Awesome

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Code for the Neurips'22 paper called "Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study".

The repo includes the source code for various experiments in the paper.

Requirement

To run the code, please create a conda environment and install the following packages

conda create -n extra python=3.7.7

conda activate extra

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

pip install pyyaml

pip install scipy==1.1.0

pip install matplotlib

pip install seaborn

Browsing the folder and files

The folder is organized as follows:

extra/: contains the code for the experiment on extrapolation

spectral: contains the code for the experiment on spectral bias

vaec: contains the code for the experiment on vaec dataset

To run experiment on extrapolation

cd extra
sh script_extra.sh

For the experiment on VAEC dataset

Firstly, download the dataset base on the instruction in the original repo. Then run the followng command to generate data:

cd extra/dset_gen
sh generated_data.sh

Training:

cd script
sh context_norm_scale_extrap.sh

To run the experiment on spectral bias

To train the network on MNIST dataset: run

cd spectral
sh script_spectral.sh

Next, to visualzie the result, run

python eval.py

Reference:

https://github.com/jinglingli/nn-extrapolate https://github.com/taylorwwebb/learning_representations_that_support_extrapolation https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation/tree/master/conditional_generation_with_gan

Cite as:

If you use this code, please cite

@inproceedings{
wu2022extrapolation,
title={Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study},
author={Yongtao Wu and Zhenyu Zhu and Fanghui Liu and Grigorios G Chrysos and Volkan Cevher},
booktitle={Advances in Neural Information Processing Systems},
year={2022},
}