🧑‍🔬 Who cites Stéphane d'Ascoli

52 citations of their works from 48 papers of our library (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · ← Stéphane d'Ascoli

1. Interpretable Machine Learning for Science with PySR and SymbolicRegression.jlarXiv:2305.01582Miles Cranmer (Princeton University, Flatiron Institute)2023 · 216 citations
2. HyperTree Proof Search for Neural Theorem ProvingConference on Neural Information Processing Systems (NeurIPS)Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, et al.2022 · 151 citations
3. What Algorithms can Transformers Learn? A Study in Length GeneralizationarXiv:2310.16028Hattie Zhou, Arwen Bradley, Etai Littwin, et al.2023 · 67 citations
4. Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning ProcessarXiv:2407.20311Tian Ye, Zicheng Xu, Yuanzhi Li, et al.2024 · 58 citations
5. Repeat After Me: Transformers are Better than State Space Models at CopyingarXiv:2402.01032Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.2024 · 57 citations
6. LLM-SR: Scientific Equation Discovery via Programming with Large Language ModelsarXiv:2404.18400Parshin Shojaee, Kazem Meidani, Shashank Gupta, et al.2024 · 47 citations
7. GPT Can Solve Mathematical Problems Without a CalculatorarXiv:2309.03241Zhen Yang, Ming Ding, Qingsong Lv, et al.2023 · 41 citations
8. From Automation to Autonomy: A Survey on Large Language Models in Scientific DiscoveryConference on Empirical Methods in Natural Language Processing (EMNLP)Tianshi Zheng, Zheye Deng, Hong Ting Tsang, et al.2025 · 39 citations
9. Transformers Can Do Arithmetic with the Right EmbeddingsConference on Neural Information Processing Systems (NeurIPS)Sean McLeish, Arpit Bansal, Alex Stein, et al.2024 · 36 citations
10. Symbolic Regression is NP-hardarXiv:2207.01018Marco Virgolin, Solon P. Pissis2022 · 35 citations
11. Linear algebra with transformersarXiv:2112.01898François Charton2021 · 31 citations
12. Positional Description Matters for Transformers ArithmeticarXiv:2311.14737Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, et al.2023 · 30 citations
13. Transformers Can Achieve Length Generalization But Not RobustlyarXiv:2402.09371Yongchao Zhou, Uri Alon, Xinyun Chen, et al.2024 · 29 citations
14. Deep Symbolic Regression for Recurrent SequencesarXiv:2201.04600Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, et al.2022 · 27 citations
15. Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformersConference on Neural Information Processing Systems (NeurIPS)Alberto Alfarano, François Charton, Amaury Hayat2024 · 19 citations
16. xVal: A Continuous Numerical Tokenization for Scientific Language ModelsarXiv:2310.02989Siavash Golkar, Mariel Pettee, Michael Eickenberg, et al.2023 · 19 citations
17. Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery SystemsMachine LearningStefan Kramer, Mattia Cerrato, Jannis Brugger, et al.2023 · 13 citations
18. Chain-of-Thought Reasoning is a Policy Improvement OperatorarXiv:2309.08589Hugh Zhang, David C. Parkes2023 · 11 citations
19. DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and ExperiencearXiv:2506.04282Runxiang Wang, Boxiao Wang, Kai Li, et al.2025 · 9 citations
20. SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingarXiv:2310.02227Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, et al.2023 · 8 citations
21. Improving Length-Generalization in Transformers via Task HintingarXiv:2310.00726Pranjal Awasthi, Anupam Gupta2023 · 7 citations
22. Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic TasksarXiv:2406.01895Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.2024 · 5 citations

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