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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} | |
| } | |
| ``` | |