 In ../ImageNet-1k/MATLAB, we give parameters for 6 models, which are listed in the table below. 
-Each model has the following architecture: \[17\times 40\times 2=1360\] FNNs, all with homogeneous architecture (900-256-25 or 900-256-77-25), 
+Each model has the following architecture: 17x40x2=1360 FNNs, all with homogeneous architecture (900-256-25 or 900-256-77-25), 
 working in parrallel to produce 1360 predictions which determine a final prediction using the majority voting protocol. 
-We trained models using the following transformation of the $64\times 64$ downsampled ImageNet-1k dataset:
- - downsampled images to $32\times 32$, using the mean values of non-overlapping $2\times 2$ grid cells and
+We trained models using the following transformation of the 64x64 downsampled ImageNet-1k dataset:
+ - downsampled images to 32x32, using the mean values of non-overlapping 2x2 grid cells and
  - trimmed off top row, bottom row, left-most column, and right-most column.
 
- This transformed data results in $30\times 30$ images, hence 900-dimensional input vectors.
+ This transformed data results in 30x30 images, hence 900-dimensional input vectors.
 
 
 | Model | Training Method | FNN Architecture | Accuracy (%) |