# Supplementary material for the paper "Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures"

This folder contains
1. a fork of the [ASDL](https://github.com/kazukiosawa/asdl) library with the implementation of K-FAC-expand and K-FAC-reduce (see the comment in the README),
2. a fork of the [MLCommons Algorithmic Efficiency benchmark repository](https://github.com/mlcommons/algorithmic-efficiency/) with the code and best hyperparameters for the ogbg-molpcba and ImageNet ViT experiments (see the comment in the README),
3. and the appendix of the paper in `appendix.pdf`.
