🔗 Who cites «Contemporary Symbolic Regression Methods and their Relative Performance»

19 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Contemporary Symbolic Regression Methods and their Relative Performance»

1. Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlarXiv:2305.01582Miles Cranmer (Princeton University, Flatiron Institute)2023 · 216 citations
2. LLM-SR: Scientific Equation Discovery via Programming with Large Language ModelsarXiv:2404.18400Parshin Shojaee, Kazem Meidani, Shashank Gupta, et al.2024 · 47 citations
3. Symbolic Regression is NP-hardarXiv:2207.01018Marco Virgolin, Solon P. Pissis2022 · 35 citations
4. LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific DiscoveryarXiv:2405.09783Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, et al.2024 · 33 citations
5. End-to-end symbolic regression with transformersarXiv:2204.10532Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, et al.2022 · 27 citations
6. Symbolic Regression with a Learned Concept LibraryConference on Neural Information Processing Systems (NeurIPS)Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, et al.2024 · 23 citations
7. SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingarXiv:2310.02227Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, et al.2023 · 8 citations

12 more papers of our library cite it; they have no page here yet.

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