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# Citing | |
To cite PySR or SymbolicRegression.jl, please use the following BibTeX entry: | |
```bibtex | |
@misc{cranmerInterpretableMachineLearning2023, | |
title = {Interpretable {Machine} {Learning} for {Science} with {PySR} and {SymbolicRegression}.jl}, | |
url = {http://arxiv.org/abs/2305.01582}, | |
doi = {10.48550/arXiv.2305.01582}, | |
urldate = {2023-07-17}, | |
publisher = {arXiv}, | |
author = {Cranmer, Miles}, | |
month = may, | |
year = {2023}, | |
note = {arXiv:2305.01582 [astro-ph, physics:physics]}, | |
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Symbolic Computation, Physics - Data Analysis, Statistics and Probability}, | |
} | |
``` | |
To cite symbolic distillation of neural networks, the following BibTeX entry can be used: | |
```bibtex | |
@article{cranmerDiscovering2020, | |
title={Discovering Symbolic Models from Deep Learning with Inductive Biases}, | |
author={Miles Cranmer and Alvaro Sanchez-Gonzalez and Peter Battaglia and Rui Xu and Kyle Cranmer and David Spergel and Shirley Ho}, | |
journal={NeurIPS 2020}, | |
year={2020}, | |
eprint={2006.11287}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.LG} | |
} | |
``` | |