--- title: "Who cites Stéphane d'Ascoli — 48 papers of our library" description: "48 papers of the Elamur Research library cite the works of Stéphane d'Ascoli, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/stephane-d-ascoli/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/stephane-d-ascoli/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Stéphane d'Ascoli](https://md.research.elamur.ai/free/en/authors/stephane-d-ascoli/) / cited by # 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](https://md.research.elamur.ai/free/en/authors/stephane-d-ascoli/) 1. [**Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl arXiv:2305.01582**Miles Cranmer (Princeton University, Flatiron Institute)*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/university-2023-interpretable-machine-learning-science/) 2. **[HyperTree Proof Search for Neural Theorem Proving](https://md.research.elamur.ai/free/en/papers/lample-2022-hypertree-proof-search-neural-theorem/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, et al.*2022 · 151 citations* 3. [**What Algorithms can Transformers Learn? A Study in Length Generalization arXiv:2310.16028**Hattie Zhou, Arwen Bradley, Etai Littwin, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/) 4. [**Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process arXiv:2407.20311**Tian Ye, Zicheng Xu, Yuanzhi Li, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-physics-language-models-part-2/) 5. [**Repeat After Me: Transformers are Better than State Space Models at Copying arXiv:2402.01032**Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/jelassi-2024-repeat-after-me-transformers-better/) 6. [**LLM-SR: Scientific Equation Discovery via Programming with Large Language Models arXiv:2404.18400**Parshin Shojaee, Kazem Meidani, Shashank Gupta, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/shojaee-2024-llm-sr-scientific-equation/) 7. [**GPT Can Solve Mathematical Problems Without a Calculator arXiv:2309.03241**Zhen Yang, Ming Ding, Qingsong Lv, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-gpt-solve-mathematical-problems-calculator/) 8. **[From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://md.research.elamur.ai/free/en/papers/zheng-2025-automation-autonomy-survey-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianshi Zheng, Zheye Deng, Hong Ting Tsang, et al.*2025 · 39 citations* 9. **[Transformers Can Do Arithmetic with the Right Embeddings](https://md.research.elamur.ai/free/en/papers/mcleish-2024-transformers-arithmetic-right-embeddings/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sean McLeish, Arpit Bansal, Alex Stein, et al.*2024 · 36 citations* 10. [**Symbolic Regression is NP-hard arXiv:2207.01018**Marco Virgolin, Solon P. Pissis*2022 · 35 citations*](https://md.research.elamur.ai/free/en/papers/virgolin-2022-symbolic-regression-np-hard/) 11. [**Linear algebra with transformers arXiv:2112.01898**François Charton*2021 · 31 citations*](https://md.research.elamur.ai/free/en/papers/charton-2021-linear-algebra-transformers/) 12. [**Positional Description Matters for Transformers Arithmetic arXiv:2311.14737**Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-positional-description-matters-transformers-arithmetic/) 13. [**Transformers Can Achieve Length Generalization But Not Robustly arXiv:2402.09371**Yongchao Zhou, Uri Alon, Xinyun Chen, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-transformers-achieve-length-generalization-but/) 14. [**Deep Symbolic Regression for Recurrent Sequences arXiv:2201.04600**Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, et al.*2022 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ascoli-2022-deep-symbolic-regression-recurrent-sequences/) 15. **[Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers](https://md.research.elamur.ai/free/en/papers/alfarano-2024-global-lyapunov-functions-long-standing/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alberto Alfarano, François Charton, Amaury Hayat*2024 · 19 citations* 16. [**xVal: A Continuous Numerical Tokenization for Scientific Language Models arXiv:2310.02989**Siavash Golkar, Mariel Pettee, Michael Eickenberg, et al.*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/golkar-2023-xval-continuous-numerical-tokenization-scientific/) 17. **[Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems](https://md.research.elamur.ai/free/en/papers/kramer-2023-automated-scientific-discovery-equation/) [Machine Learning](https://md.research.elamur.ai/free/en/venues/machine-learning/)**Stefan Kramer, Mattia Cerrato, Jannis Brugger, et al.*2023 · 13 citations* 18. [**Chain-of-Thought Reasoning is a Policy Improvement Operator arXiv:2309.08589**Hugh Zhang, David C. Parkes*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-chain-thought-reasoning-policy-improvement/) 19. [**DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience arXiv:2506.04282**Runxiang Wang, Boxiao Wang, Kai Li, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-drsr-llm-based-scientific-equation/) 20. [**SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training arXiv:2310.02227**Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, et al.*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/meidani-2023-snip-bridging-mathematical-symbolic-numeric/) 21. [**Improving Length-Generalization in Transformers via Task Hinting arXiv:2310.00726**Pranjal Awasthi, Anupam Gupta*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/awasthi-2023-improving-length-generalization-transformers-task/) 22. [**Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks arXiv:2406.01895**Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/sabbaghi-2024-explicitly-encoding-structural-symmetry-key/) 26 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/stephane-d-ascoli/) — Stéphane d'Ascoli