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# Citing | ||
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To cite PySR or SymbolicRegression.jl, please use the following BibTeX entry: | ||
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```bibtex | ||
@misc{cranmerInterpretableMachineLearning2023a, | ||
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}, | ||
} | ||
``` | ||
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To cite symbolic distillation of neural networks, the following BibTeX entry can be used: | ||
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```bibtex | ||
@article{cranmer2020discovering, | ||
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} | ||
} | ||
``` |