This folder is the supplementary material for *Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher*. This folder contains supplementary and source code for our paper.

- The file *supplementary.pdf* contains proof of our risk bound and other experiment details.
- The folder *code/toy* is the module for our toy distillation experiment.
- The folder *code/resnet* is the module for our ResNet distillation experiment.
- The folder *code/experiment_code* contains the code of running all experiments and plotting figures. The subfolder is named by number of figure. 

Files with *.py* suffix are executable by python. Files with *.ipynb* suffix can be opened with jupyter notebook. Files with *.nb* suffix can be opened with Mathematica.

