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Deep Learning Interpretability with Symbolic Regression
| Date | Stars |
|---|---|
| 2026-07-31 | 301 |
| 2026-08-06 | 303 |
Today
+2 stars today
This week
— stars this week
This month
— stars this month
Momentum
8.0
growth rate 0.00%/day
*SymTorch* allows you to approximate the behaviour of components within deep learning models with symbolic equations using [PySR](https://ai.damtp.cam.ac.uk/pysr/).
## Installation
SymTorch is available on [PyPI](https://pypi.org/) as `torch-symbolic`:
```bash
pip install torch-symbolic
```
## Paper
Our paper is available on [arXiv](https://arxiv.org/abs/2602.21307). You can also visit the [accompanying website](https://astroautomata.github.io/symtorch-web/) for more information.
## Documentation
Full documentation is available at [ReadTheDocs](https://symtorch.readthedocs.io/en/latest/).
## Citation
If you use SymTorch in your research, please cite:
```bibtex
@misc{symtorch2026,
title = {SymTorch: A Framework for Symbolic Distillation of Deep Neural Networks},
author = {Tan, Elizabeth S.Z. and Soubki, Adil and Cranmer, Miles},
year = {2026},
eprint = {2602.21307},
archivePrefix= {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2602.21307}
}
```
## License
MIT LicenseExcerpt of 1,164 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2f427ba7407897da, desc:interpretability