hypergrad 
Simple and extensible hypergradient for PyTorch
Installation
First, install torch and its accompanying torchvision appropriately. Then,
pip install hypergrad
Methods
Implicit hypergradient approximation (via approximated inverse Hessian-vector product)
- conjugate gradient
- Neumann-series approximation
- Nyström method
Implementation of these methods can be found in hypergrad/approximate_ihvp.py
Citation
To cite this repository,
@software{hypergrad,
author = {Ryuichiro Hataya},
title = {{hypergrad}},
url = {https://github.com/moskomule/hypergrad},
year = {2023}
}
hypergrad is developed as a part of the following research projects:
@inproceedings{hataya2023nystrom,
author = {Ryuichiro Hataya and Makoto Yamada},
title = {{Nystr\"om Method for Accurate and Scalable Implicit Differentiation}},
booktitle = {AISTATS},
year = {2023}
}
1 maintainer
Metadata
1 maintainer
Release files for hypergrad 0.0.1
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