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Pierre-Alexandre Kamienny

Pierre-Alexandre Kamienny has 2 papers in our library, in 2022, most of them on Reasoning; often with François Charton, Guillaume Lample, Stéphane d'Ascoli.

2 papers in our library · 2022

OpenAlex · h-index 8 · 20 works · 269 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 28 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #9596

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Sorbonne UniversitéParis · France2021–2023 · 4 papers🔬Institut Systèmes Intelligents et de RobotiqueParis · France2021–2022 · 2 papers🔬LIP6Paris · France2021–2022 · 2 papers🏢MetaLondon · United Kingdom2021 · 2 papers🏢MetaMenlo Park · United States2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksPhysics and Astronomy · 2 worksStatistical and Nonlinear Physics · 2 worksMathematics · 1 workModeling and Simulation · 1 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20222 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Deep Symbolic Regression for Recurrent Sequences (2022 · 27 citations)
    The paper tackles symbolic regression for sequences. Given the first terms of an integer or float sequence, the goal is to recover a recurrence relation u_n = f(n, earlier terms) that generates it. This is the familiar "guess the next number" puzzle from IQ tests, and earlier symbolic regression work, which targeted…
  2. End-to-end symbolic regression with transformers (2022 · 27 citations)
    Symbolic regression (SR) looks for a readable mathematical formula that reproduces observed data. Usual pipelines first guess an expression "skeleton" and then fit its constants with BFGS. Genetic programming (GP) repeats this loop many times, so it is accurate but slow. Earlier Transformer methods predicted only…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Régression symbolique de bout en bout avec des transformers. ↗HAL (Le Centre pour la Communication Scientifique Directe) · no summary yetPierre-Alexandre Kamienny et al.2022 · 37 citations
  2. Deep Symbolic Regression for Recurrent SequencesarXiv:2201.04600Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, et al.2022 · 27 citations
  3. End-to-end symbolic regression with transformersarXiv:2204.10532Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, et al.2022 · 27 citations
  4. Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search ↗arXiv (Cornell University) · no summary yetPierre-Alexandre Kamienny et al.2023 · 7 citations

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 28 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/pierre-alexandre-kamienny/